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B.; Askamentov, I.; Druelle, V.; Badenhorst, A.; Jefferies, G.; Albert, J.; Neher, R. title: COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2 date: 2020-05-07 journal: nan DOI: 10.1101/2020.05.05.20091363 sha: doc_id: 340354 cord_uid: j3xsp2po file: cache/cord-339649-ppgmmeuz.json key: cord-339649-ppgmmeuz authors: Klein, Michael G.; Cheng, Carolynn J.; Lii, Evonne; Mao, Keying; Mesbahi, Hamza; Zhu, Tianjie; Muckstadt, John A.; Hupert, Nathaniel title: COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review date: 2020-09-10 journal: Disaster medicine and public health preparedness DOI: 10.1017/dmp.2020.332 sha: doc_id: 339649 cord_uid: ppgmmeuz file: cache/cord-335689-8a704p38.json key: cord-335689-8a704p38 authors: Martin, Gerardo; Yanez-Arenas, Carlos; Chen, Carla; Plowright, Raina K.; Webb, Rebecca J.; Skerratt, Lee F. title: Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk date: 2018-03-19 journal: Ecohealth DOI: 10.1007/s10393-018-1322-9 sha: doc_id: 335689 cord_uid: 8a704p38 file: cache/cord-340805-qbvgnr4r.json key: cord-340805-qbvgnr4r authors: Ioannidis, John P.A.; Cripps, Sally; Tanner, Martin A. title: Forecasting for COVID-19 has failed date: 2020-08-25 journal: Int J Forecast DOI: 10.1016/j.ijforecast.2020.08.004 sha: doc_id: 340805 cord_uid: qbvgnr4r file: cache/cord-340564-3fu914lk.json key: cord-340564-3fu914lk authors: Cohen, Joseph Paul; Dao, Lan; Roth, Karsten; Morrison, Paul; Bengio, Yoshua; Abbasi, Almas F; Shen, Beiyi; Mahsa, Hoshmand Kochi; Ghassemi, Marzyeh; Li, Haifang; Duong, Tim title: Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning date: 2020-07-28 journal: Cureus DOI: 10.7759/cureus.9448 sha: doc_id: 340564 cord_uid: 3fu914lk file: cache/cord-340375-lhv83zac.json key: cord-340375-lhv83zac authors: Bliznashki, Svetoslav title: A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York date: 2020-04-07 journal: nan DOI: 10.1101/2020.04.05.20054577 sha: doc_id: 340375 cord_uid: lhv83zac file: cache/cord-333919-nrd9ajj2.json key: cord-333919-nrd9ajj2 authors: Albi, G.; Pareschi, L.; Zanella, M. title: Relaxing lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty date: 2020-05-16 journal: nan DOI: 10.1101/2020.05.12.20099721 sha: doc_id: 333919 cord_uid: nrd9ajj2 file: cache/cord-342591-6joc2ld1.json key: cord-342591-6joc2ld1 authors: Higazy, M. title: Novel Fractional Order SIDARTHE Mathematical Model of The COVID-19 Pandemic date: 2020-06-13 journal: Chaos Solitons Fractals DOI: 10.1016/j.chaos.2020.110007 sha: doc_id: 342591 cord_uid: 6joc2ld1 file: cache/cord-337915-usi3crfl.json key: cord-337915-usi3crfl authors: Vo, Khuong; Le, Tai; Rahmani, Amir M.; Dutt, Nikil; Cao, Hung title: An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram date: 2020-07-04 journal: Sensors (Basel) DOI: 10.3390/s20133757 sha: doc_id: 337915 cord_uid: usi3crfl file: cache/cord-340713-v5sdowb7.json key: cord-340713-v5sdowb7 authors: Bird, Jordan J.; Barnes, Chloe M.; Premebida, Cristiano; Ekárt, Anikó; Faria, Diego R. title: Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach date: 2020-10-28 journal: PLoS One DOI: 10.1371/journal.pone.0241332 sha: doc_id: 340713 cord_uid: v5sdowb7 file: cache/cord-337897-hkvll3xh.json key: cord-337897-hkvll3xh authors: Yang, Zheng Rong title: Peptide Bioinformatics- Peptide Classification Using Peptide Machines date: 2009 journal: Artificial Neural Networks DOI: 10.1007/978-1-60327-101-1_9 sha: doc_id: 337897 cord_uid: hkvll3xh file: cache/cord-336747-8m7n5r85.json key: cord-336747-8m7n5r85 authors: Grossmann, G.; Backenkoehler, M.; Wolf, V. title: Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study date: 2020-05-08 journal: nan DOI: 10.1101/2020.05.05.20091736 sha: doc_id: 336747 cord_uid: 8m7n5r85 file: cache/cord-336687-iw3bzy0m.json key: cord-336687-iw3bzy0m authors: Kraemer, M. 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A. title: A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies date: 2020-05-06 journal: nan DOI: 10.1101/2020.05.02.20089136 sha: doc_id: 352543 cord_uid: 8il0dh58 file: cache/cord-354254-89vjfkfd.json key: cord-354254-89vjfkfd authors: Peng, Shanbi; Chen, Qikun; Liu, Enbin title: The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control date: 2020-08-31 journal: Sci Total Environ DOI: 10.1016/j.scitotenv.2020.142090 sha: doc_id: 354254 cord_uid: 89vjfkfd file: cache/cord-353200-5csewb1k.json key: cord-353200-5csewb1k authors: Jehi, Lara; Ji, Xinge; Milinovich, Alex; Erzurum, Serpil; Merlino, Amy; Gordon, Steve; Young, James B.; Kattan, Michael W. title: Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19 date: 2020-08-11 journal: PLoS One DOI: 10.1371/journal.pone.0237419 sha: doc_id: 353200 cord_uid: 5csewb1k file: cache/cord-355102-jcyq8qve.json key: cord-355102-jcyq8qve authors: Avila, Eduardo; Kahmann, Alessandro; Alho, Clarice; Dorn, Marcio title: Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios date: 2020-06-29 journal: PeerJ DOI: 10.7717/peerj.9482 sha: doc_id: 355102 cord_uid: jcyq8qve Reading metadata file and updating bibliogrpahics === updating bibliographic database Building study carrel named keyword-model-cord === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 48305 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 95. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 45898 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 48045 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 95. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 95. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 94. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 95. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 95. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable parallel: Warning: No more processes: Decreasing number of running jobs to 94. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 49443 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 44956 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 45880 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 48463 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 48977 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 94. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 48797 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 50055 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 50358 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 50427 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 50728 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-018976-0ndb7rm2 author: Iwasa, Yoh title: Mathematical Studies of Dynamics and Evolution of Infectious Diseases date: 2007 pages: extension: .txt txt: ./txt/cord-018976-0ndb7rm2.txt cache: ./cache/cord-018976-0ndb7rm2.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-018976-0ndb7rm2.txt' /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-007129-qjdg46o9 author: Simoes, Joana Margarida title: Spatial Epidemic Modelling in Social Networks date: 2005-06-21 pages: extension: .txt txt: ./txt/cord-007129-qjdg46o9.txt cache: ./cache/cord-007129-qjdg46o9.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-007129-qjdg46o9.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 50708 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 51392 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53495 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 52883 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53470 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 52121 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53249 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53341 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-011400-zyjd9rmp author: Peixoto, Tiago P. title: Network Reconstruction and Community Detection from Dynamics date: 2019-09-18 pages: extension: .txt txt: ./txt/cord-011400-zyjd9rmp.txt cache: ./cache/cord-011400-zyjd9rmp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-011400-zyjd9rmp.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 54512 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 52993 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 94. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53353 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 53331 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 54577 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 54576 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 54774 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 51206 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-027228-s32v6bmd author: Subramanian, Vigneshwar title: Editorial: Why is modeling COVID-19 so difficult? date: 2020-06-19 pages: extension: .txt txt: ./txt/cord-027228-s32v6bmd.txt cache: ./cache/cord-027228-s32v6bmd.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-027228-s32v6bmd.txt' === file2bib.sh === id: cord-010903-kuwy7pbo author: Liu, Jiajun title: Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime date: 2020-03-05 pages: extension: .txt txt: ./txt/cord-010903-kuwy7pbo.txt cache: ./cache/cord-010903-kuwy7pbo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-010903-kuwy7pbo.txt' === file2bib.sh === id: cord-025517-rb4sr8r4 author: Koutsomitropoulos, Dimitrios A. title: Automated MeSH Indexing of Biomedical Literature Using Contextualized Word Representations date: 2020-05-06 pages: extension: .txt txt: ./txt/cord-025517-rb4sr8r4.txt cache: ./cache/cord-025517-rb4sr8r4.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-025517-rb4sr8r4.txt' === file2bib.sh === id: cord-027201-owzhv0xy author: Tkacz, Magdalena A. title: Advantage of Using Spherical over Cartesian Coordinates in the Chromosome Territories 3D Modeling date: 2020-06-15 pages: extension: .txt txt: ./txt/cord-027201-owzhv0xy.txt cache: ./cache/cord-027201-owzhv0xy.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-027201-owzhv0xy.txt' === file2bib.sh === id: cord-012866-p3mb7r0v author: Luo, Yan title: Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis date: 2020-06-12 pages: extension: .txt txt: ./txt/cord-012866-p3mb7r0v.txt cache: ./cache/cord-012866-p3mb7r0v.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-012866-p3mb7r0v.txt' === file2bib.sh === id: cord-020193-3oqkdbq0 author: Bley, Katja title: Overcoming the Ivory Tower: A Meta Model for Staged Maturity Models date: 2020-03-06 pages: extension: .txt txt: ./txt/cord-020193-3oqkdbq0.txt cache: ./cache/cord-020193-3oqkdbq0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-020193-3oqkdbq0.txt' === file2bib.sh === id: cord-024501-nl0gsr0c author: Tan, Chunyang title: MSGE: A Multi-step Gated Model for Knowledge Graph Completion date: 2020-04-17 pages: extension: .txt txt: ./txt/cord-024501-nl0gsr0c.txt cache: ./cache/cord-024501-nl0gsr0c.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-024501-nl0gsr0c.txt' /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-016045-od0fr8l0 author: Liu, Ming title: Epidemic-Logistics Network Considering Time Windows and Service Level date: 2019-10-04 pages: extension: .txt txt: ./txt/cord-016045-od0fr8l0.txt cache: ./cache/cord-016045-od0fr8l0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-016045-od0fr8l0.txt' === file2bib.sh === id: cord-004332-99lxmq4u author: Zhao, Shi title: Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall date: 2020-01-10 pages: extension: .txt txt: ./txt/cord-004332-99lxmq4u.txt cache: ./cache/cord-004332-99lxmq4u.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-004332-99lxmq4u.txt' === file2bib.sh === id: cord-017934-3wyebaxb author: Kurahashi, Setsuya title: An Agent-Based Infectious Disease Model of Rubella Outbreaks date: 2019-05-07 pages: extension: .txt txt: ./txt/cord-017934-3wyebaxb.txt cache: ./cache/cord-017934-3wyebaxb.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-017934-3wyebaxb.txt' === file2bib.sh === id: cord-020683-5s3lghj6 author: Buonomo, Bruno title: Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model date: 2020-04-09 pages: extension: .txt txt: ./txt/cord-020683-5s3lghj6.txt cache: ./cache/cord-020683-5s3lghj6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-020683-5s3lghj6.txt' === file2bib.sh === id: cord-020871-1v6dcmt3 author: Papariello, Luca title: On the Replicability of Combining Word Embeddings and Retrieval Models date: 2020-03-24 pages: extension: .txt txt: ./txt/cord-020871-1v6dcmt3.txt cache: ./cache/cord-020871-1v6dcmt3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-020871-1v6dcmt3.txt' === file2bib.sh === id: cord-004157-osol7wdp author: Ma, Junling title: Estimating epidemic exponential growth rate and basic reproduction number date: 2020-01-08 pages: extension: .txt txt: ./txt/cord-004157-osol7wdp.txt cache: ./cache/cord-004157-osol7wdp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-004157-osol7wdp.txt' === file2bib.sh === id: cord-016261-jms7hrmp author: Liu, Chunmei title: Profiling and Searching for RNA Pseudoknot Structures in Genomes date: 2005 pages: extension: .txt txt: ./txt/cord-016261-jms7hrmp.txt cache: ./cache/cord-016261-jms7hrmp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-016261-jms7hrmp.txt' === file2bib.sh === id: cord-007255-jmjolo9p author: Pulliam, Juliet R. C. title: Ability to replicate in the cytoplasm predicts zoonotic transmission of livestock viruses date: 2009-02-15 pages: extension: .txt txt: ./txt/cord-007255-jmjolo9p.txt cache: ./cache/cord-007255-jmjolo9p.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-007255-jmjolo9p.txt' === file2bib.sh === id: cord-017423-cxua1o5t author: Wang, Rui title: A Review of Microblogging Marketing Based on the Complex Network Theory date: 2011-11-12 pages: extension: .txt txt: ./txt/cord-017423-cxua1o5t.txt cache: ./cache/cord-017423-cxua1o5t.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-017423-cxua1o5t.txt' === file2bib.sh === id: cord-000282-phepjf55 author: Hsieh, Ying-Hen title: On epidemic modeling in real time: An application to the 2009 Novel A (H1N1) influenza outbreak in Canada date: 2010-11-05 pages: extension: .txt txt: ./txt/cord-000282-phepjf55.txt cache: ./cache/cord-000282-phepjf55.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-000282-phepjf55.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 54515 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-002474-2l31d7ew author: Lv, Yang title: Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China date: 2017-04-03 pages: extension: .txt txt: ./txt/cord-002474-2l31d7ew.txt cache: ./cache/cord-002474-2l31d7ew.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-002474-2l31d7ew.txt' === file2bib.sh === id: cord-003377-9vkhptas author: Wu, Tong title: The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia date: 2018-12-19 pages: extension: .txt txt: ./txt/cord-003377-9vkhptas.txt cache: ./cache/cord-003377-9vkhptas.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-003377-9vkhptas.txt' === file2bib.sh === id: cord-009481-6pm3rpzj author: Parnell, Gregory S. title: Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model date: 2009-12-11 pages: extension: .txt txt: ./txt/cord-009481-6pm3rpzj.txt cache: ./cache/cord-009481-6pm3rpzj.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-009481-6pm3rpzj.txt' === file2bib.sh === id: cord-000332-u3f89kvg author: Broeck, Wouter Van den title: The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale date: 2011-02-02 pages: extension: .txt txt: ./txt/cord-000332-u3f89kvg.txt cache: ./cache/cord-000332-u3f89kvg.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-000332-u3f89kvg.txt' === file2bib.sh === id: cord-026384-ejk9wjr1 author: Crilly, Colin J. title: Predicting the outcomes of preterm neonates beyond the neonatal intensive care unit: What are we missing? date: 2020-05-19 pages: extension: .txt txt: ./txt/cord-026384-ejk9wjr1.txt cache: ./cache/cord-026384-ejk9wjr1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-026384-ejk9wjr1.txt' === file2bib.sh === id: cord-001603-vlv8x8l8 author: Ul-Haq, Zaheer title: 3D Structure Prediction of Human β1-Adrenergic Receptor via Threading-Based Homology Modeling for Implications in Structure-Based Drug Designing date: 2015-04-10 pages: extension: .txt txt: ./txt/cord-001603-vlv8x8l8.txt cache: ./cache/cord-001603-vlv8x8l8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-001603-vlv8x8l8.txt' === file2bib.sh === id: cord-005321-b3pyg5b3 author: Cai, Li-Ming title: Global analysis of an epidemic model with vaccination date: 2017-07-21 pages: extension: .txt txt: ./txt/cord-005321-b3pyg5b3.txt cache: ./cache/cord-005321-b3pyg5b3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-005321-b3pyg5b3.txt' === file2bib.sh === id: cord-027119-zazr8uj5 author: Taif, Khasrouf title: Cast Shadow Generation Using Generative Adversarial Networks date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-027119-zazr8uj5.txt cache: ./cache/cord-027119-zazr8uj5.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-027119-zazr8uj5.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61353 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-000759-36dhfptw author: Uribe-Sánchez, Andrés title: Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks date: 2011-06-05 pages: extension: .txt txt: ./txt/cord-000759-36dhfptw.txt cache: ./cache/cord-000759-36dhfptw.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-000759-36dhfptw.txt' === file2bib.sh === id: cord-026827-6vjg386e author: Awan, Ammar Ahmad title: HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow date: 2020-05-22 pages: extension: .txt txt: ./txt/cord-026827-6vjg386e.txt cache: ./cache/cord-026827-6vjg386e.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-026827-6vjg386e.txt' === file2bib.sh === id: cord-016965-z7a6eoyo author: Brockmann, Dirk title: Human Mobility, Networks and Disease Dynamics on a Global Scale date: 2017-10-23 pages: extension: .txt txt: ./txt/cord-016965-z7a6eoyo.txt cache: ./cache/cord-016965-z7a6eoyo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-016965-z7a6eoyo.txt' === file2bib.sh === id: cord-024341-sw2pdnh6 author: Aksyonov, Konstantin title: Development of Cloud-Based Microservices to Decision Support System date: 2020-05-05 pages: extension: .txt txt: ./txt/cord-024341-sw2pdnh6.txt cache: ./cache/cord-024341-sw2pdnh6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-024341-sw2pdnh6.txt' === file2bib.sh === id: cord-005033-voi9gu0l author: Xuan, Huiyu title: A CA-based epidemic model for HIV/AIDS transmission with heterogeneity date: 2008-06-07 pages: extension: .txt txt: ./txt/cord-005033-voi9gu0l.txt cache: ./cache/cord-005033-voi9gu0l.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-005033-voi9gu0l.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 60733 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 60964 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 93. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 94. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61703 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 60395 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61530 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61544 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61968 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-001921-73esrper author: Lin, Cheng-Yung title: Zebrafish and Medaka: new model organisms for modern biomedical research date: 2016-01-28 pages: extension: .txt txt: ./txt/cord-001921-73esrper.txt cache: ./cache/cord-001921-73esrper.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-001921-73esrper.txt' === file2bib.sh === id: cord-004416-qw6tusd2 author: Krishna, Smriti M. title: Development of a two-stage limb ischemia model to better simulate human peripheral artery disease date: 2020-02-26 pages: extension: .txt txt: ./txt/cord-004416-qw6tusd2.txt cache: ./cache/cord-004416-qw6tusd2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-004416-qw6tusd2.txt' === file2bib.sh === id: cord-028789-dqa74cus author: Ouhami, Maryam title: Deep Transfer Learning Models for Tomato Disease Detection date: 2020-06-05 pages: extension: .txt txt: ./txt/cord-028789-dqa74cus.txt cache: ./cache/cord-028789-dqa74cus.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-028789-dqa74cus.txt' /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-003243-u744apzw author: Michael, Edwin title: Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination date: 2018-10-08 pages: extension: .txt txt: ./txt/cord-003243-u744apzw.txt cache: ./cache/cord-003243-u744apzw.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-003243-u744apzw.txt' === file2bib.sh === id: cord-024515-iioqkydg author: Zhong, Qi title: Protecting IP of Deep Neural Networks with Watermarking: A New Label Helps date: 2020-04-17 pages: extension: .txt txt: ./txt/cord-024515-iioqkydg.txt cache: ./cache/cord-024515-iioqkydg.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-024515-iioqkydg.txt' /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes === file2bib.sh === id: cord-025843-5gpasqtr author: Wild, Karoline title: Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models date: 2020-05-09 pages: extension: .txt txt: ./txt/cord-025843-5gpasqtr.txt cache: ./cache/cord-025843-5gpasqtr.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-025843-5gpasqtr.txt' /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 63225 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61961 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 61654 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-015255-1qhgeirb author: Busby, J S title: Managing the social amplification of risk: a simulation of interacting actors date: 2012-07-11 pages: extension: .txt txt: ./txt/cord-015255-1qhgeirb.txt cache: ./cache/cord-015255-1qhgeirb.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-015255-1qhgeirb.txt' === file2bib.sh === id: cord-016954-l3b6n7ej author: Young, Colin R. title: Animal Models of Multiple Sclerosis date: 2008 pages: extension: .txt txt: ./txt/cord-016954-l3b6n7ej.txt cache: ./cache/cord-016954-l3b6n7ej.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-016954-l3b6n7ej.txt' === file2bib.sh === id: cord-007147-0v8ltunv author: Dungan, R. S. title: BOARD-INVITED REVIEW: Fate and transport of bioaerosols associated with livestock operations and manures date: 2010-11-17 pages: extension: .txt txt: ./txt/cord-007147-0v8ltunv.txt cache: ./cache/cord-007147-0v8ltunv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-007147-0v8ltunv.txt' === file2bib.sh === id: cord-002169-7kwlteyr author: Wu, Nicholas C title: Adaptation in protein fitness landscapes is facilitated by indirect paths date: 2016-07-08 pages: extension: .txt txt: ./txt/cord-002169-7kwlteyr.txt cache: ./cache/cord-002169-7kwlteyr.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-002169-7kwlteyr.txt' === file2bib.sh === id: cord-027337-eorjnma3 author: Fratrič, Peter title: Integrating Agent-Based Modelling with Copula Theory: Preliminary Insights and Open Problems date: 2020-05-22 pages: extension: .txt txt: ./txt/cord-027337-eorjnma3.txt cache: ./cache/cord-027337-eorjnma3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-027337-eorjnma3.txt' === file2bib.sh === id: cord-017003-3farxcc3 author: Koibuchi, Yukio title: Numerical Simulation of Urban Coastal Zones date: 2010 pages: extension: .txt txt: ./txt/cord-017003-3farxcc3.txt cache: ./cache/cord-017003-3farxcc3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-017003-3farxcc3.txt' === file2bib.sh === id: cord-031143-a1qyadm6 author: Pinto Neto, Osmar title: Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19 date: 2020-08-30 pages: extension: .txt txt: ./txt/cord-031143-a1qyadm6.txt cache: ./cache/cord-031143-a1qyadm6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-031143-a1qyadm6.txt' === file2bib.sh === id: cord-018791-h3bfdr14 author: Rasulev, Bakhtiyor title: Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures date: 2016-12-09 pages: extension: .txt txt: ./txt/cord-018791-h3bfdr14.txt cache: ./cache/cord-018791-h3bfdr14.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-018791-h3bfdr14.txt' === file2bib.sh === id: cord-022891-vgfv5pi4 author: Hall, Graeme M. J. title: SIMULATING NEW ZEALAND FOREST DYNAMICS WITH A GENERALIZED TEMPERATE FOREST GAP MODEL date: 2000-02-01 pages: extension: .txt txt: ./txt/cord-022891-vgfv5pi4.txt cache: ./cache/cord-022891-vgfv5pi4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-022891-vgfv5pi4.txt' /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes === file2bib.sh === id: cord-010977-fwz7chzf author: Myserlis, Pavlos title: Translational Genomics in Neurocritical Care: a Review date: 2020-02-20 pages: extension: .txt txt: ./txt/cord-010977-fwz7chzf.txt cache: ./cache/cord-010977-fwz7chzf.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-010977-fwz7chzf.txt' === file2bib.sh === id: cord-129272-p1jeiljo author: Broniec, William title: Using VERA to explain the impact of social distancing on the spread of COVID-19 date: 2020-03-30 pages: extension: .txt txt: ./txt/cord-129272-p1jeiljo.txt cache: ./cache/cord-129272-p1jeiljo.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-129272-p1jeiljo.txt' === file2bib.sh === Traceback (most recent call last): File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2646, in get_loc return self._engine.get_loc(key) File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'cord-118731-h5au2h09' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/data-disk/reader-compute/reader-cord/bin/file2bib.py", line 64, in if ( bibliographics.loc[ escape ,'author'] ) : author = bibliographics.loc[ escape,'author'] File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexing.py", line 1762, in __getitem__ return self._getitem_tuple(key) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexing.py", line 1272, in _getitem_tuple return self._getitem_lowerdim(tup) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexing.py", line 1389, in _getitem_lowerdim section = self._getitem_axis(key, axis=i) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexing.py", line 1965, in _getitem_axis return self._get_label(key, axis=axis) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexing.py", line 625, in _get_label return self.obj._xs(label, axis=axis) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/generic.py", line 3537, in xs loc = self.index.get_loc(key) File "/data-disk/python/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2648, in get_loc return self._engine.get_loc(self._maybe_cast_indexer(key)) File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas/_libs/hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'cord-118731-h5au2h09' === file2bib.sh === id: cord-119104-9d421si9 author: Huynh, Tin Van title: BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models date: 2020-09-06 pages: extension: .txt txt: ./txt/cord-119104-9d421si9.txt cache: ./cache/cord-119104-9d421si9.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-119104-9d421si9.txt' === file2bib.sh === id: cord-017595-v3rllyyu author: Puzyn, Tomasz title: Nanomaterials – the Next Great Challenge for Qsar Modelers date: 2009-06-25 pages: extension: .txt txt: ./txt/cord-017595-v3rllyyu.txt cache: ./cache/cord-017595-v3rllyyu.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-017595-v3rllyyu.txt' parallel: Warning: No more processes: Decreasing number of running jobs to 93. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === id: cord-118553-ki6bbuod author: Piccolomini, Elena Loli title: Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model date: 2020-03-22 pages: extension: .txt txt: ./txt/cord-118553-ki6bbuod.txt cache: ./cache/cord-118553-ki6bbuod.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-118553-ki6bbuod.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 66369 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 92. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 93. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 93. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 93. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 65496 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-018947-d4im0p9e author: Helbing, Dirk title: Challenges in Economics date: 2012-02-10 pages: extension: .txt txt: ./txt/cord-018947-d4im0p9e.txt cache: ./cache/cord-018947-d4im0p9e.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-018947-d4im0p9e.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 64695 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-020764-5tq9cr7o author: Vertrees, Roger A. title: Tissue Culture Models date: 2010-05-21 pages: extension: .txt txt: ./txt/cord-020764-5tq9cr7o.txt cache: ./cache/cord-020764-5tq9cr7o.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-020764-5tq9cr7o.txt' === file2bib.sh === id: cord-030683-xe9bn1cc author: Wang, Wenxi title: A Study of Symmetry Breaking Predicates and Model Counting date: 2020-03-13 pages: extension: .txt txt: ./txt/cord-030683-xe9bn1cc.txt cache: ./cache/cord-030683-xe9bn1cc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-030683-xe9bn1cc.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 67258 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-103913-jgko7b0j author: Macedo, A. M. S. title: A comparative analysis between a SIRD compartmental model and the Richards growth model date: 2020-08-06 pages: extension: .txt txt: ./txt/cord-103913-jgko7b0j.txt cache: ./cache/cord-103913-jgko7b0j.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-103913-jgko7b0j.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 67538 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 91. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 92. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-018746-s9knxdne author: Perra, Nicola title: Modeling and Predicting Human Infectious Diseases date: 2015-04-23 pages: extension: .txt txt: ./txt/cord-018746-s9knxdne.txt cache: ./cache/cord-018746-s9knxdne.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-018746-s9knxdne.txt' /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 67384 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-048461-397hp1yt author: Coelho, Flávio C title: Epigrass: a tool to study disease spread in complex networks date: 2008-02-26 pages: extension: .txt txt: ./txt/cord-048461-397hp1yt.txt cache: ./cache/cord-048461-397hp1yt.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-048461-397hp1yt.txt' === file2bib.sh === id: cord-033882-uts6wfqw author: Khakharia, Aman title: Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning date: 2020-10-16 pages: extension: .txt txt: ./txt/cord-033882-uts6wfqw.txt cache: ./cache/cord-033882-uts6wfqw.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-033882-uts6wfqw.txt' === file2bib.sh === id: cord-025348-sh1kehrh author: Jurj, Sorin Liviu title: Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities date: 2020-05-02 pages: extension: .txt txt: ./txt/cord-025348-sh1kehrh.txt cache: ./cache/cord-025348-sh1kehrh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-025348-sh1kehrh.txt' === file2bib.sh === id: cord-029311-9769dgb6 author: Nemati, Hamed title: Validation of Abstract Side-Channel Models for Computer Architectures date: 2020-06-13 pages: extension: .txt txt: ./txt/cord-029311-9769dgb6.txt cache: ./cache/cord-029311-9769dgb6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-029311-9769dgb6.txt' === file2bib.sh === id: cord-132307-bkkzg6h1 author: Blanco, Natalia title: Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness date: 2020-07-06 pages: extension: .txt txt: ./txt/cord-132307-bkkzg6h1.txt cache: ./cache/cord-132307-bkkzg6h1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-132307-bkkzg6h1.txt' === file2bib.sh === id: cord-125330-jyppul4o author: Crokidakis, Nuno title: Modeling the evolution of drinking behavior: A Statistical Physics perspective date: 2020-08-24 pages: extension: .txt txt: ./txt/cord-125330-jyppul4o.txt cache: ./cache/cord-125330-jyppul4o.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-125330-jyppul4o.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68666 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 90. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68267 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68325 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 92. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === id: cord-104133-d01joq23 author: Arthur, Ronan F. title: Adaptive social contact rates induce complex dynamics during epidemics date: 2020-07-14 pages: extension: .txt txt: ./txt/cord-104133-d01joq23.txt cache: ./cache/cord-104133-d01joq23.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-104133-d01joq23.txt' parallel: Warning: No more processes: Decreasing number of running jobs to 89. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68572 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 91. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 66236 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68959 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-034181-ji4empe6 author: Saqib, Mohd title: Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model date: 2020-10-23 pages: extension: .txt txt: ./txt/cord-034181-ji4empe6.txt cache: ./cache/cord-034181-ji4empe6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-034181-ji4empe6.txt' /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes === file2bib.sh === id: cord-031232-6cv8n2bf author: de Weck, Olivier title: Handling the COVID‐19 crisis: Toward an agile model‐based systems approach date: 2020-08-27 pages: extension: .txt txt: ./txt/cord-031232-6cv8n2bf.txt cache: ./cache/cord-031232-6cv8n2bf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-031232-6cv8n2bf.txt' /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === id: cord-034846-05h2no14 author: Singer, Gonen title: Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic date: 2020-08-07 pages: extension: .txt txt: ./txt/cord-034846-05h2no14.txt cache: ./cache/cord-034846-05h2no14.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-034846-05h2no14.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 92. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 69162 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 68569 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-013784-zhgjmt2j author: Tang, Min title: Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions date: 2020-06-04 pages: extension: .txt txt: ./txt/cord-013784-zhgjmt2j.txt cache: ./cache/cord-013784-zhgjmt2j.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-013784-zhgjmt2j.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-140977-mg04drna author: Maltezos, S. title: Methodology for Modelling the new COVID-19 Pandemic Spread and Implementation to European Countries date: 2020-06-27 pages: extension: .txt txt: ./txt/cord-140977-mg04drna.txt cache: ./cache/cord-140977-mg04drna.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-140977-mg04drna.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70027 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 69883 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-127900-78x19fw4 author: Leung, Abby title: Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies date: 2020-10-06 pages: extension: .txt txt: ./txt/cord-127900-78x19fw4.txt cache: ./cache/cord-127900-78x19fw4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-127900-78x19fw4.txt' === file2bib.sh === id: cord-103435-yufvt44t author: van Aalst, Marvin title: Constructing and analysing dynamic models with modelbase v1.0 - a software update date: 2020-10-02 pages: extension: .txt txt: ./txt/cord-103435-yufvt44t.txt cache: ./cache/cord-103435-yufvt44t.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-103435-yufvt44t.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 69260 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-123800-pxhott2p author: Pandey, Gaurav title: SEIR and Regression Model based COVID-19 outbreak predictions in India date: 2020-04-01 pages: extension: .txt txt: ./txt/cord-123800-pxhott2p.txt cache: ./cache/cord-123800-pxhott2p.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-123800-pxhott2p.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 69620 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 88. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 91. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70647 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70403 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 92. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 91. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70804 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 87. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 90. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-103280-kf6mqv4e author: Bergs, Thomas title: Determination of Johnson-Cook material model parameters for AISI 1045 from orthogonal cutting tests using the Downhill-Simplex algorithm date: 2020-12-31 pages: extension: .txt txt: ./txt/cord-103280-kf6mqv4e.txt cache: ./cache/cord-103280-kf6mqv4e.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-103280-kf6mqv4e.txt' === file2bib.sh === id: cord-161039-qh9hz4wz author: Tripathy, Shrabani S. title: Flood Evacuation During Pandemic: A multi-objective Framework to Handle Compound Hazard date: 2020-10-03 pages: extension: .txt txt: ./txt/cord-161039-qh9hz4wz.txt cache: ./cache/cord-161039-qh9hz4wz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-161039-qh9hz4wz.txt' === file2bib.sh === id: cord-035388-n9hza6vm author: Xu, Jie title: Federated Learning for Healthcare Informatics date: 2020-11-12 pages: extension: .txt txt: ./txt/cord-035388-n9hza6vm.txt cache: ./cache/cord-035388-n9hza6vm.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-035388-n9hza6vm.txt' /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-026742-us7llnva author: Gonçalves, Judite title: Effects of self-employment on hospitalizations: instrumental variables analysis of social security data date: 2020-06-15 pages: extension: .txt txt: ./txt/cord-026742-us7llnva.txt cache: ./cache/cord-026742-us7llnva.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-026742-us7llnva.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === id: cord-126012-h7er0prc author: Diaz, Victor Hugo Grisales title: COVID-19: Forecasting mortality given mobility trend data and non-pharmaceutical interventions date: 2020-09-25 pages: extension: .txt txt: ./txt/cord-126012-h7er0prc.txt cache: ./cache/cord-126012-h7er0prc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-126012-h7er0prc.txt' /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes === file2bib.sh === /data-disk/reader-compute/reader-cord/bin/file2bib.sh: fork: retry: No child processes id: cord-034843-cirltmy4 author: Nabipour, M. title: Deep Learning for Stock Market Prediction date: 2020-07-30 pages: extension: .txt txt: ./txt/cord-034843-cirltmy4.txt cache: ./cache/cord-034843-cirltmy4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-034843-cirltmy4.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes parallel: Warning: No more processes: Decreasing number of running jobs to 89. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 90. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === id: cord-159103-dbgs2ado author: Rieke, Nicola title: The Future of Digital Health with Federated Learning date: 2020-03-18 pages: extension: .txt txt: ./txt/cord-159103-dbgs2ado.txt cache: ./cache/cord-159103-dbgs2ado.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-159103-dbgs2ado.txt' parallel: Warning: No more processes: Decreasing number of running jobs to 91. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 72969 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 72107 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 72809 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-102850-0kiypige author: Huang, C.-C. title: A Machine Learning Study to Improve Surgical Case Duration Prediction date: 2020-06-12 pages: extension: .txt txt: ./txt/cord-102850-0kiypige.txt cache: ./cache/cord-102850-0kiypige.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-102850-0kiypige.txt' === file2bib.sh === id: cord-102359-k1xxz4hc author: Klotsa, Daphne title: Electronic Transport in DNA date: 2005-04-04 pages: extension: .txt txt: ./txt/cord-102359-k1xxz4hc.txt cache: ./cache/cord-102359-k1xxz4hc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-102359-k1xxz4hc.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 62196 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-033010-o5kiadfm author: Durojaye, Olanrewaju Ayodeji title: Potential therapeutic target identification in the novel 2019 coronavirus: insight from homology modeling and blind docking study date: 2020-10-02 pages: extension: .txt txt: ./txt/cord-033010-o5kiadfm.txt cache: ./cache/cord-033010-o5kiadfm.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-033010-o5kiadfm.txt' parallel: Warning: No more processes: Decreasing number of running jobs to 88. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 90. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70727 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 71057 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 71726 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 71004 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 89. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === id: cord-162105-u0w56xrp author: Centeno, Raffy S. title: How much did the Tourism Industry Lost? Estimating Earning Loss of Tourism in the Philippines date: 2020-04-21 pages: extension: .txt txt: ./txt/cord-162105-u0w56xrp.txt cache: ./cache/cord-162105-u0w56xrp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-162105-u0w56xrp.txt' === file2bib.sh === id: cord-117688-20gfpbyf author: Cakmakli, Cem title: Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model date: 2020-07-03 pages: extension: .txt txt: ./txt/cord-117688-20gfpbyf.txt cache: ./cache/cord-117688-20gfpbyf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-117688-20gfpbyf.txt' === file2bib.sh === id: cord-103502-asphso2s author: Herrgårdh, Tilda title: An organ-based multi-level model for glucose homeostasis: organ distributions, timing, and impact of blood flow date: 2020-10-21 pages: extension: .txt txt: ./txt/cord-103502-asphso2s.txt cache: ./cache/cord-103502-asphso2s.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-103502-asphso2s.txt' /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === id: cord-143539-gvt25gac author: Marmarelis, Myrl G. title: Latent Embeddings of Point Process Excitations date: 2020-05-05 pages: extension: .txt txt: ./txt/cord-143539-gvt25gac.txt cache: ./cache/cord-143539-gvt25gac.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-143539-gvt25gac.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-154170-7pnz98o6 author: Ponciano, Jos'e Miguel title: Poverty levels, societal and individual heterogeneities explain the SARS-CoV-2 pandemic growth in Latin America date: 2020-05-22 pages: extension: .txt txt: ./txt/cord-154170-7pnz98o6.txt cache: ./cache/cord-154170-7pnz98o6.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-154170-7pnz98o6.txt' === file2bib.sh === id: cord-147202-clje3b2r author: Ghanam, Ryad title: SEIRD Model for Qatar Covid-19 Outbreak: A Case Study date: 2020-05-26 pages: extension: .txt txt: ./txt/cord-147202-clje3b2r.txt cache: ./cache/cord-147202-clje3b2r.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-147202-clje3b2r.txt' === file2bib.sh === id: cord-130967-cvbpgvso author: Dinamarca, Jos'e Luis title: Clinical concepts might be included in health-related mathematic models date: 2020-04-23 pages: extension: .txt txt: ./txt/cord-130967-cvbpgvso.txt cache: ./cache/cord-130967-cvbpgvso.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-130967-cvbpgvso.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-135004-68y19dpg author: Russo, Carlo title: Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction date: 2020-10-27 pages: extension: .txt txt: ./txt/cord-135004-68y19dpg.txt cache: ./cache/cord-135004-68y19dpg.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-135004-68y19dpg.txt' /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes === file2bib.sh === id: cord-122344-2lepkvby author: Hayashi, Hiroaki title: What's New? Summarizing Contributions in Scientific Literature date: 2020-11-06 pages: extension: .txt txt: ./txt/cord-122344-2lepkvby.txt cache: ./cache/cord-122344-2lepkvby.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-122344-2lepkvby.txt' /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes === file2bib.sh === id: cord-128991-mb91j2zs author: Agapiou, Sergios title: Modeling of Covid-19 Pandemic in Cyprus date: 2020-10-05 pages: extension: .txt txt: ./txt/cord-128991-mb91j2zs.txt cache: ./cache/cord-128991-mb91j2zs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-128991-mb91j2zs.txt' parallel: Warning: No more processes: Decreasing number of running jobs to 88. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 73575 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74085 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74490 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74592 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 87. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 90. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 89. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74223 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74510 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 87. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74233 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordpos2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === id: cord-185125-be11h9wn author: Baldea, Ioan title: What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia? date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-185125-be11h9wn.txt cache: ./cache/cord-185125-be11h9wn.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-185125-be11h9wn.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 70480 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74236 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 86. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === id: cord-171231-m54moffr author: Habli, Ibrahim title: Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models date: 2020-05-17 pages: extension: .txt txt: ./txt/cord-171231-m54moffr.txt cache: ./cache/cord-171231-m54moffr.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-171231-m54moffr.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 74542 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 88. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 85. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. parallel: Warning: No more processes: Decreasing number of running jobs to 84. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 75498 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 75091 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 87. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76171 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 75335 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76386 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 75084 Aborted $FILE2BIB "$FILE" > "$OUTPUT" parallel: Warning: No more processes: Decreasing number of running jobs to 86. parallel: Warning: Raising ulimit -u or /etc/security/limits.conf may help. === file2bib.sh === id: cord-048325-pk7pnmlo author: Hanley, Brian title: An object simulation model for modeling hypothetical disease epidemics – EpiFlex date: 2006-08-23 pages: extension: .txt txt: ./txt/cord-048325-pk7pnmlo.txt cache: ./cache/cord-048325-pk7pnmlo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-048325-pk7pnmlo.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76375 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76881 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/cordwrd2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2adr.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/cordent2carrel.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 75587 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 73947 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76854 Aborted $FILE2BIB "$FILE" > "$OUTPUT" /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76806 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 76850 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-034839-6xctzwng author: Bień-Barkowska, Katarzyna title: Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets date: 2020-07-20 pages: extension: .txt txt: ./txt/cord-034839-6xctzwng.txt cache: ./cache/cord-034839-6xctzwng.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-034839-6xctzwng.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 77565 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78679 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78840 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78622 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 79085 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-164964-vcxx1s6k author: Kharkwal, Himanshu title: University Operations During a Pandemic: A Flexible Decision Analysis Toolkit date: 2020-10-20 pages: extension: .txt txt: ./txt/cord-164964-vcxx1s6k.txt cache: ./cache/cord-164964-vcxx1s6k.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-164964-vcxx1s6k.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 77764 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78528 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78861 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 79144 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-024061-gxv8y146 author: Alkhamis, Moh A. title: Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks date: 2020-04-21 pages: extension: .txt txt: ./txt/cord-024061-gxv8y146.txt cache: ./cache/cord-024061-gxv8y146.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-024061-gxv8y146.txt' /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: Resource temporarily unavailable /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes /data-disk/reader-compute/reader-cord/bin/txt2urls.sh: fork: retry: No child processes === file2bib.sh === id: cord-191876-03a757gf author: Weinert, Andrew title: Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment date: 2020-08-03 pages: extension: .txt txt: ./txt/cord-191876-03a757gf.txt cache: ./cache/cord-191876-03a757gf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-191876-03a757gf.txt' === file2bib.sh === id: cord-195082-7tnwkxuh author: Oodally, Ajmal title: Modeling dependent survival data through random effects with spatial correlation at the subject level date: 2020-10-12 pages: extension: .txt txt: ./txt/cord-195082-7tnwkxuh.txt cache: ./cache/cord-195082-7tnwkxuh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-195082-7tnwkxuh.txt' === file2bib.sh === id: cord-229393-t3cpzmwj author: Srivastava, Ajitesh title: Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic date: 2020-04-23 pages: extension: .txt txt: ./txt/cord-229393-t3cpzmwj.txt cache: ./cache/cord-229393-t3cpzmwj.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-229393-t3cpzmwj.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 81725 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 80767 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 79507 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 80089 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 81006 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 80156 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === /data-disk/reader-compute/reader-cord/bin/file2bib.sh: fork: retry: No child processes id: cord-246317-wz7epr3n author: Wang, Wei-Yao title: EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods date: 2020-07-05 pages: extension: .txt txt: ./txt/cord-246317-wz7epr3n.txt cache: ./cache/cord-246317-wz7epr3n.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-246317-wz7epr3n.txt' === file2bib.sh === id: cord-034834-zap82dta author: Bai, Xiao title: A Review of Micro-Based Systemic Risk Research from Multiple Perspectives date: 2020-06-27 pages: extension: .txt txt: ./txt/cord-034834-zap82dta.txt cache: ./cache/cord-034834-zap82dta.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-034834-zap82dta.txt' === file2bib.sh === id: cord-021426-zo9dx8mr author: Peiffer, Robert L. title: Models in Ophthalmology and Vision Research date: 2013-10-21 pages: extension: .txt txt: ./txt/cord-021426-zo9dx8mr.txt cache: ./cache/cord-021426-zo9dx8mr.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-021426-zo9dx8mr.txt' === file2bib.sh === id: cord-133917-uap1vvbm author: Grave, Mal'u title: Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction Epidemiological Models date: 2020-10-22 pages: extension: .txt txt: ./txt/cord-133917-uap1vvbm.txt cache: ./cache/cord-133917-uap1vvbm.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-133917-uap1vvbm.txt' === file2bib.sh === id: cord-214774-yro1iw80 author: Srivastava, Anuj title: Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown Measures date: 2020-04-25 pages: extension: .txt txt: ./txt/cord-214774-yro1iw80.txt cache: ./cache/cord-214774-yro1iw80.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-214774-yro1iw80.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 81989 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 81890 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 78000 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 82034 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-229937-fy90oebs author: Amaro, J. E. title: Global analysis of the COVID-19 pandemic using simple epidemiological models date: 2020-05-14 pages: extension: .txt txt: ./txt/cord-229937-fy90oebs.txt cache: ./cache/cord-229937-fy90oebs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-229937-fy90oebs.txt' === file2bib.sh === id: cord-219817-dqmztvo4 author: Oghaz, Toktam A. title: Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model date: 2020-04-14 pages: extension: .txt txt: ./txt/cord-219817-dqmztvo4.txt cache: ./cache/cord-219817-dqmztvo4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-219817-dqmztvo4.txt' === file2bib.sh === id: cord-018899-tbfg0vmd author: Brauer, Fred title: Epidemic Models date: 2011-10-03 pages: extension: .txt txt: ./txt/cord-018899-tbfg0vmd.txt cache: ./cache/cord-018899-tbfg0vmd.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-018899-tbfg0vmd.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 83205 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 83585 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 79645 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-225640-l0z56qx4 author: Ghamizi, Salah title: Data-driven Simulation and Optimization for Covid-19 Exit Strategies date: 2020-06-12 pages: extension: .txt txt: ./txt/cord-225640-l0z56qx4.txt cache: ./cache/cord-225640-l0z56qx4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-225640-l0z56qx4.txt' === file2bib.sh === id: cord-241351-li476eqy author: Liu, Junhua title: CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding date: 2020-05-11 pages: extension: .txt txt: ./txt/cord-241351-li476eqy.txt cache: ./cache/cord-241351-li476eqy.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-241351-li476eqy.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 83613 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 83796 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 83790 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-130240-bfnav9sn author: Friston, Karl J. title: Dynamic causal modelling of COVID-19 date: 2020-04-09 pages: extension: .txt txt: ./txt/cord-130240-bfnav9sn.txt cache: ./cache/cord-130240-bfnav9sn.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-130240-bfnav9sn.txt' === file2bib.sh === id: cord-248050-apjwnwky author: Vrugt, Michael te title: Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model date: 2020-03-31 pages: extension: .txt txt: ./txt/cord-248050-apjwnwky.txt cache: ./cache/cord-248050-apjwnwky.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-248050-apjwnwky.txt' === file2bib.sh === id: cord-031957-df4luh5v author: dos Santos-Silva, Carlos André title: Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era date: 2020-09-02 pages: extension: .txt txt: ./txt/cord-031957-df4luh5v.txt cache: ./cache/cord-031957-df4luh5v.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-031957-df4luh5v.txt' === file2bib.sh === id: cord-259426-qbolo3k3 author: Tadesse, Trhas title: Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020 date: 2020-10-22 pages: extension: .txt txt: ./txt/cord-259426-qbolo3k3.txt cache: ./cache/cord-259426-qbolo3k3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-259426-qbolo3k3.txt' === file2bib.sh === id: cord-208252-e0vlaoii author: Calvetti, Daniela title: Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model date: 2020-05-09 pages: extension: .txt txt: ./txt/cord-208252-e0vlaoii.txt cache: ./cache/cord-208252-e0vlaoii.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-208252-e0vlaoii.txt' === file2bib.sh === id: cord-264136-jjtsd4n3 author: Ferstad, Johannes Opsahl title: A model to forecast regional demand for COVID-19 related hospital beds date: 2020-03-30 pages: extension: .txt txt: ./txt/cord-264136-jjtsd4n3.txt cache: ./cache/cord-264136-jjtsd4n3.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-264136-jjtsd4n3.txt' === file2bib.sh === id: cord-252903-pg0l92zb author: Abueg, M. title: Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state date: 2020-09-02 pages: extension: .txt txt: ./txt/cord-252903-pg0l92zb.txt cache: ./cache/cord-252903-pg0l92zb.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-252903-pg0l92zb.txt' === file2bib.sh === id: cord-232238-aicird98 author: Ferrario, Andrea title: A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations date: 2020-10-09 pages: extension: .txt txt: ./txt/cord-232238-aicird98.txt cache: ./cache/cord-232238-aicird98.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-232238-aicird98.txt' === file2bib.sh === id: cord-269873-4hxwo5kt author: R., Mohammadi title: Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images date: 2020-10-01 pages: extension: .txt txt: ./txt/cord-269873-4hxwo5kt.txt cache: ./cache/cord-269873-4hxwo5kt.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-269873-4hxwo5kt.txt' === file2bib.sh === id: cord-244657-zp65561y author: Hawryluk, Iwona title: Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection date: 2020-09-08 pages: extension: .txt txt: ./txt/cord-244657-zp65561y.txt cache: ./cache/cord-244657-zp65561y.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-244657-zp65561y.txt' === file2bib.sh === id: cord-281543-ivhr2no3 author: Richardson, Eugene T title: Pandemicity, COVID-19 and the limits of public health ‘science’ date: 2020-04-17 pages: extension: .txt txt: ./txt/cord-281543-ivhr2no3.txt cache: ./cache/cord-281543-ivhr2no3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-281543-ivhr2no3.txt' === file2bib.sh === /data-disk/reader-compute/reader-cord/bin/file2bib.sh: fork: retry: No child processes id: cord-266090-f40v4039 author: Gao, Wei title: New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system date: 2020-08-03 pages: extension: .txt txt: ./txt/cord-266090-f40v4039.txt cache: ./cache/cord-266090-f40v4039.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-266090-f40v4039.txt' === file2bib.sh === id: cord-277128-g90hp8j7 author: Rajendran, Sukumar title: Accessing Covid19 Epidemic Outbreak in Tamilnadu and the Impact of Lockdown through Epidemiological Models and Dynamic systems date: 2020-09-17 pages: extension: .txt txt: ./txt/cord-277128-g90hp8j7.txt cache: ./cache/cord-277128-g90hp8j7.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-277128-g90hp8j7.txt' === file2bib.sh === id: cord-255557-k0xat0u7 author: Mao, Liang title: Modeling monthly flows of global air travel passengers: An open-access data resource date: 2015-10-31 pages: extension: .txt txt: ./txt/cord-255557-k0xat0u7.txt cache: ./cache/cord-255557-k0xat0u7.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-255557-k0xat0u7.txt' === file2bib.sh === id: cord-266424-wchxkdtj author: Lofstedt, Jeanne title: Model to Predict Septicemia in Diarrheic Calves date: 2008-06-28 pages: extension: .txt txt: ./txt/cord-266424-wchxkdtj.txt cache: ./cache/cord-266424-wchxkdtj.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-266424-wchxkdtj.txt' === file2bib.sh === id: cord-262524-ununcin0 author: Bankhead, Armand title: A Simulation Framework to Investigate in vitro Viral Infection Dynamics date: 2011-12-31 pages: extension: .txt txt: ./txt/cord-262524-ununcin0.txt cache: ./cache/cord-262524-ununcin0.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-262524-ununcin0.txt' === file2bib.sh === id: cord-274513-0biyfhab author: Baumgartner, M. T. title: Assessing the relative contributions of healthcare protocols for epidemic control: an example with network transmission model for COVID-19 date: 2020-07-22 pages: extension: .txt txt: ./txt/cord-274513-0biyfhab.txt cache: ./cache/cord-274513-0biyfhab.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-274513-0biyfhab.txt' === file2bib.sh === id: cord-268142-lmkfxme5 author: Schafrum Macedo, Aline title: Animal modeling in bone research—Should we follow the White Rabbit? date: 2019-09-26 pages: extension: .txt txt: ./txt/cord-268142-lmkfxme5.txt cache: ./cache/cord-268142-lmkfxme5.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-268142-lmkfxme5.txt' === file2bib.sh === id: cord-266189-b3b36d72 author: Dignum, Frank title: Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation date: 2020-06-15 pages: extension: .txt txt: ./txt/cord-266189-b3b36d72.txt cache: ./cache/cord-266189-b3b36d72.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-266189-b3b36d72.txt' === file2bib.sh === id: cord-273815-7ftztaqn author: Gupta, R. K. title: Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date: 2020-07-26 pages: extension: .txt txt: ./txt/cord-273815-7ftztaqn.txt cache: ./cache/cord-273815-7ftztaqn.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-273815-7ftztaqn.txt' === file2bib.sh === id: cord-283092-t3yqsac3 author: Shah, Kamal title: Qualitative Analysis of a Mathematical Model in the Time of COVID-19 date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-283092-t3yqsac3.txt cache: ./cache/cord-283092-t3yqsac3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-283092-t3yqsac3.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 85942 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 85934 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 85713 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 63809 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 72108 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-277237-tjsw205c author: Hernandez Vargas, Esteban Abelardo title: In-host Modelling of COVID-19 Kinetics in Humans date: 2020-03-30 pages: extension: .txt txt: ./txt/cord-277237-tjsw205c.txt cache: ./cache/cord-277237-tjsw205c.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-277237-tjsw205c.txt' === file2bib.sh === id: cord-264994-j8iawzp8 author: Fitzpatrick, Meagan C. title: Modelling microbial infection to address global health challenges date: 2019-09-20 pages: extension: .txt txt: ./txt/cord-264994-j8iawzp8.txt cache: ./cache/cord-264994-j8iawzp8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-264994-j8iawzp8.txt' === file2bib.sh === id: cord-299312-asc120pn author: Khoshnaw, Sarbaz H.A. title: A Quantitative and Qualitative Analysis of the COVID–19 Pandemic Model date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-299312-asc120pn.txt cache: ./cache/cord-299312-asc120pn.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-299312-asc120pn.txt' === file2bib.sh === id: cord-222868-k3k0iqds author: Goswami, Anindya title: Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning date: 2020-08-02 pages: extension: .txt txt: ./txt/cord-222868-k3k0iqds.txt cache: ./cache/cord-222868-k3k0iqds.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-222868-k3k0iqds.txt' === file2bib.sh === id: cord-285897-ahysay2l author: Wu, Guangyao title: Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study date: 2020-07-02 pages: extension: .txt txt: ./txt/cord-285897-ahysay2l.txt cache: ./cache/cord-285897-ahysay2l.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-285897-ahysay2l.txt' === file2bib.sh === id: cord-295116-eo887olu author: Chimmula, Vinay Kumar Reddy title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() date: 2020-05-08 pages: extension: .txt txt: ./txt/cord-295116-eo887olu.txt cache: ./cache/cord-295116-eo887olu.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-295116-eo887olu.txt' === file2bib.sh === id: cord-281122-dtgmn9e0 author: Ribeiro, Matheus Henrique Dal Molin title: Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods date: 2020-09-22 pages: extension: .txt txt: ./txt/cord-281122-dtgmn9e0.txt cache: ./cache/cord-281122-dtgmn9e0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-281122-dtgmn9e0.txt' === file2bib.sh === id: cord-269559-gvvnvcfo author: Kergaßner, Andreas title: Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany date: 2020-08-03 pages: extension: .txt txt: ./txt/cord-269559-gvvnvcfo.txt cache: ./cache/cord-269559-gvvnvcfo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-269559-gvvnvcfo.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 86756 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 86620 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 86271 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 85365 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-270249-miys1fve author: Liu, Xianbo title: COVID-19: data-driven dynamics, statistical and distributed delay models, and observations date: 2020-08-06 pages: extension: .txt txt: ./txt/cord-270249-miys1fve.txt cache: ./cache/cord-270249-miys1fve.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-270249-miys1fve.txt' === file2bib.sh === id: cord-258316-uiusqr59 author: Spil, Ton A.M. title: Are serious games too serious? Diffusion of wearable technologies and the creation of a diffusion of serious games model date: 2020-08-18 pages: extension: .txt txt: ./txt/cord-258316-uiusqr59.txt cache: ./cache/cord-258316-uiusqr59.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-258316-uiusqr59.txt' === file2bib.sh === id: cord-292699-855am0mv author: Engbert, Ralf title: Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics date: 2020-04-17 pages: extension: .txt txt: ./txt/cord-292699-855am0mv.txt cache: ./cache/cord-292699-855am0mv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-292699-855am0mv.txt' === file2bib.sh === id: cord-276782-3fpmatkb author: Garbey, M. title: A Model of Workflow in the Hospital During a Pandemic to Assist Management date: 2020-05-02 pages: extension: .txt txt: ./txt/cord-276782-3fpmatkb.txt cache: ./cache/cord-276782-3fpmatkb.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-276782-3fpmatkb.txt' === file2bib.sh === id: cord-297161-ziwfr9dv author: Sauter, T. title: TESTING INFORMED SIR BASED EPIDEMIOLOGICAL MODEL FOR COVID-19 IN LUXEMBOURG date: 2020-07-25 pages: extension: .txt txt: ./txt/cord-297161-ziwfr9dv.txt cache: ./cache/cord-297161-ziwfr9dv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-297161-ziwfr9dv.txt' === file2bib.sh === id: cord-167889-um3djluz author: Chen, Jianguo title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date: 2020-07-04 pages: extension: .txt txt: ./txt/cord-167889-um3djluz.txt cache: ./cache/cord-167889-um3djluz.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-167889-um3djluz.txt' === file2bib.sh === id: cord-280064-rz8cglyt author: Gwizdałła, Tomasz title: Viral disease spreading in grouped population date: 2020-08-27 pages: extension: .txt txt: ./txt/cord-280064-rz8cglyt.txt cache: ./cache/cord-280064-rz8cglyt.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-280064-rz8cglyt.txt' === file2bib.sh === id: cord-299439-xvfab24g author: Fokas, A. S. title: COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA date: 2020-05-12 pages: extension: .txt txt: ./txt/cord-299439-xvfab24g.txt cache: ./cache/cord-299439-xvfab24g.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-299439-xvfab24g.txt' === file2bib.sh === id: cord-290952-tbsccwgx author: Ullah, Saif title: Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study date: 2020-07-03 pages: extension: .txt txt: ./txt/cord-290952-tbsccwgx.txt cache: ./cache/cord-290952-tbsccwgx.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-290952-tbsccwgx.txt' === file2bib.sh === id: cord-280683-5572l6bo author: Liu, Laura title: Panel forecasts of country-level Covid-19 infections() date: 2020-10-16 pages: extension: .txt txt: ./txt/cord-280683-5572l6bo.txt cache: ./cache/cord-280683-5572l6bo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-280683-5572l6bo.txt' === file2bib.sh === id: cord-225347-lnzz2chk author: Chakraborty, Tanujit title: Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges date: 2020-10-10 pages: extension: .txt txt: ./txt/cord-225347-lnzz2chk.txt cache: ./cache/cord-225347-lnzz2chk.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-225347-lnzz2chk.txt' === file2bib.sh === id: cord-307340-00m2g55u author: Gerasimov, A. title: Reaching collective immunity for COVID-19: an estimate with a heterogeneous model based on the data for Italy date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-307340-00m2g55u.txt cache: ./cache/cord-307340-00m2g55u.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-307340-00m2g55u.txt' === file2bib.sh === /data-disk/reader-compute/reader-cord/bin/file2bib.sh: fork: retry: No child processes id: cord-293333-mqoml9o5 author: Scharbarg, Emeric title: From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves date: 2020-10-03 pages: extension: .txt txt: ./txt/cord-293333-mqoml9o5.txt cache: ./cache/cord-293333-mqoml9o5.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-293333-mqoml9o5.txt' === file2bib.sh === id: cord-289325-jhokn5bu author: Lachiany, Menachem title: Effects of distribution of infection rate on epidemic models date: 2016-08-11 pages: extension: .txt txt: ./txt/cord-289325-jhokn5bu.txt cache: ./cache/cord-289325-jhokn5bu.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-289325-jhokn5bu.txt' === file2bib.sh === id: cord-296388-ayfdsn07 author: Maziarz, Mariusz title: Agent‐based modelling for SARS‐CoV‐2 epidemic prediction and intervention assessment: A methodological appraisal date: 2020-08-21 pages: extension: .txt txt: ./txt/cord-296388-ayfdsn07.txt cache: ./cache/cord-296388-ayfdsn07.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-296388-ayfdsn07.txt' === file2bib.sh === id: cord-308115-bjyr6ehq author: Baba, Isa Abdullah title: Fractional Order Model for the Role of Mild Cases in the Transmission of COVID-19 date: 2020-10-20 pages: extension: .txt txt: ./txt/cord-308115-bjyr6ehq.txt cache: ./cache/cord-308115-bjyr6ehq.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-308115-bjyr6ehq.txt' === file2bib.sh === id: cord-297517-w8cvq0m5 author: Toğaçar, Mesut title: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches date: 2020-05-06 pages: extension: .txt txt: ./txt/cord-297517-w8cvq0m5.txt cache: ./cache/cord-297517-w8cvq0m5.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-297517-w8cvq0m5.txt' === file2bib.sh === id: cord-311432-js84ruve author: Hossein Rashidi, T. title: Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment date: 2020-04-29 pages: extension: .txt txt: ./txt/cord-311432-js84ruve.txt cache: ./cache/cord-311432-js84ruve.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-311432-js84ruve.txt' === file2bib.sh === id: cord-313279-15wii9nn author: Trevijano-Contador, Nuria title: Expanding the use of alternative models to investigate novel aspects of immunity to microbial pathogens date: 2014-05-15 pages: extension: .txt txt: ./txt/cord-313279-15wii9nn.txt cache: ./cache/cord-313279-15wii9nn.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-313279-15wii9nn.txt' === file2bib.sh === id: cord-310406-5pvln91x author: Asbury, Thomas M title: Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome date: 2010-09-02 pages: extension: .txt txt: ./txt/cord-310406-5pvln91x.txt cache: ./cache/cord-310406-5pvln91x.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-310406-5pvln91x.txt' === file2bib.sh === id: cord-289447-d93qwjui author: Helmy, Mohamed title: Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering date: 2020-10-09 pages: extension: .txt txt: ./txt/cord-289447-d93qwjui.txt cache: ./cache/cord-289447-d93qwjui.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-289447-d93qwjui.txt' === file2bib.sh === id: cord-274732-mh0xixzh author: Faizal, W.M. title: Computational fluid dynamics modelling of human upper airway: a review date: 2020-06-26 pages: extension: .txt txt: ./txt/cord-274732-mh0xixzh.txt cache: ./cache/cord-274732-mh0xixzh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 5 resourceName b'cord-274732-mh0xixzh.txt' === file2bib.sh === id: cord-022219-y7vsc6r7 author: PEIFFER, ROBERT L. title: Animals in Ophthalmic Research: Concepts and Methodologies date: 2013-11-17 pages: extension: .txt txt: ./txt/cord-022219-y7vsc6r7.txt cache: ./cache/cord-022219-y7vsc6r7.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-022219-y7vsc6r7.txt' === file2bib.sh === id: cord-307133-bm9z8gss author: Kong, Lingcai title: Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model date: 2016-02-24 pages: extension: .txt txt: ./txt/cord-307133-bm9z8gss.txt cache: ./cache/cord-307133-bm9z8gss.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-307133-bm9z8gss.txt' === file2bib.sh === id: cord-298646-wurzy88k author: van der Merwe, René title: Challenge models to assess new therapies in chronic obstructive pulmonary disease date: 2012-09-13 pages: extension: .txt txt: ./txt/cord-298646-wurzy88k.txt cache: ./cache/cord-298646-wurzy88k.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-298646-wurzy88k.txt' === file2bib.sh === id: cord-285435-fu90vb2z author: Björklund, Tua A. title: Expanding entrepreneurial solution spaces in times of crisis: Business model experimentation amongst packaged food and beverage ventures date: 2020-11-30 pages: extension: .txt txt: ./txt/cord-285435-fu90vb2z.txt cache: ./cache/cord-285435-fu90vb2z.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-285435-fu90vb2z.txt' === file2bib.sh === id: cord-308652-i6q23olv author: Cobos-Sanchiz, David title: The Importance of Work-Related Events and Changes in Psychological Distress and Life Satisfaction amongst Young Workers in Spain: A Gender Analysis date: 2020-06-30 pages: extension: .txt txt: ./txt/cord-308652-i6q23olv.txt cache: ./cache/cord-308652-i6q23olv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-308652-i6q23olv.txt' === file2bib.sh === id: cord-268779-qbn3i2nq author: Alrasheed, Hend title: COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis date: 2020-10-23 pages: extension: .txt txt: ./txt/cord-268779-qbn3i2nq.txt cache: ./cache/cord-268779-qbn3i2nq.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-268779-qbn3i2nq.txt' === file2bib.sh === id: cord-293893-ibca88xu author: Xie, Tian title: Parallel Evolution and Response Decision Method for Public Sentiment based on System Dynamics date: 2020-05-23 pages: extension: .txt txt: ./txt/cord-293893-ibca88xu.txt cache: ./cache/cord-293893-ibca88xu.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-293893-ibca88xu.txt' === file2bib.sh === id: cord-310863-jxbw8wl2 author: PRASAD, J. title: A data first approach to modelling Covid-19 date: 2020-05-26 pages: extension: .txt txt: ./txt/cord-310863-jxbw8wl2.txt cache: ./cache/cord-310863-jxbw8wl2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-310863-jxbw8wl2.txt' === file2bib.sh === id: cord-302336-zj3oixvk author: Clift, Ash K title: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study date: 2020-10-21 pages: extension: .txt txt: ./txt/cord-302336-zj3oixvk.txt cache: ./cache/cord-302336-zj3oixvk.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-302336-zj3oixvk.txt' === file2bib.sh === id: cord-276218-dcg9oq6y author: Kim, Jihoon title: Human organoids: model systems for human biology and medicine date: 2020-07-07 pages: extension: .txt txt: ./txt/cord-276218-dcg9oq6y.txt cache: ./cache/cord-276218-dcg9oq6y.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-276218-dcg9oq6y.txt' === file2bib.sh === id: cord-293562-69nnyq8p author: Imran, Mudassar title: Mathematical analysis of the role of hospitalization/isolation in controlling the spread of Zika fever date: 2018-08-15 pages: extension: .txt txt: ./txt/cord-293562-69nnyq8p.txt cache: ./cache/cord-293562-69nnyq8p.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-293562-69nnyq8p.txt' === file2bib.sh === id: cord-318900-dovu6kha author: Pitschel, T. title: SARS-Cov-2 proliferation: an analytical aggregate-level model date: 2020-08-22 pages: extension: .txt txt: ./txt/cord-318900-dovu6kha.txt cache: ./cache/cord-318900-dovu6kha.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-318900-dovu6kha.txt' === file2bib.sh === id: cord-311086-i4e0rdxp author: Adekola, Hafeez Aderinsayo title: Mathematical modeling for infectious viral disease: The COVID‐19 perspective date: 2020-08-17 pages: extension: .txt txt: ./txt/cord-311086-i4e0rdxp.txt cache: ./cache/cord-311086-i4e0rdxp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-311086-i4e0rdxp.txt' === file2bib.sh === id: cord-283907-ev1ghlwl author: Cao, Lingyan title: Electrical load prediction of healthcare buildings through single and ensemble learning date: 2020-11-30 pages: extension: .txt txt: ./txt/cord-283907-ev1ghlwl.txt cache: ./cache/cord-283907-ev1ghlwl.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-283907-ev1ghlwl.txt' === file2bib.sh === id: cord-301505-np4nr7gg author: Lin, Xin title: Two types of transmembrane homomeric interactions in the integrin receptor family are evolutionarily conserved date: 2006-01-27 pages: extension: .txt txt: ./txt/cord-301505-np4nr7gg.txt cache: ./cache/cord-301505-np4nr7gg.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-301505-np4nr7gg.txt' === file2bib.sh === id: cord-309096-vwbpkpxd author: Magdon-Ismail, Malik title: Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics date: 2020-03-20 pages: extension: .txt txt: ./txt/cord-309096-vwbpkpxd.txt cache: ./cache/cord-309096-vwbpkpxd.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-309096-vwbpkpxd.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 88509 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-313046-3g2us5zh author: Taghizadeh, L. title: Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic date: 2020-06-03 pages: extension: .txt txt: ./txt/cord-313046-3g2us5zh.txt cache: ./cache/cord-313046-3g2us5zh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-313046-3g2us5zh.txt' === file2bib.sh === id: cord-303187-ny4qr2a2 author: Belo, Vinícius Silva title: Abundance, survival, recruitment and effectiveness of sterilization of free-roaming dogs: A capture and recapture study in Brazil date: 2017-11-01 pages: extension: .txt txt: ./txt/cord-303187-ny4qr2a2.txt cache: ./cache/cord-303187-ny4qr2a2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-303187-ny4qr2a2.txt' === file2bib.sh === id: cord-312911-nqq87d0m author: Zou, D. title: Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States date: 2020-05-25 pages: extension: .txt txt: ./txt/cord-312911-nqq87d0m.txt cache: ./cache/cord-312911-nqq87d0m.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-312911-nqq87d0m.txt' === file2bib.sh === id: cord-308219-97gor71p author: Elzeiny, Sami title: Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images date: 2020-09-17 pages: extension: .txt txt: ./txt/cord-308219-97gor71p.txt cache: ./cache/cord-308219-97gor71p.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-308219-97gor71p.txt' === file2bib.sh === id: cord-295786-cpuz08vl author: Castillo-Sánchez, Gema title: Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review date: 2020-11-09 pages: extension: .txt txt: ./txt/cord-295786-cpuz08vl.txt cache: ./cache/cord-295786-cpuz08vl.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-295786-cpuz08vl.txt' === file2bib.sh === id: cord-299852-t0mqe7yy author: Janssen, Loes H. C. title: Does the COVID-19 pandemic impact parents’ and adolescents’ well-being? An EMA-study on daily affect and parenting date: 2020-10-16 pages: extension: .txt txt: ./txt/cord-299852-t0mqe7yy.txt cache: ./cache/cord-299852-t0mqe7yy.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-299852-t0mqe7yy.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 89354 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 89070 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 88655 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-296565-apqm0i58 author: Togati, Teodoro Dario title: General Theorizing and Historical Specificity in the ‘Keynes Versus the Classics’ Dispute’ date: 2020-10-06 pages: extension: .txt txt: ./txt/cord-296565-apqm0i58.txt cache: ./cache/cord-296565-apqm0i58.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-296565-apqm0i58.txt' === file2bib.sh === id: cord-325738-c800ynvc author: Shi, Pengpeng title: SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration date: 2020-02-20 pages: extension: .txt txt: ./txt/cord-325738-c800ynvc.txt cache: ./cache/cord-325738-c800ynvc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-325738-c800ynvc.txt' === file2bib.sh === id: cord-283678-xdma6vyo author: Séférian, Roland title: Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6 date: 2020-08-18 pages: extension: .txt txt: ./txt/cord-283678-xdma6vyo.txt cache: ./cache/cord-283678-xdma6vyo.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-283678-xdma6vyo.txt' === file2bib.sh === id: cord-318079-jvx1rh7g author: Hinch, R. title: OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing date: 2020-09-22 pages: extension: .txt txt: ./txt/cord-318079-jvx1rh7g.txt cache: ./cache/cord-318079-jvx1rh7g.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-318079-jvx1rh7g.txt' === file2bib.sh === id: cord-284617-uwby8r3y author: Area, Iván title: Determination in Galicia of the required beds at Intensive Care Units date: 2020-10-06 pages: extension: .txt txt: ./txt/cord-284617-uwby8r3y.txt cache: ./cache/cord-284617-uwby8r3y.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-284617-uwby8r3y.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 89894 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-309010-tmfm5u5h author: Dietert, Kristina title: Spectrum of pathogen- and model-specific histopathologies in mouse models of acute pneumonia date: 2017-11-20 pages: extension: .txt txt: ./txt/cord-309010-tmfm5u5h.txt cache: ./cache/cord-309010-tmfm5u5h.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-309010-tmfm5u5h.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 89580 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 90058 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 87614 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-290421-9v841ose author: Weston, Dale title: Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews date: 2020-10-01 pages: extension: .txt txt: ./txt/cord-290421-9v841ose.txt cache: ./cache/cord-290421-9v841ose.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-290421-9v841ose.txt' === file2bib.sh === id: cord-317993-012hx4kc author: Movia, Dania title: Preclinical Development of Orally Inhaled Drugs (OIDs)—Are Animal Models Predictive or Shall We Move Towards In Vitro Non-Animal Models? date: 2020-07-24 pages: extension: .txt txt: ./txt/cord-317993-012hx4kc.txt cache: ./cache/cord-317993-012hx4kc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-317993-012hx4kc.txt' === file2bib.sh === id: cord-326280-kjjljbl5 author: Abdo, Mohammed S. title: Existence theory and numerical analysis of three species prey–predator model under Mittag-Leffler power law date: 2020-05-27 pages: extension: .txt txt: ./txt/cord-326280-kjjljbl5.txt cache: ./cache/cord-326280-kjjljbl5.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-326280-kjjljbl5.txt' === file2bib.sh === id: cord-332729-f1e334g0 author: Shah, Nirav R. title: An Impact-Oriented Approach to Epidemiological Modeling date: 2020-09-21 pages: extension: .txt txt: ./txt/cord-332729-f1e334g0.txt cache: ./cache/cord-332729-f1e334g0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-332729-f1e334g0.txt' === file2bib.sh === OMP: Error #34: System unable to allocate necessary resources for OMP thread: OMP: System error #11: Resource temporarily unavailable OMP: Hint Try decreasing the value of OMP_NUM_THREADS. /data-disk/reader-compute/reader-cord/bin/file2bib.sh: line 39: 90382 Aborted $FILE2BIB "$FILE" > "$OUTPUT" === file2bib.sh === id: cord-305318-cont592g author: Lancaster, Madeline A. title: Disease modelling in human organoids date: 2019-07-01 pages: extension: .txt txt: ./txt/cord-305318-cont592g.txt cache: ./cache/cord-305318-cont592g.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-305318-cont592g.txt' === file2bib.sh === id: cord-303651-fkdep6cp author: Thompson, Robin N. title: Key questions for modelling COVID-19 exit strategies date: 2020-08-12 pages: extension: .txt txt: ./txt/cord-303651-fkdep6cp.txt cache: ./cache/cord-303651-fkdep6cp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-303651-fkdep6cp.txt' === file2bib.sh === id: cord-326409-m3rgspxc author: Lai, Alvin C.K. title: Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors date: 2007-03-24 pages: extension: .txt txt: ./txt/cord-326409-m3rgspxc.txt cache: ./cache/cord-326409-m3rgspxc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-326409-m3rgspxc.txt' === file2bib.sh === id: cord-316393-ozl28ztz author: Enrique Amaro, José title: Global analysis of the COVID-19 pandemic using simple epidemiological models date: 2020-10-22 pages: extension: .txt txt: ./txt/cord-316393-ozl28ztz.txt cache: ./cache/cord-316393-ozl28ztz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-316393-ozl28ztz.txt' === file2bib.sh === id: cord-325321-37kyd8ak author: Iftikhar, H. title: Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study date: 2020-09-22 pages: extension: .txt txt: ./txt/cord-325321-37kyd8ak.txt cache: ./cache/cord-325321-37kyd8ak.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-325321-37kyd8ak.txt' === file2bib.sh === id: cord-312366-8qg1fn8f author: Adiga, Aniruddha title: Mathematical Models for COVID-19 Pandemic: A Comparative Analysis date: 2020-10-30 pages: extension: .txt txt: ./txt/cord-312366-8qg1fn8f.txt cache: ./cache/cord-312366-8qg1fn8f.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-312366-8qg1fn8f.txt' === file2bib.sh === id: cord-315462-u2dj79yw author: Hewitt, Judith A. title: ACTIVating Resources for the COVID-19 Pandemic: In vivo Models for Vaccines and Therapeutics date: 2020-10-01 pages: extension: .txt txt: ./txt/cord-315462-u2dj79yw.txt cache: ./cache/cord-315462-u2dj79yw.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-315462-u2dj79yw.txt' === file2bib.sh === id: cord-326831-dvg0isgt author: Muhammad, L. J. title: Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery date: 2020-06-21 pages: extension: .txt txt: ./txt/cord-326831-dvg0isgt.txt cache: ./cache/cord-326831-dvg0isgt.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-326831-dvg0isgt.txt' === file2bib.sh === id: cord-324254-qikr9ryf author: Lyócsa, Štefan title: FX Market Volatility Modelling: Can we use low-frequency data? date: 2020-09-30 pages: extension: .txt txt: ./txt/cord-324254-qikr9ryf.txt cache: ./cache/cord-324254-qikr9ryf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-324254-qikr9ryf.txt' === file2bib.sh === id: cord-321735-c40m2o5l author: Manca, Davide title: A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under Covid-19 pandemic date: 2020-06-04 pages: extension: .txt txt: ./txt/cord-321735-c40m2o5l.txt cache: ./cache/cord-321735-c40m2o5l.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-321735-c40m2o5l.txt' === file2bib.sh === id: cord-310844-7i92mk4x author: Hryhorowicz, Magdalena title: Application of Genetically Engineered Pigs in Biomedical Research date: 2020-06-19 pages: extension: .txt txt: ./txt/cord-310844-7i92mk4x.txt cache: ./cache/cord-310844-7i92mk4x.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 5 resourceName b'cord-310844-7i92mk4x.txt' === file2bib.sh === id: cord-320141-892v3b7m author: Boshra, Mina title: 3D printing in critical care: a narrative review date: 2020-09-30 pages: extension: .txt txt: ./txt/cord-320141-892v3b7m.txt cache: ./cache/cord-320141-892v3b7m.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-320141-892v3b7m.txt' === file2bib.sh === id: cord-301117-egd1gxby author: Barh, Debmalya title: In Silico Models: From Simple Networks to Complex Diseases date: 2013-11-15 pages: extension: .txt txt: ./txt/cord-301117-egd1gxby.txt cache: ./cache/cord-301117-egd1gxby.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-301117-egd1gxby.txt' === file2bib.sh === id: cord-319291-6l688krc author: Hung, Chun-Min title: Alignment using genetic programming with causal trees for identification of protein functions date: 2006-09-01 pages: extension: .txt txt: ./txt/cord-319291-6l688krc.txt cache: ./cache/cord-319291-6l688krc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-319291-6l688krc.txt' === file2bib.sh === id: cord-330474-c6eq1djd author: Fox, J title: Rapid translation of clinical guidelines into executable knowledge: a case study of COVID‐19 and on‐line demonstration date: 2020-06-18 pages: extension: .txt txt: ./txt/cord-330474-c6eq1djd.txt cache: ./cache/cord-330474-c6eq1djd.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-330474-c6eq1djd.txt' === file2bib.sh === id: cord-326314-9ycht8gi author: Armstrong, Eve title: Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation date: 2020-11-02 pages: extension: .txt txt: ./txt/cord-326314-9ycht8gi.txt cache: ./cache/cord-326314-9ycht8gi.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-326314-9ycht8gi.txt' === file2bib.sh === id: cord-296826-870mxd1t author: Taghikhah, Firouzeh title: Integrated modeling of extended agro-food supply chains: A systems approach date: 2020-06-27 pages: extension: .txt txt: ./txt/cord-296826-870mxd1t.txt cache: ./cache/cord-296826-870mxd1t.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-296826-870mxd1t.txt' === file2bib.sh === id: cord-302277-c66xm2n4 author: Bakaletz, Lauren O. title: Developing animal models for polymicrobial diseases date: 2004 pages: extension: .txt txt: ./txt/cord-302277-c66xm2n4.txt cache: ./cache/cord-302277-c66xm2n4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-302277-c66xm2n4.txt' === file2bib.sh === id: cord-324924-5f7b02yq author: Agarwal, A. title: A TRANSPARENT, OPEN-SOURCE SIRD MODEL FOR COVID19DEATH PROJECTIONS IN INDIA date: 2020-06-04 pages: extension: .txt txt: ./txt/cord-324924-5f7b02yq.txt cache: ./cache/cord-324924-5f7b02yq.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-324924-5f7b02yq.txt' === file2bib.sh === id: cord-329256-7njgmdd1 author: Leecaster, Molly title: Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics date: 2011-04-21 pages: extension: .txt txt: ./txt/cord-329256-7njgmdd1.txt cache: ./cache/cord-329256-7njgmdd1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-329256-7njgmdd1.txt' === file2bib.sh === id: cord-322577-5bboc1z0 author: Parola, Anna title: Mental Health Through the COVID-19 Quarantine: A Growth Curve Analysis on Italian Young Adults date: 2020-10-02 pages: extension: .txt txt: ./txt/cord-322577-5bboc1z0.txt cache: ./cache/cord-322577-5bboc1z0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-322577-5bboc1z0.txt' === file2bib.sh === id: cord-321852-e7369brf author: Wang, Bo title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system date: 2020-11-10 pages: extension: .txt txt: ./txt/cord-321852-e7369brf.txt cache: ./cache/cord-321852-e7369brf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-321852-e7369brf.txt' === file2bib.sh === id: cord-329534-deoyowto author: McBryde, Emma S. title: Role of modelling in COVID-19 policy development date: 2020-06-18 pages: extension: .txt txt: ./txt/cord-329534-deoyowto.txt cache: ./cache/cord-329534-deoyowto.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-329534-deoyowto.txt' === file2bib.sh === id: cord-288342-i37v602u author: Wang, Zhen title: Coupled disease–behavior dynamics on complex networks: A review date: 2015-07-08 pages: extension: .txt txt: ./txt/cord-288342-i37v602u.txt cache: ./cache/cord-288342-i37v602u.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-288342-i37v602u.txt' === file2bib.sh === id: cord-318562-jif88gof author: Jiménez-Liso, Maria Rut title: Changing How We Teach Acid-Base Chemistry: A Proposal Grounded in Studies of the History and Nature of Science Education date: 2020-08-15 pages: extension: .txt txt: ./txt/cord-318562-jif88gof.txt cache: ./cache/cord-318562-jif88gof.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-318562-jif88gof.txt' === file2bib.sh === id: cord-331849-o346txxr author: Cardoso, Pedro J.S. title: Computational Science in the Interconnected World: Selected papers from 2019 International Conference on Computational Science date: 2020-09-21 pages: extension: .txt txt: ./txt/cord-331849-o346txxr.txt cache: ./cache/cord-331849-o346txxr.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-331849-o346txxr.txt' === file2bib.sh === id: cord-297530-7zbvgvk8 author: Kühnert, Denise title: Phylogenetic and epidemic modeling of rapidly evolving infectious diseases date: 2011-08-31 pages: extension: .txt txt: ./txt/cord-297530-7zbvgvk8.txt cache: ./cache/cord-297530-7zbvgvk8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-297530-7zbvgvk8.txt' === file2bib.sh === id: cord-330668-7aw17jf8 author: Chen, Cheng-Chang title: ORF8a of SARS-CoV forms an ion channel: Experiments and molecular dynamics simulations date: 2011-02-28 pages: extension: .txt txt: ./txt/cord-330668-7aw17jf8.txt cache: ./cache/cord-330668-7aw17jf8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-330668-7aw17jf8.txt' === file2bib.sh === id: cord-330596-p4k7jexz author: Hu, Ji title: An integrated classification model for incremental learning date: 2020-10-21 pages: extension: .txt txt: ./txt/cord-330596-p4k7jexz.txt cache: ./cache/cord-330596-p4k7jexz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-330596-p4k7jexz.txt' === file2bib.sh === id: cord-332618-8al98ya2 author: Barraza, Néstor Ruben title: A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic date: 2020-09-18 pages: extension: .txt txt: ./txt/cord-332618-8al98ya2.txt cache: ./cache/cord-332618-8al98ya2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-332618-8al98ya2.txt' === file2bib.sh === id: cord-333693-z2ni79al author: Wu, Lin title: Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion date: 2020-08-06 pages: extension: .txt txt: ./txt/cord-333693-z2ni79al.txt cache: ./cache/cord-333693-z2ni79al.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-333693-z2ni79al.txt' === file2bib.sh === id: cord-332922-2qjae0x7 author: Mbuvha, Rendani title: Bayesian inference of COVID-19 spreading rates in South Africa date: 2020-08-05 pages: extension: .txt txt: ./txt/cord-332922-2qjae0x7.txt cache: ./cache/cord-332922-2qjae0x7.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-332922-2qjae0x7.txt' === file2bib.sh === id: cord-322806-g01wmmbx author: Sturniolo, S. title: Testing, tracing and isolation in compartmental models date: 2020-05-19 pages: extension: .txt txt: ./txt/cord-322806-g01wmmbx.txt cache: ./cache/cord-322806-g01wmmbx.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-322806-g01wmmbx.txt' === file2bib.sh === id: cord-320666-cmqj8get author: Walach, H. title: What association do political interventions, environmental and health variables have with the number of Covid-19 cases and deaths? A linear modeling approach date: 2020-06-22 pages: extension: .txt txt: ./txt/cord-320666-cmqj8get.txt cache: ./cache/cord-320666-cmqj8get.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-320666-cmqj8get.txt' === file2bib.sh === id: cord-321715-bkfkmtld author: Redelings, Benjamin D title: Incorporating indel information into phylogeny estimation for rapidly emerging pathogens date: 2007-03-14 pages: extension: .txt txt: ./txt/cord-321715-bkfkmtld.txt cache: ./cache/cord-321715-bkfkmtld.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-321715-bkfkmtld.txt' === file2bib.sh === id: cord-324230-nu0pn2q8 author: Ardabili, S. F. title: COVID-19 Outbreak Prediction with Machine Learning date: 2020-04-22 pages: extension: .txt txt: ./txt/cord-324230-nu0pn2q8.txt cache: ./cache/cord-324230-nu0pn2q8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-324230-nu0pn2q8.txt' === file2bib.sh === id: cord-332412-lrn0wpvj author: Ibrahim, Mohamed R. title: Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe date: 2020-04-24 pages: extension: .txt txt: ./txt/cord-332412-lrn0wpvj.txt cache: ./cache/cord-332412-lrn0wpvj.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-332412-lrn0wpvj.txt' === file2bib.sh === id: cord-335418-s8ugu8e1 author: Annan, James D title: Model calibration, nowcasting, and operational prediction of the COVID-19 pandemic date: 2020-04-17 pages: extension: .txt txt: ./txt/cord-335418-s8ugu8e1.txt cache: ./cache/cord-335418-s8ugu8e1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-335418-s8ugu8e1.txt' === file2bib.sh === id: cord-326908-l9wrrapv author: Duchêne, David A. title: Evaluating the Adequacy of Molecular Clock Models Using Posterior Predictive Simulations date: 2015-07-10 pages: extension: .txt txt: ./txt/cord-326908-l9wrrapv.txt cache: ./cache/cord-326908-l9wrrapv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-326908-l9wrrapv.txt' === file2bib.sh === id: cord-332093-iluqwwxs author: Lessler, Justin title: Mechanistic Models of Infectious Disease and Their Impact on Public Health date: 2016-02-17 pages: extension: .txt txt: ./txt/cord-332093-iluqwwxs.txt cache: ./cache/cord-332093-iluqwwxs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-332093-iluqwwxs.txt' === file2bib.sh === /data-disk/reader-compute/reader-cord/bin/file2bib.sh: fork: retry: No child processes id: cord-327784-xet20fcw author: Rieke, Nicola title: The future of digital health with federated learning date: 2020-09-14 pages: extension: .txt txt: ./txt/cord-327784-xet20fcw.txt cache: ./cache/cord-327784-xet20fcw.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-327784-xet20fcw.txt' === file2bib.sh === id: cord-325862-rohhvq4h author: Zhang, Yong title: Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 date: 2020-06-04 pages: extension: .txt txt: ./txt/cord-325862-rohhvq4h.txt cache: ./cache/cord-325862-rohhvq4h.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-325862-rohhvq4h.txt' === file2bib.sh === id: cord-326540-1r4gm2d4 author: Liu, Yuliang title: Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically date: 2020-03-11 pages: extension: .txt txt: ./txt/cord-326540-1r4gm2d4.txt cache: ./cache/cord-326540-1r4gm2d4.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-326540-1r4gm2d4.txt' === file2bib.sh === id: cord-330714-hhvap8ts author: Shah, Kamal title: Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan date: 2020-11-12 pages: extension: .txt txt: ./txt/cord-330714-hhvap8ts.txt cache: ./cache/cord-330714-hhvap8ts.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-330714-hhvap8ts.txt' === file2bib.sh === id: cord-330148-yltc6wpv author: Lessler, Justin title: Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases date: 2016-07-02 pages: extension: .txt txt: ./txt/cord-330148-yltc6wpv.txt cache: ./cache/cord-330148-yltc6wpv.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-330148-yltc6wpv.txt' === file2bib.sh === id: cord-263620-9rvlnqxk author: Li, Zhi-Chun title: Fifty years of the bottleneck model: A bibliometric review and future research directions date: 2020-09-30 pages: extension: .txt txt: ./txt/cord-263620-9rvlnqxk.txt cache: ./cache/cord-263620-9rvlnqxk.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-263620-9rvlnqxk.txt' === file2bib.sh === id: cord-333088-ygdau2px author: Roy, Manojit title: On representing network heterogeneities in the incidence rate of simple epidemic models date: 2006-03-31 pages: extension: .txt txt: ./txt/cord-333088-ygdau2px.txt cache: ./cache/cord-333088-ygdau2px.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-333088-ygdau2px.txt' === file2bib.sh === id: cord-335465-sckfkciz author: Gupta, Rishi K. title: Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date: 2020-09-25 pages: extension: .txt txt: ./txt/cord-335465-sckfkciz.txt cache: ./cache/cord-335465-sckfkciz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-335465-sckfkciz.txt' === file2bib.sh === id: cord-332583-5enha3g9 author: Bodine, Erin N. title: Agent-Based Modeling and Simulation in Mathematics and Biology Education date: 2020-07-28 pages: extension: .txt txt: ./txt/cord-332583-5enha3g9.txt cache: ./cache/cord-332583-5enha3g9.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-332583-5enha3g9.txt' === file2bib.sh === id: cord-344115-gtbkwuqv author: Grimm, Volker title: Three questions to ask before using model outputs for decision support date: 2020-09-30 pages: extension: .txt txt: ./txt/cord-344115-gtbkwuqv.txt cache: ./cache/cord-344115-gtbkwuqv.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-344115-gtbkwuqv.txt' === file2bib.sh === id: cord-347906-3ehsg8oi author: Zhang, Zizhen title: Dynamics of COVID-19 mathematical model with stochastic perturbation date: 2020-08-28 pages: extension: .txt txt: ./txt/cord-347906-3ehsg8oi.txt cache: ./cache/cord-347906-3ehsg8oi.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-347906-3ehsg8oi.txt' === file2bib.sh === id: cord-320914-zf54jfol author: Parrish, Rebecca title: A Critical Analysis of the Drivers of Human Migration Patterns in the Presence of Climate Change: A New Conceptual Model date: 2020-08-19 pages: extension: .txt txt: ./txt/cord-320914-zf54jfol.txt cache: ./cache/cord-320914-zf54jfol.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-320914-zf54jfol.txt' === file2bib.sh === id: cord-333490-8pv5x6tz author: Liao, Yi title: Early box office prediction in China’s film market based on a stacking fusion model date: 2020-10-06 pages: extension: .txt txt: ./txt/cord-333490-8pv5x6tz.txt cache: ./cache/cord-333490-8pv5x6tz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-333490-8pv5x6tz.txt' === file2bib.sh === id: cord-342591-6joc2ld1 author: Higazy, M. title: Novel Fractional Order SIDARTHE Mathematical Model of The COVID-19 Pandemic date: 2020-06-13 pages: extension: .txt txt: ./txt/cord-342591-6joc2ld1.txt cache: ./cache/cord-342591-6joc2ld1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-342591-6joc2ld1.txt' === file2bib.sh === id: cord-288183-pz3t29a7 author: McKibbin, Warwick J. title: Chapter 15 A Global Approach to Energy and the Environment The G-Cubed Model date: 2013-12-31 pages: extension: .txt txt: ./txt/cord-288183-pz3t29a7.txt cache: ./cache/cord-288183-pz3t29a7.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-288183-pz3t29a7.txt' === file2bib.sh === id: cord-340354-j3xsp2po author: Noll, N. B. title: COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2 date: 2020-05-07 pages: extension: .txt txt: ./txt/cord-340354-j3xsp2po.txt cache: ./cache/cord-340354-j3xsp2po.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-340354-j3xsp2po.txt' === file2bib.sh === id: cord-319378-li77za5e author: Schroeder, Wheaton L. title: Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis date: 2020-09-11 pages: extension: .txt txt: ./txt/cord-319378-li77za5e.txt cache: ./cache/cord-319378-li77za5e.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-319378-li77za5e.txt' === file2bib.sh === id: cord-342855-dvgqouk2 author: Anzum, R. title: Mathematical Modeling of Coronavirus Reproduction Rate with Policy and Behavioral Effects date: 2020-06-18 pages: extension: .txt txt: ./txt/cord-342855-dvgqouk2.txt cache: ./cache/cord-342855-dvgqouk2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-342855-dvgqouk2.txt' === file2bib.sh === id: cord-340805-qbvgnr4r author: Ioannidis, John P.A. title: Forecasting for COVID-19 has failed date: 2020-08-25 pages: extension: .txt txt: ./txt/cord-340805-qbvgnr4r.txt cache: ./cache/cord-340805-qbvgnr4r.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-340805-qbvgnr4r.txt' === file2bib.sh === id: cord-340244-qjf23a7e author: Bernstein, Daniel J. title: Further analysis of the impact of distancing upon the COVID-19 pandemic date: 2020-04-16 pages: extension: .txt txt: ./txt/cord-340244-qjf23a7e.txt cache: ./cache/cord-340244-qjf23a7e.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-340244-qjf23a7e.txt' === file2bib.sh === id: cord-350603-ssen3q08 author: Albrecht, Randy A. title: Moving Forward: Recent Developments for the Ferret Biomedical Research Model date: 2018-07-17 pages: extension: .txt txt: ./txt/cord-350603-ssen3q08.txt cache: ./cache/cord-350603-ssen3q08.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350603-ssen3q08.txt' === file2bib.sh === id: cord-350510-o4libq5d author: Grinfeld, M. title: On Linear Growth in COVID-19 Cases date: 2020-06-22 pages: extension: .txt txt: ./txt/cord-350510-o4libq5d.txt cache: ./cache/cord-350510-o4libq5d.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350510-o4libq5d.txt' === file2bib.sh === id: cord-331374-3gau0vmc author: Giorgi, Gabriele title: Expatriates’ Multiple Fears, from Terrorism to Working Conditions: Development of a Model date: 2016-10-13 pages: extension: .txt txt: ./txt/cord-331374-3gau0vmc.txt cache: ./cache/cord-331374-3gau0vmc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-331374-3gau0vmc.txt' === file2bib.sh === id: cord-346921-3hfxv6h8 author: Nave, OPhir title: Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition date: 2020-09-21 pages: extension: .txt txt: ./txt/cord-346921-3hfxv6h8.txt cache: ./cache/cord-346921-3hfxv6h8.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-346921-3hfxv6h8.txt' === file2bib.sh === id: cord-331646-j5mkparg author: Sze To, G. N. title: Review and comparison between the Wells–Riley and dose‐response approaches to risk assessment of infectious respiratory diseases date: 2009-07-31 pages: extension: .txt txt: ./txt/cord-331646-j5mkparg.txt cache: ./cache/cord-331646-j5mkparg.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-331646-j5mkparg.txt' === file2bib.sh === id: cord-336687-iw3bzy0m author: Kraemer, M. U. G. title: Big city, small world: density, contact rates, and transmission of dengue across Pakistan date: 2015-10-06 pages: extension: .txt txt: ./txt/cord-336687-iw3bzy0m.txt cache: ./cache/cord-336687-iw3bzy0m.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-336687-iw3bzy0m.txt' === file2bib.sh === id: cord-346265-jx4kspen author: Tatapudi, Hanisha title: Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region date: 2020-10-19 pages: extension: .txt txt: ./txt/cord-346265-jx4kspen.txt cache: ./cache/cord-346265-jx4kspen.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-346265-jx4kspen.txt' === file2bib.sh === id: cord-350240-bmppif8g author: Girardi, Paolo title: Robust inference for nonlinear regression models from the Tsallis score: application to COVID‐19 contagion in Italy date: 2020-08-12 pages: extension: .txt txt: ./txt/cord-350240-bmppif8g.txt cache: ./cache/cord-350240-bmppif8g.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350240-bmppif8g.txt' === file2bib.sh === id: cord-347791-wofyftrs author: Hao, Tian title: Prediction of Coronavirus Disease (covid-19) Evolution in USA with the Model Based on the Eyring Rate Process Theory and Free Volume Concept date: 2020-04-22 pages: extension: .txt txt: ./txt/cord-347791-wofyftrs.txt cache: ./cache/cord-347791-wofyftrs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-347791-wofyftrs.txt' === file2bib.sh === id: cord-335689-8a704p38 author: Martin, Gerardo title: Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk date: 2018-03-19 pages: extension: .txt txt: ./txt/cord-335689-8a704p38.txt cache: ./cache/cord-335689-8a704p38.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-335689-8a704p38.txt' === file2bib.sh === id: cord-343701-x5rghsbs author: Zhao, Yu-Feng title: Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models date: 2020-06-25 pages: extension: .txt txt: ./txt/cord-343701-x5rghsbs.txt cache: ./cache/cord-343701-x5rghsbs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-343701-x5rghsbs.txt' === file2bib.sh === id: cord-340713-v5sdowb7 author: Bird, Jordan J. title: Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach date: 2020-10-28 pages: extension: .txt txt: ./txt/cord-340713-v5sdowb7.txt cache: ./cache/cord-340713-v5sdowb7.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-340713-v5sdowb7.txt' === file2bib.sh === id: cord-338466-7uvta990 author: Singh, Brijesh P. title: Modeling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlook date: 2020-10-31 pages: extension: .txt txt: ./txt/cord-338466-7uvta990.txt cache: ./cache/cord-338466-7uvta990.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-338466-7uvta990.txt' === file2bib.sh === id: cord-329276-tfrjw743 author: Ledzewicz, Urszula title: On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies date: 2020-09-30 pages: extension: .txt txt: ./txt/cord-329276-tfrjw743.txt cache: ./cache/cord-329276-tfrjw743.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-329276-tfrjw743.txt' === file2bib.sh === id: cord-320953-1st77mvh author: Overton, ChristopherE. title: Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example date: 2020-07-04 pages: extension: .txt txt: ./txt/cord-320953-1st77mvh.txt cache: ./cache/cord-320953-1st77mvh.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-320953-1st77mvh.txt' === file2bib.sh === id: cord-351411-q9kqjvvf author: Moghadas, Seyed M title: Improving public health policy through infection transmission modelling: Guidelines for creating a Community of Practice date: 2015 pages: extension: .txt txt: ./txt/cord-351411-q9kqjvvf.txt cache: ./cache/cord-351411-q9kqjvvf.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-351411-q9kqjvvf.txt' === file2bib.sh === id: cord-333919-nrd9ajj2 author: Albi, G. title: Relaxing lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty date: 2020-05-16 pages: extension: .txt txt: ./txt/cord-333919-nrd9ajj2.txt cache: ./cache/cord-333919-nrd9ajj2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-333919-nrd9ajj2.txt' === file2bib.sh === id: cord-309301-ai84el0j author: Li, Yaqi title: Organoid based personalized medicine: from bench to bedside date: 2020-11-02 pages: extension: .txt txt: ./txt/cord-309301-ai84el0j.txt cache: ./cache/cord-309301-ai84el0j.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-309301-ai84el0j.txt' === file2bib.sh === id: cord-352348-2wtyk3r5 author: Sabroe, Ian title: Identifying and hurdling obstacles to translational research date: 2007 pages: extension: .txt txt: ./txt/cord-352348-2wtyk3r5.txt cache: ./cache/cord-352348-2wtyk3r5.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-352348-2wtyk3r5.txt' === file2bib.sh === id: cord-355102-jcyq8qve author: Avila, Eduardo title: Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios date: 2020-06-29 pages: extension: .txt txt: ./txt/cord-355102-jcyq8qve.txt cache: ./cache/cord-355102-jcyq8qve.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-355102-jcyq8qve.txt' === file2bib.sh === id: cord-336747-8m7n5r85 author: Grossmann, G. title: Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study date: 2020-05-08 pages: extension: .txt txt: ./txt/cord-336747-8m7n5r85.txt cache: ./cache/cord-336747-8m7n5r85.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-336747-8m7n5r85.txt' === file2bib.sh === id: cord-337897-hkvll3xh author: Yang, Zheng Rong title: Peptide Bioinformatics- Peptide Classification Using Peptide Machines date: 2009 pages: extension: .txt txt: ./txt/cord-337897-hkvll3xh.txt cache: ./cache/cord-337897-hkvll3xh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-337897-hkvll3xh.txt' === file2bib.sh === id: cord-350001-pd2bnqbp author: Liu, L. title: Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility date: 2020-08-07 pages: extension: .txt txt: ./txt/cord-350001-pd2bnqbp.txt cache: ./cache/cord-350001-pd2bnqbp.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350001-pd2bnqbp.txt' === file2bib.sh === id: cord-350870-a89zj5mh author: Ikeda, Hiroki title: Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model date: 2014-05-21 pages: extension: .txt txt: ./txt/cord-350870-a89zj5mh.txt cache: ./cache/cord-350870-a89zj5mh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350870-a89zj5mh.txt' === file2bib.sh === id: cord-344252-6g3zzj0o author: Farooq, Junaid title: A Novel Adaptive Deep Learning Model of Covid-19 with focus on mortality reduction strategies date: 2020-07-21 pages: extension: .txt txt: ./txt/cord-344252-6g3zzj0o.txt cache: ./cache/cord-344252-6g3zzj0o.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-344252-6g3zzj0o.txt' === file2bib.sh === id: cord-353200-5csewb1k author: Jehi, Lara title: Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19 date: 2020-08-11 pages: extension: .txt txt: ./txt/cord-353200-5csewb1k.txt cache: ./cache/cord-353200-5csewb1k.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-353200-5csewb1k.txt' === file2bib.sh === id: cord-354627-y07w2f43 author: pinter, g. title: COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach date: 2020-05-06 pages: extension: .txt txt: ./txt/cord-354627-y07w2f43.txt cache: ./cache/cord-354627-y07w2f43.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-354627-y07w2f43.txt' === file2bib.sh === id: cord-352543-8il0dh58 author: Kuzdeuov, A. title: A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies date: 2020-05-06 pages: extension: .txt txt: ./txt/cord-352543-8il0dh58.txt cache: ./cache/cord-352543-8il0dh58.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-352543-8il0dh58.txt' === file2bib.sh === id: cord-344417-1seb8b09 author: Wang, Yuhang title: SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection date: 2020-10-03 pages: extension: .txt txt: ./txt/cord-344417-1seb8b09.txt cache: ./cache/cord-344417-1seb8b09.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-344417-1seb8b09.txt' === file2bib.sh === id: cord-339374-2hxnez28 author: De Kort, Hanne title: Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors date: 2020-09-22 pages: extension: .txt txt: ./txt/cord-339374-2hxnez28.txt cache: ./cache/cord-339374-2hxnez28.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-339374-2hxnez28.txt' === file2bib.sh === id: cord-346309-hveuq2x9 author: Reis, Ben Y title: An Epidemiological Network Model for Disease Outbreak Detection date: 2007-06-26 pages: extension: .txt txt: ./txt/cord-346309-hveuq2x9.txt cache: ./cache/cord-346309-hveuq2x9.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-346309-hveuq2x9.txt' === file2bib.sh === id: cord-347199-slq70aou author: Safta, Cosmin title: Characterization of partially observed epidemics through Bayesian inference: application to COVID-19 date: 2020-10-07 pages: extension: .txt txt: ./txt/cord-347199-slq70aou.txt cache: ./cache/cord-347199-slq70aou.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-347199-slq70aou.txt' === file2bib.sh === id: cord-346136-sqc09x9c author: Hamilton, Kyra title: Application of the Health Action Process Approach to Social Distancing Behavior During COVID‐19 date: 2020-10-02 pages: extension: .txt txt: ./txt/cord-346136-sqc09x9c.txt cache: ./cache/cord-346136-sqc09x9c.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-346136-sqc09x9c.txt' === file2bib.sh === id: cord-354254-89vjfkfd author: Peng, Shanbi title: The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control date: 2020-08-31 pages: extension: .txt txt: ./txt/cord-354254-89vjfkfd.txt cache: ./cache/cord-354254-89vjfkfd.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-354254-89vjfkfd.txt' === file2bib.sh === id: cord-352431-yu7kxnab author: Langbeheim, Elon title: Science Teachers’ Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module date: 2020-08-25 pages: extension: .txt txt: ./txt/cord-352431-yu7kxnab.txt cache: ./cache/cord-352431-yu7kxnab.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-352431-yu7kxnab.txt' === file2bib.sh === id: cord-347952-k95wrory author: Prieto, Diana M title: A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels date: 2012-03-30 pages: extension: .txt txt: ./txt/cord-347952-k95wrory.txt cache: ./cache/cord-347952-k95wrory.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-347952-k95wrory.txt' === file2bib.sh === id: cord-340827-vx37vlkf author: Jackson, Matthew O. title: Chapter 14 Diffusion, Strategic Interaction, and Social Structure date: 2011-12-31 pages: extension: .txt txt: ./txt/cord-340827-vx37vlkf.txt cache: ./cache/cord-340827-vx37vlkf.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-340827-vx37vlkf.txt' === file2bib.sh === id: cord-348010-m3a3utvz author: Wolff, Michael title: On build‐up of epidemiologic models—Development of a SEI(3)RSD model for the spread of SARS‐CoV‐2 date: 2020-10-13 pages: extension: .txt txt: ./txt/cord-348010-m3a3utvz.txt cache: ./cache/cord-348010-m3a3utvz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-348010-m3a3utvz.txt' === file2bib.sh === id: cord-319933-yp9ofhi8 author: Ruiz, Sara I. title: Chapter 38 Animal Models of Human Viral Diseases date: 2013-12-31 pages: extension: .txt txt: ./txt/cord-319933-yp9ofhi8.txt cache: ./cache/cord-319933-yp9ofhi8.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-319933-yp9ofhi8.txt' === file2bib.sh === id: cord-264408-vk4lt83x author: Ruiz, Sara I. title: Animal Models of Human Viral Diseases date: 2017-06-23 pages: extension: .txt txt: ./txt/cord-264408-vk4lt83x.txt cache: ./cache/cord-264408-vk4lt83x.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-264408-vk4lt83x.txt' === file2bib.sh === id: cord-015147-h0o0yqv8 author: nan title: Oral Communications and Posters date: 2014-09-12 pages: extension: .txt txt: ./txt/cord-015147-h0o0yqv8.txt cache: ./cache/cord-015147-h0o0yqv8.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 6 resourceName b'cord-015147-h0o0yqv8.txt' === file2bib.sh === id: cord-004584-bcw90f5b author: nan title: Abstracts: 8th EBSA European Biophysics Congress, August 23rd–27th 2011, Budapest, Hungary date: 2011-08-06 pages: extension: .txt txt: ./txt/cord-004584-bcw90f5b.txt cache: ./cache/cord-004584-bcw90f5b.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 11 resourceName b'cord-004584-bcw90f5b.txt' === file2bib.sh === id: cord-006229-7yoilsho author: nan title: Abstracts of the 82(nd) Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) and the 18(th) Annual Meeting of the Network Clinical Pharmacology Germany (VKliPha) in cooperation with the Arbeitsgemeinschaft für Angewandte Humanpharmakologie e.V. (AGAH) date: 2016-02-06 pages: extension: .txt txt: ./txt/cord-006229-7yoilsho.txt cache: ./cache/cord-006229-7yoilsho.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 13 resourceName b'cord-006229-7yoilsho.txt' Que is empty; done keyword-model-cord === reduce.pl bib === id = cord-000332-u3f89kvg author = Broeck, Wouter Van den title = The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale date = 2011-02-02 pages = extension = .txt mime = text/plain words = 7455 sentences = 337 flesch = 41 summary = The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. GLEaMviz is a client-server software system that can model the world-wide spread of epidemics for human transmissible diseases like influenzalike illnesses (ILI), offering extensive flexibility in the design of the compartmental model and scenario setup, including computationally-optimized numerical simulations based on high-resolution global demographic and mobility data. GLEaMviz makes use of a stochastic and discrete computational scheme to model epidemic spread called "GLEaM" -GLobal Epidemic and Mobility model, presented in previously published work [6, 3, 14] which is based on a geo-referenced metapopulation approach that considers 3,362 subpopulations in 220 countries of the world, as well as air travel flow connections and short-range commuting data. cache = ./cache/cord-000332-u3f89kvg.txt txt = ./txt/cord-000332-u3f89kvg.txt === reduce.pl bib === === reduce.pl bib === id = cord-003377-9vkhptas author = Wu, Tong title = The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia date = 2018-12-19 pages = extension = .txt mime = text/plain words = 4969 sentences = 267 flesch = 49 summary = title: The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia We focus on the role played by the live poultry trade in the spread of H5N1 across three regions widely infected by the disease, which also correspond to three major trade blocs: the European Union (EU), the Economic Community of West African States (ECOWAS), and the Association of Southeast Asian Nations (ASEAN). The indicator for wild bird habitat used in this study was the set of "Important Bird and Biodiversity Areas" (IBAs) for "migratory and congregatory waterbirds" identified by BirdLife The live poultry trade poses different avian influenza risks in different regions of the world Table 1 . Our first specification (Model 1) included a number of factors related to disease risk but excluded both live poultry imports and biosecurity measures. cache = ./cache/cord-003377-9vkhptas.txt txt = ./txt/cord-003377-9vkhptas.txt === reduce.pl bib === id = cord-003243-u744apzw author = Michael, Edwin title = Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination date = 2018-10-08 pages = extension = .txt mime = text/plain words = 10321 sentences = 336 flesch = 33 summary = METHODOLOGY AND PRINCIPAL FINDINGS: We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYM-FASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. cache = ./cache/cord-003243-u744apzw.txt txt = ./txt/cord-003243-u744apzw.txt === reduce.pl bib === id = cord-007129-qjdg46o9 author = Simoes, Joana Margarida title = Spatial Epidemic Modelling in Social Networks date = 2005-06-21 pages = extension = .txt mime = text/plain words = 1796 sentences = 94 flesch = 51 summary = In this model, it was considered the social mobility network: the daily movement of individuals, which has been already referred in the literature as a complex network with a Small World behaviour [2] . In this paper, it is described a simulation system using artificial agents integrated with Geographical Information Systems (GIS) that helps to understand the spatial and temporal behaviour of a epidemic phenomena. The present model is inspired by a Site Exchange Cellular Automata [5] , which considers two phases for each time step: movement and infection, assuming there is no virus transmission while the individual is moving. Based on these regions, four ranges of movement were considered: neighbourhood, intra region, inter region and small world. The Small World movement simulation (Fig. 15 ) presents a totally different distribution of population. The movement should always be considered in human epidemics models. cache = ./cache/cord-007129-qjdg46o9.txt txt = ./txt/cord-007129-qjdg46o9.txt === reduce.pl bib === id = cord-011400-zyjd9rmp author = Peixoto, Tiago P. title = Network Reconstruction and Community Detection from Dynamics date = 2019-09-18 pages = extension = .txt mime = text/plain words = 3327 sentences = 191 flesch = 50 summary = Researchers have approached this reconstruction task from a variety of angles, resulting in many different methods, including thresholding the correlation between time series [6] , inversion of deterministic dynamics [7] [8] [9] , statistical inference of graphical models [10] [11] [12] [13] [14] and of models of epidemic spreading [15] [16] [17] [18] [19] [20] , as well as approaches that avoid explicit modeling, such as those based on transfer entropy [21] , Granger causality [22] , compressed sensing [23] [24] [25] , generalized linearization [26] , and matching of pairwise correlations [27, 28] . [32] proposed a method to infer community structure from time-series data that bypasses network reconstruction by employing a direct modeling of the dynamics given the group assignments, instead. We take two empirical networks, the with E ¼ 39430 edges, and a food web from Little Rock Lake [46] , containing N ¼ 183 nodes and E ¼ 2434 edges, and we sample from the SIS (mimicking the spread of a pandemic) and Ising model (representing simplified interspecies interactions) on them, respectively, and evaluate the reconstruction obtained via the joint and separate inference with community detection, with results shown in Fig. 2 . cache = ./cache/cord-011400-zyjd9rmp.txt txt = ./txt/cord-011400-zyjd9rmp.txt === reduce.pl bib === id = cord-005321-b3pyg5b3 author = Cai, Li-Ming title = Global analysis of an epidemic model with vaccination date = 2017-07-21 pages = extension = .txt mime = text/plain words = 5605 sentences = 412 flesch = 61 summary = In some of these studies (e.g., papers [16, 31, 43] ), authors have shown that the dynamics of the model are determined by the disease's basic reproduction number 0 . In order to derive the equations of the mathematical model, we divide the total population N in a community into five compartments: susceptible, exposed (not yet infectious), infective, recovered, and vaccinated; the numbers in these states are denoted by S(t), If ψ = 0 and limit γ 1 → ∞, system (2.1) will be reduced into an SIV epidemic model in [36] , where authors investigate the effect of imperfect vaccines on the disease's transmission dynamics. This is also in line with results in paper [26] , where the vaccination-free model (2.3) has a globally asymptotically stable equilibrium if the basic reproduction number R 0 is less than one. Global results for an epidemic model with vaccination that exhibits backward bifurcation cache = ./cache/cord-005321-b3pyg5b3.txt txt = ./txt/cord-005321-b3pyg5b3.txt === reduce.pl bib === id = cord-007147-0v8ltunv author = Dungan, R. S. title = BOARD-INVITED REVIEW: Fate and transport of bioaerosols associated with livestock operations and manures date = 2010-11-17 pages = extension = .txt mime = text/plain words = 8223 sentences = 399 flesch = 39 summary = Although most studies at animal operations and wastewater spray irrigation sites suggest a decreased risk of bioaerosol exposure with increasing distance from the source, many challenges remain in evaluating the health effects of aerosolized pathogens and allergens in outdoor environments. An area of growing interest is airborne pathogens and microbial by-products generated at AFO and during the land application of manures (Chang et al., 2001b; Wilson et al., 2002; Cole et al., 2008; Chinivasagam et al., 2009; Dungan and Leytem, 2009a; Millner, 2009) , which can potentially affect the health of livestock, farm workers, and individuals in nearby residences (Heederik et al., 2007) . With most bioaerosol studies, whether conducted at AFO, composting facilities, wastewater treatment plants, biosolids application sites, or wastewater spray irrigations sites, the general trend observed is that the airborne microorganism concentrations decrease with distance from the source (Goff et al., 1973; Katzenelson and Teltch, 1976; Boutin et al., 1988; Taha et al., 2005; Green et al., 2006; Low et al., 2007) . cache = ./cache/cord-007147-0v8ltunv.txt txt = ./txt/cord-007147-0v8ltunv.txt === reduce.pl bib === id = cord-004332-99lxmq4u author = Zhao, Shi title = Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall date = 2020-01-10 pages = extension = .txt mime = text/plain words = 4195 sentences = 218 flesch = 53 summary = title: Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall This work aims to study the epidemiological features of epidemics in different Nigerian regions and quantify the association between reproduction number (R) and state rainfall. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points (τ) in different regions at different time periods. As illustrated in Figure 3 , the reproduction numbers, R j s, are estimated for different epidemics from the selected growth models. To quantify the impacts of state rainfall, we calculate the percentage changing rate with different cumulative lags (t) from 4 to 9 months and estimate their significant levels. The estimated changing rate in R under a one-unit (mm) increase in the average monthly rainfall is summarised with different cumulative lag terms from 4 to 9 months (the t in Eqn (3)). cache = ./cache/cord-004332-99lxmq4u.txt txt = ./txt/cord-004332-99lxmq4u.txt === reduce.pl bib === id = cord-018791-h3bfdr14 author = Rasulev, Bakhtiyor title = Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures date = 2016-12-09 pages = extension = .txt mime = text/plain words = 10404 sentences = 526 flesch = 45 summary = In short, QSAR is a method to find correlations and mathematical models for congeneric series of compounds, affinities of ligands to their binding sites, rate constants, inhibition constants, toxicological effect, and many other biological activities, based on structural features, as well as group and molecular properties, such as electronic properties, polarizability, or steric properties (Klebe et al. Later authors improved this approach and by combining the two existing techniques, GRID and PLS, has developed a powerful 3D QSAR methodology, so-called comparative molecular field analysis (CoMFA) (Cramer et al. The main advantage of this combined approach of 3D QSAR and pharmacophore-based docking is to focus on specific key interaction for protein-ligand binding to improve drug candidates. Another group published in 2013 a study that conducted a comprehensive investigation of fullerene analogues by combined computational approach including quantum chemical, molecular docking, and 3D descriptors-based QSAR (Ahmed et al. cache = ./cache/cord-018791-h3bfdr14.txt txt = ./txt/cord-018791-h3bfdr14.txt === reduce.pl bib === id = cord-000282-phepjf55 author = Hsieh, Ying-Hen title = On epidemic modeling in real time: An application to the 2009 Novel A (H1N1) influenza outbreak in Canada date = 2010-11-05 pages = extension = .txt mime = text/plain words = 4027 sentences = 166 flesch = 48 summary = BACKGROUND: Management of emerging infectious diseases such as the 2009 influenza pandemic A (H1N1) poses great challenges for real-time mathematical modeling of disease transmission due to limited information on disease natural history and epidemiology, stochastic variation in the course of epidemics, and changing case definitions and surveillance practices. We sought to address three critical issues in real time disease modeling for newly emerged 2009 pH1N1: (i) to estimate the basic reproduction number; (ii) to identify the main turning points in the epidemic curve that distinguish different phases or waves of disease; and (iii) to predict the future course of events, including the final size of the outbreak in the absence of intervention. We fit both the single-and multi-phase Richards models to Canadian cumulative 2009 pH1N1 cumulative case data, using publicly available disease onset dates obtained from the Public Health Agency of Canada (PHAC) website [10, 11] . cache = ./cache/cord-000282-phepjf55.txt txt = ./txt/cord-000282-phepjf55.txt === reduce.pl bib === id = cord-005033-voi9gu0l author = Xuan, Huiyu title = A CA-based epidemic model for HIV/AIDS transmission with heterogeneity date = 2008-06-07 pages = extension = .txt mime = text/plain words = 6567 sentences = 395 flesch = 57 summary = In this paper, we develop an extended CA simulation model to study the dynamical behaviors of HIV/AIDS transmission. Additional, we divide the post-infection process of AIDS disease into several sub-stages in order to facilitate the study of the dynamics in different development stages of epidemics. Higher population density, higher mobility, higher number of infection source, and greater neighborhood are more likely to result in high levels of infections and in persistence. Ahmed and Agiza (1998) develop a CA model that takes into consideration the latency and incubation period of epidemics and allow each individual (agent) to have distinctive susceptibility. We also define four types of agents that are characterized by different infectivity (and susceptibility) and various forms of neighborhood to represent four types of people in real life. To capture this, we extend classical CA models by allowing each agent to have its own attributes such as mobility, infectivity, resistibility (susceptibility) 2 and different extent of neighborhood. cache = ./cache/cord-005033-voi9gu0l.txt txt = ./txt/cord-005033-voi9gu0l.txt === reduce.pl bib === id = cord-018746-s9knxdne author = Perra, Nicola title = Modeling and Predicting Human Infectious Diseases date = 2015-04-23 pages = extension = .txt mime = text/plain words = 9708 sentences = 543 flesch = 53 summary = Building on these concepts we present two realistic data-driven epidemiological models able to forecast the spreading of infectious diseases at different geographical granularities. The unprecedented amount of data on human dynamics made available by recent advances technology has allowed the development of realistic epidemic models able to capture and predict the unfolding of infectious disease at different geographical scales [59] . The new approach allows for the early detection of disease outbreaks [62] , the real time monitoring of the evolution of a disease with an incredible geographical granularity [63] [64] [65] , the access to health related behaviors, practices and sentiments at large scales [66, 67] , inform data-driven epidemic models [68, 69] , and development of statistical based models with prediction power [67, [70] [71] [72] [73] [74] [75] [76] [77] [78] . cache = ./cache/cord-018746-s9knxdne.txt txt = ./txt/cord-018746-s9knxdne.txt === reduce.pl bib === id = cord-015255-1qhgeirb author = Busby, J S title = Managing the social amplification of risk: a simulation of interacting actors date = 2012-07-11 pages = extension = .txt mime = text/plain words = 9934 sentences = 404 flesch = 45 summary = Such cases are therefore an important and promising setting for exploring the idea that amplification is only in the heads of social actors, and for exploring the notion that this might nonetheless produce observable, and potentially highly consequential, outcomes in a way that risk managers need to understand. In the remainder of this article we therefore explore the consequences of the idea that social risk amplification is nothing more than an attribution, or judgment that one social actor makes of another, and try to see what implications this might have for risk managers based on a systems dynamics model. Therefore in the second model, shown in Figure 2 , we now have a subsystem in which a risk manager (a government agency or an industrial undertaking in the case of zoonotic disease outbreaks) observes the public risk perception in relation to the expert risk assessment, and communicates a risk level that is designed to compensate for any discrepancy between the two. cache = ./cache/cord-015255-1qhgeirb.txt txt = ./txt/cord-015255-1qhgeirb.txt === reduce.pl bib === id = cord-004157-osol7wdp author = Ma, Junling title = Estimating epidemic exponential growth rate and basic reproduction number date = 2020-01-08 pages = extension = .txt mime = text/plain words = 5474 sentences = 393 flesch = 60 summary = Typically, for an epidemic model that contains a single transmission rate b, if all other parameters can be estimated independently to the exponential growth rate l, then l determines b, and thus determines R 0 . Wallinga and Lipsitch (Wallinga & Lipsitch, 2006 ) developed a non-parametric method to infer the basic reproduction number from the exponential growth rate without assuming a model. Let cðtÞdt be the number of new infections during the time interval ½t;t þ dt, that is, cðtÞ is the incidence rate, and SðtÞ be the average susceptibility of the population, i.e., the expected susceptibility of a randomly selected individual. Equation (5) links the exponential growth rate to the basic reproduction number though the serial interval distribution only. That is, if we can estimate the serial interval distribution and the exponential growth rate independently, that we can infer the basic reproduction number. Note that the serial interval distribution wðtÞ can be estimated independently to the exponential growth rate. cache = ./cache/cord-004157-osol7wdp.txt txt = ./txt/cord-004157-osol7wdp.txt === reduce.pl bib === id = cord-012866-p3mb7r0v author = Luo, Yan title = Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis date = 2020-06-12 pages = extension = .txt mime = text/plain words = 5247 sentences = 264 flesch = 45 summary = title: Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). DISCUSSION: This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. Individual participant data meta-analysis (IPD-MA) has been previously employed to develop prediction models for treatment effects [3] [4] [5] [6] . In the second stage, this baseline risk score will be used as a prognostic factor and an effect modifier in an IPD meta-regression model to estimate the individualized treatment effects of CTZ. cache = ./cache/cord-012866-p3mb7r0v.txt txt = ./txt/cord-012866-p3mb7r0v.txt === reduce.pl bib === id = cord-020193-3oqkdbq0 author = Bley, Katja title = Overcoming the Ivory Tower: A Meta Model for Staged Maturity Models date = 2020-03-06 pages = extension = .txt mime = text/plain words = 4734 sentences = 243 flesch = 47 summary = We introduce this meta model regarding the different MM concepts, where each MM can be an instance of it as it provides a conceptual template for the rigorous development of new and the evaluation of existing maturity models. Based on a Summarizing, many approaches can support researchers in creating MMs. However, these guidelines are limited in their interpretability and validity, as they do not provide concrete terminology specifications or structural concept models. The development of the Meta Model for Maturity Models (4M) was based on a study of the most common and representative staged MMs. In order to elaborate sufficient meta model elements that are valid for a broad class of staged MMs, an analysis of different staged MMs, their development and their structure was conducted to summarize and analyze existing concepts, their relationships as well as their multiplicities and instantiations. cache = ./cache/cord-020193-3oqkdbq0.txt txt = ./txt/cord-020193-3oqkdbq0.txt === reduce.pl bib === id = cord-016954-l3b6n7ej author = Young, Colin R. title = Animal Models of Multiple Sclerosis date = 2008 pages = extension = .txt mime = text/plain words = 9705 sentences = 495 flesch = 44 summary = The relative inaccessibility and sensitivity of the central nervous system (CNS) in humans preclude studies on disease pathogenesis, and so much of our understanding of infections and immune responses has been derived from experimental animal models. Viral models are immensely relevant since epidemiological studies suggest an environmental factor, and almost all naturally occurring CNS demyelinating diseases of humans and animals of known etiology are caused by a virus. The most widely studied models of MS are the experimental infections of rodents resulting in an inflammatory demyelinating disease in the CNS, such as Theiler's virus, mouse hepatitis virus, and Semliki Forest virus. Theiler's virus-induced demyelination, a model for human MS, bears several similarities to the human disease: an immune-mediated demyelination, involvement of CD4 + helper T cells and CD8 + cytotoxic T cells, delayed type hypersensitivity responses to viral antigens and autoantigens, and pathology. cache = ./cache/cord-016954-l3b6n7ej.txt txt = ./txt/cord-016954-l3b6n7ej.txt === reduce.pl bib === id = cord-010903-kuwy7pbo author = Liu, Jiajun title = Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime date = 2020-03-05 pages = extension = .txt mime = text/plain words = 3735 sentences = 205 flesch = 41 summary = This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. The purpose of this study was to: (1) develop and evaluate a unified cefepime population PK model for adult and pediatric patients, and (2) construct an individualized model that can be utilized to deliver precision cefepime dosing. A unified cefepime population pharmacokinetic model has been developed from adult and pediatric patients and evaluates well in independent populations. The base one-and two-compartment models (without covariate adjustment) produced reasonable fits for observed and Bayesian posterior-predicted cefepime concentrations (R 2 = 84.7% and 85.2%, respectively), but population estimates were unsatisfactory (R 2 = 22.7% and 27.8%, respectively) ( Table 1) . This study created a population and individual PK model for adult and pediatric patients and can serve as a Bayesian prior for precision dosing. cache = ./cache/cord-010903-kuwy7pbo.txt txt = ./txt/cord-010903-kuwy7pbo.txt === reduce.pl bib === id = cord-002169-7kwlteyr author = Wu, Nicholas C title = Adaptation in protein fitness landscapes is facilitated by indirect paths date = 2016-07-08 pages = extension = .txt mime = text/plain words = 9303 sentences = 504 flesch = 54 summary = Previous empirical studies on combinatorially complete fitness landscapes have been limited to subgraphs of the sequence space consisting of only two amino acids at each site (2 L genotypes) (Weinreich et al., 2006; Lunzer et al., 2005; O'Maille et al., 2008; Lozovsky et al., 2009; Franke et al., 2011; Tan et al., 2011) . Our findings support the view that direct paths of protein adaptation are often constrained by pairwise epistasis on a rugged fitness landscape (Weinreich et al., 2005; Kondrashov and Kondrashov, 2015) . With our experimental data, we observed two distinct mechanisms of bypass, either using an extra amino acid at the same site or using an additional site, that allow proteins to continue adaptation when no direct paths were accessible due to reciprocal sign epistasis ( Figure 2 ). Our results suggest that higher-order epistasis can either increase or decrease the ruggedness induced by pairwise epistasis, which in turn determines the accessibility of direct paths in a rugged fitness landscape (Figure 3-figure supplement 6). cache = ./cache/cord-002169-7kwlteyr.txt txt = ./txt/cord-002169-7kwlteyr.txt === reduce.pl bib === id = cord-020764-5tq9cr7o author = Vertrees, Roger A. title = Tissue Culture Models date = 2010-05-21 pages = extension = .txt mime = text/plain words = 11293 sentences = 580 flesch = 39 summary = Scientists have developed diverse and unique tissue culture systems that contain air-liquid barriers of lung epithelium and subjected these cells to various gaseous toxicants to determine what occurs following inhalation of various chemicals. In addition to the characterization of responses to inhaled agents, epithelial cell cultures, notably alveolar epithelium obtained from fetal lung tissue, have allowed investigators to characterize the liquid transport phenotype that occurs in the developing lung. Primary cell cultures of human airway smooth muscle tissue can be obtained utilizing a method described by Halayko et al. Additionally, if investigators do not wish to use currently established lung cancer cell lines, obtaining clinical samples for use in tissue culture models is relatively easy. This model is composed of a coculture of in vitro threedimensional human bronchoepithelial TLAs engineered using a rotating-wall vessel to mimic the characteristics of in vivo tissue and to provide a tool to study human respiratory viruses and host-pathogen cell interactions. cache = ./cache/cord-020764-5tq9cr7o.txt txt = ./txt/cord-020764-5tq9cr7o.txt === reduce.pl bib === id = cord-017934-3wyebaxb author = Kurahashi, Setsuya title = An Agent-Based Infectious Disease Model of Rubella Outbreaks date = 2019-05-07 pages = extension = .txt mime = text/plain words = 3239 sentences = 195 flesch = 57 summary = We aim to study the relationship between antibody holding rate of men and the spread of infection by constructing infection of rubella virus with the agent-based model and repeating simulation experiment on a computer. Although our previous study described the infectious disease model of smallpox and Ebola [6] , this paper proposes a new model of rubella which has caused crucial problems for pregnant women in recent years. As results of experiments showed that (1) in a base model in which any infectious disease measures were not taken, the epidemic spread within 82 days and 30% of people died, (2) a trace vaccination measure was effective but it was difficult to trace all contacts to patients in an underground railway or an airport, (3) a mass vaccination measure was effective, but the number of vaccinations would be huge so it was not realistic and (4) epidemic quenching was also effective, and reactive household trace vaccination along with pre-emptive vaccination of hospital workers showed a dramatic effect. cache = ./cache/cord-017934-3wyebaxb.txt txt = ./txt/cord-017934-3wyebaxb.txt === reduce.pl bib === id = cord-017003-3farxcc3 author = Koibuchi, Yukio title = Numerical Simulation of Urban Coastal Zones date = 2010 pages = extension = .txt mime = text/plain words = 7577 sentences = 476 flesch = 53 summary = Such a mixing process continues until the river water reaches the same density as the surrounding sea water, resulting in vertical circulation in the bays that is is several to ten times greater than the river flux (Unoki 1998) . The ecosystem model introduced here was developed to simulate the nutrient budget of an urban coastal zone. To quantify the nutrients budget, we applied our numerical model to Tokyo Bay. The computational domain was divided into 1km horizontal grids with 20 vertical layers. Fig. 3-13 shows the calculation results of an annual budget of nitrogen and phosphorus in Tokyo Bay. The annual budget is useful in understanding nutrient cycles. We have developed a water quality model to simulate both nutrient cycles and pathogens distributions, and coupled it with a three-dimensional hydrodynamic model of urban coastal areas. We applied this model to the Tokyo Bay and simulated water column temperatures, salinity, and nutrient concentrations that were closely linked with field observations. cache = ./cache/cord-017003-3farxcc3.txt txt = ./txt/cord-017003-3farxcc3.txt === reduce.pl bib === id = cord-001921-73esrper author = Lin, Cheng-Yung title = Zebrafish and Medaka: new model organisms for modern biomedical research date = 2016-01-28 pages = extension = .txt mime = text/plain words = 7725 sentences = 417 flesch = 42 summary = Studies on gene expression patterns, regulatory cis-elements identification, and gene functions can be facilitated by using zebrafish embryos via a number of techniques, including transgenesis, in vivo transient assay, overexpression by injection of mRNAs, knockdown by injection of morpholino oligonucleotides, knockout and gene editing by CRISPR/Cas9 system and mutagenesis. In addition, transgenic lines of model fish harboring a tissue-specific reporter have become a powerful tool for the study of biological sciences, since it is possible to visualize the dynamic expression of a specific gene in the transparent embryos. generated a transgenic zebrafish line huORFZ, which harbors the upstream open reading frame (uORF) from human CCAAT/enhancer-binding protein homologous protein gene (chop), fused with the GFP reporter and driven by a cytomegalovirus promoter [54] . For example, the Tsai's lab established a transgenic line which could be induced to knock down the expression level of cardiac troponin C at any developmental stage, including embryos, larva or adult fish. cache = ./cache/cord-001921-73esrper.txt txt = ./txt/cord-001921-73esrper.txt === reduce.pl bib === id = cord-020683-5s3lghj6 author = Buonomo, Bruno title = Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model date = 2020-04-09 pages = extension = .txt mime = text/plain words = 4675 sentences = 267 flesch = 54 summary = The model has the basic structure of SIRI compartments (susceptible–infectious–recovered–infectious) and is implemented by taking into account of the behavioral changes of individuals in response to the available information on the status of the disease in the community. Therefore, it becomes an intriguing problem to qualitatively assess how the administration of a vaccine could affect the outbreak, taking into account of the behavioral changes of individuals in response to the information available on the status of the disease in the community. Since the disease of our interest has both reinfection and partial immunity after infection, we first consider the SIRI model, which is given by the following nonlinear ordinary differential equations (the upper dot denotes the time derivative) [18] : In the next section we will modify the SIRI model (4) to assess how an hypothetical vaccine could control the outbreak, taking into account of the behavioral changes of individuals produced by the information available on the status of the disease in the community. cache = ./cache/cord-020683-5s3lghj6.txt txt = ./txt/cord-020683-5s3lghj6.txt === reduce.pl bib === id = cord-017595-v3rllyyu author = Puzyn, Tomasz title = Nanomaterials – the Next Great Challenge for Qsar Modelers date = 2009-06-25 pages = extension = .txt mime = text/plain words = 9662 sentences = 570 flesch = 46 summary = However, from the physico-chemical viewpoint, the novel properties of nanoparticles can also be determined by their chemical composition, surface structure, solubility, shape, ratio of particles in relation to agglomerates, and surface area to volume ratio. Analyzing the literature data (Section 14.3) it must be concluded that even if a class of structurally similar nanoparticles is tested with the same laboratory protocol, the number of tested compounds is often insufficient to perform comprehensive internal and external validation of a model and to calculate the appropriate measures of robustness and predictivity in QSAR. [81] have developed two models defining the relationships between basic physico-chemical properties (namely, water solubility, log S, and n-octanol/water partition coefficient, log P) of carbon nanotubes and their chiral vectors (as structural descriptors). Although we strongly believe in the usefulness and appropriateness of QSAR methodology for nanomaterial studies, the number of available models related to activity and toxicity is still very limited. cache = ./cache/cord-017595-v3rllyyu.txt txt = ./txt/cord-017595-v3rllyyu.txt === reduce.pl bib === id = cord-017423-cxua1o5t author = Wang, Rui title = A Review of Microblogging Marketing Based on the Complex Network Theory date = 2011-11-12 pages = extension = .txt mime = text/plain words = 2682 sentences = 121 flesch = 36 summary = Microblogging marketing which is based on the online social network with both small-world and scale-free properties can be explained by the complex network theory. In brief, the complex network theory pioneered by the small-world and scalefree network model overcomes the constraints of the network size and structure of regular network and random network, describes the basic structural features of high clustering coefficient, short average path length, power-law degree distribution, and scale-free characteristics. Generally speaking, microblog has characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, and node degree distribution of positive and negative correlation. The complex network characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, node degree distribution of positive and negative correlation and its application in various industries provide theoretical and practical methods to conduct and implement microblogging marketing. cache = ./cache/cord-017423-cxua1o5t.txt txt = ./txt/cord-017423-cxua1o5t.txt === reduce.pl bib === id = cord-016965-z7a6eoyo author = Brockmann, Dirk title = Human Mobility, Networks and Disease Dynamics on a Global Scale date = 2017-10-23 pages = extension = .txt mime = text/plain words = 6792 sentences = 396 flesch = 55 summary = In addition for infected sites to transmit the disease to neighboring susceptible lattice sites, every now and then (with a probability of 1%) they can also Fig. 19 .1) geographic distance to the initial outbreak location is no longer a good predictor of arrival time, unlike in systems with local or spatially limited host mobility infect randomly chosen lattice sites anywhere in the system. A visual inspection of the air-transportation system depicted in Fig. 19 .1 is sufficiently convincing that the significant fraction of long-range connections in global mobility will not only increase the speed at which infectious diseases spread but, more importantly, also cause the patterns of spread to exhibit high spatial incoherence and complexity caused by the intricate connectivity of the air-transportation network. Figure 19 .7 shows that also the model epidemic depicts only a weak correlation between geographic distance to the outbreak location and arrival time. cache = ./cache/cord-016965-z7a6eoyo.txt txt = ./txt/cord-016965-z7a6eoyo.txt === reduce.pl bib === id = cord-020871-1v6dcmt3 author = Papariello, Luca title = On the Replicability of Combining Word Embeddings and Retrieval Models date = 2020-03-24 pages = extension = .txt mime = text/plain words = 2135 sentences = 146 flesch = 55 summary = We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval. The last 5 years have seen proof that neural network-based word embedding models provide term representations that are a useful information source for a variety of tasks in natural language processing. They are grouped in three sets: classification, clustering, and information retrieval, and compare "standard" embedding methods with the novel moVMF representation. First, text processing (e.g. tokenisation); second, creating a fixed-length vector representation for every document; finally, the third phase is determined by the goal to be achieved, i.e. classification, clustering, and retrieval. We replicated previously reported experiments that presented evidence that a new mixture model, based on von Mises-Fisher distributions, outperformed a series of other models in three tasks (classification, clustering, and retrievalwhen combined with standard retrieval models). cache = ./cache/cord-020871-1v6dcmt3.txt txt = ./txt/cord-020871-1v6dcmt3.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-000759-36dhfptw author = Uribe-Sánchez, Andrés title = Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks date = 2011-06-05 pages = extension = .txt mime = text/plain words = 6945 sentences = 385 flesch = 43 summary = The existing models on pandemic influenza (PI) containment and mitigation aims to address various complex aspects of the pandemic evolution process including: (i) the mechanism of disease progression, from the initial contact and infection transmission to the asymptomatic phase, manifestation of symptoms, and the final health outcome [10] [11] [12] , (ii) the population dynamics, including individual susceptibility [13, 14] and transmissibility [10, [15] [16] [17] , and behavioral factors affecting infection generation and effectiveness of interventions [18] [19] [20] , (iii) the impact of pharmaceutical and nonpharmaceutical measures, including vaccination [21] [22] [23] , antiviral therapy [24] [25] [26] , social distancing [27] [28] [29] [30] [31] and travel restrictions, and the use of low-cost measures, such as face masks and hand washing [26, [32] [33] [34] . The single-region model subsumes the following components (see Figure 3 ): (i) population dynamics (mixing groups and schedules), (ii) contact and infection process, (iii) disease natural history, and (iv) mitigation strategies, including social distancing, vaccination, and antiviral application. cache = ./cache/cord-000759-36dhfptw.txt txt = ./txt/cord-000759-36dhfptw.txt === reduce.pl bib === id = cord-004416-qw6tusd2 author = Krishna, Smriti M. title = Development of a two-stage limb ischemia model to better simulate human peripheral artery disease date = 2020-02-26 pages = extension = .txt mime = text/plain words = 8149 sentences = 464 flesch = 49 summary = HLI was more severe in mice receiving the 2-stage compared to the 1-stage ischemia induction procedure as assessed by LDPI (p = 0.014), and reflected in a higher ischemic score (p = 0.004) and lower average distance travelled on a treadmill test (p = 0.045). Mice undergoing the 2-stage HLI also had lower expression of angiogenesis markers (vascular endothelial growth factor, p = 0.004; vascular endothelial growth factorreceptor 2, p = 0.008) and shear stress response mechano-transducer transient receptor potential vanilloid 4 (p = 0.041) within gastrocnemius muscle samples, compared to animals having the 1-stage HLI procedure. In contrast, the most commonly used animal model for initial testing of novel therapies for PAD is a model of acute blood supply interruption through ligation or excision of the femoral artery (referred to here as the 1-stage hind limb ischemia (HLI) model) 14, 15 . cache = ./cache/cord-004416-qw6tusd2.txt txt = ./txt/cord-004416-qw6tusd2.txt === reduce.pl bib === id = cord-018947-d4im0p9e author = Helbing, Dirk title = Challenges in Economics date = 2012-02-10 pages = extension = .txt mime = text/plain words = 11075 sentences = 750 flesch = 48 summary = This is also relevant for the following challenges, as boundedly rational agents may react inefficently and with delays, which questions the efficient market hypothesis, the equilibrium paradigm, and other fundamental concepts, calling for the consideration of spatial, network, and time-dependencies, heterogeneity and correlations etc. While it is a well-known problem that people tend to make unfair contributions to public goods or try to get a bigger share of them, individuals cooperate much more than one would expect according to the representative agent approach. In economics, one tries to solve the problem by introducing taxes (i.e. another incentive structure) or a "shadow of the future" (i.e. a strategic optimization over infinite time horizons in accordance with the rational agent approach) [96, 97] . One of the most important drawbacks of the representative agent approach is that it cannot explain the fundamental fact of economic exchange, since it requires one to assume a heterogeneity in resources or production costs, or to consider a variation in the value of goods among individuals. cache = ./cache/cord-018947-d4im0p9e.txt txt = ./txt/cord-018947-d4im0p9e.txt === reduce.pl bib === id = cord-010977-fwz7chzf author = Myserlis, Pavlos title = Translational Genomics in Neurocritical Care: a Review date = 2020-02-20 pages = extension = .txt mime = text/plain words = 11990 sentences = 519 flesch = 31 summary = In this review, we describe some of the approaches being taken to apply translational genomics to the study of diseases commonly encountered in the neurocritical care setting, including hemorrhagic and ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and status epilepticus, utilizing both forward and reverse genomic translational techniques. Termed "reverse translation," this approach starts with humans as the model system, utilizing genomic associations to derive new information about biological mechanisms that can be in turn studied further in vitro and in animal models for target refinement (Fig. 1) . These results highlight the value of reverse genomic translation in first identifying human-relevant genetic risk factors for disease, and using model systems to understand the pathways impacted by their introduction to select rationally-informed modalities for potential treatment. These observations provide vital information about cellular mechanisms impacted by human disease-associated genetic risk factors without requiring the expense and time investment of creating, validating, and studying animal models. cache = ./cache/cord-010977-fwz7chzf.txt txt = ./txt/cord-010977-fwz7chzf.txt === reduce.pl bib === id = cord-025517-rb4sr8r4 author = Koutsomitropoulos, Dimitrios A. title = Automated MeSH Indexing of Biomedical Literature Using Contextualized Word Representations date = 2020-05-06 pages = extension = .txt mime = text/plain words = 4474 sentences = 236 flesch = 52 summary = 4 presents our methodology and approach, by outlining the indexing procedure designed, describing the algorithms used and discussing optimizations regarding dataset balancing, distributed processing and training parallelization. There are two steps in this method: first, constructing MeSH term graph based on its RDF data and sampling the MeSH term sequences and, second, employing the FastText subword embedding model to learn the distributed word embeddings based on text sequences and MeSH term sequences. We then proceed by evaluating and reporting on two prominent embedding algorithms, namely Doc2Vec and ELMo. The models constructed with these algorithms, once trained, can be used to suggest thematic classification terms from the MeSH vocabulary. This body of text is next fed into the model and its vector similarity score is computed against the list of MeSH terms available in the vocabulary. Training datasets comprise biomedical literature from open access repositories including PubMed [19], EuropePMC [3] and ClinicalTrials [17] along with their handpicked MeSH terms. cache = ./cache/cord-025517-rb4sr8r4.txt txt = ./txt/cord-025517-rb4sr8r4.txt === reduce.pl bib === id = cord-018976-0ndb7rm2 author = Iwasa, Yoh title = Mathematical Studies of Dynamics and Evolution of Infectious Diseases date = 2007 pages = extension = .txt mime = text/plain words = 1796 sentences = 109 flesch = 48 summary = Mathematical modeling of infectious diseases is the most advanced subfield of theoretical studies in biology and the life sciences. The papers included in this volume are for mathematical studies of models on infectious diseases and cancer. This introductory chapter is followed by four papers on infectious disease dynamics, in which the roles of time delay (Chaps. Then, there are two chapters that discuss competition between strains and evolution occurring in the host population (Chap. By considering the appearance of novel strains with different properties from those of the resident population of pathogens, and tracing their abundance, we can discuss the evolutionary dynamics of infectious diseases. Iwasa and his colleagues derive a result that, without cross-immunity among strains, the pathogenicity of the disease tends to increase by any evolutionary changes. Beretta and his colleagues summarize their study of modeling of an immune system dynamics in which time delay is incorporated. cache = ./cache/cord-018976-0ndb7rm2.txt txt = ./txt/cord-018976-0ndb7rm2.txt === reduce.pl bib === id = cord-022891-vgfv5pi4 author = Hall, Graeme M. J. title = SIMULATING NEW ZEALAND FOREST DYNAMICS WITH A GENERALIZED TEMPERATE FOREST GAP MODEL date = 2000-02-01 pages = extension = .txt mime = text/plain words = 10475 sentences = 541 flesch = 53 summary = Forest gap simulation models have been developed to predict long-term impacts on forest ecosystems caused by blight, harvest management, past climates, animal browse, pollution, and large-scale disturbance by fire or storm, and to predict transients in species composition and forest structure due to changing climate, (e.g., Shugart and West 1977 , Aber et al. The LINKAGES model, as presented by Pastor and Post (1986) , required modifications to its slow-growth, available-light, and decay-rate conditions to reproduce forests characteristic of New Zealand sites. By contrast, simulations carried out using the cooler climate conditions for Reefton (typical of the South Island west coast of New Zealand) suggest that the emergent podocarp Dacrydium cupressinum, in association with the common hardwood Weinmannia racemosa, will more quickly dominate plots in this area (after the initial establishment of Aristotelia serrata, Leptospermum scoparium, and Kunzea ericoides). cache = ./cache/cord-022891-vgfv5pi4.txt txt = ./txt/cord-022891-vgfv5pi4.txt === reduce.pl bib === id = cord-002474-2l31d7ew author = Lv, Yang title = Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China date = 2017-04-03 pages = extension = .txt mime = text/plain words = 4938 sentences = 270 flesch = 51 summary = title: Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. Besides, according to the tested microbial density and the identified genome sequence of collected microorganisms, the hygrothermal response experiment of dominant fungal was detected, and the fitting analysis was carried out based on the prediction model, followed by a series of statistical analysis. The unit A showed the obvious microbial contamination status, though all components and airborne microorganism meet the Hygienic specification of central air conditioning ventilation system in public buildings of China 22 . cache = ./cache/cord-002474-2l31d7ew.txt txt = ./txt/cord-002474-2l31d7ew.txt === reduce.pl bib === id = cord-027201-owzhv0xy author = Tkacz, Magdalena A. title = Advantage of Using Spherical over Cartesian Coordinates in the Chromosome Territories 3D Modeling date = 2020-06-15 pages = extension = .txt mime = text/plain words = 3143 sentences = 191 flesch = 55 summary = This paper shows results of chromosome territory modeling in two cases: when the implementation of the algorithm was based on Cartesian coordinates and when implementation was made with Spherical coordinates. In the article, the summary of measurements of computational times of simulation of chromatin decondensation process (which led to constitute the chromosome territory) was presented. Initially, when implementation was made with the use of Cartesian Coordinates, simulation takes a lot of time to create a model (mean 746.7[sec] with the median 569.1[sec]) and additionally requires restarts of the algorithm, also often exceeds acceptable (given a priori) time for the computational experiment. This paper shows some new knowledge that we discover while trying to model chromosome territories (CT's) being a final result of modeling and simulation chromatin decondensation (CD) process and documents some problems (and the way we took to solve them) to make the working model. cache = ./cache/cord-027201-owzhv0xy.txt txt = ./txt/cord-027201-owzhv0xy.txt === reduce.pl bib === id = cord-013784-zhgjmt2j author = Tang, Min title = Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions date = 2020-06-04 pages = extension = .txt mime = text/plain words = 13704 sentences = 794 flesch = 45 summary = To move beyond serum-free sphere culture-based models, we utilized a DLP-based rapid 3D bioprinting system to generate 3D tri-culture or tetra-culture glioblastoma tissue models, with a background "normal brain" made up of NPCs and astrocytes and a tumor mass generated by GSCs, with or without macrophage, using brain-specific extracellular matrix (ECM) materials (Fig. 1a ). 35 While patient-derived glioblastoma cells grown under serum-free conditions enrich for stem-like tumor cells (GSCs) that form spheres and more closely replicate transcriptional profiles and invasive potential than standard culture conditions, we previously demonstrated that spheres display differential transcriptional profiles and cellular dependencies in an RNA interference screen compared to in vivo xenografts. [49] [50] [51] g Therapeutic efficacy prediction of drugs in all cancer cells in the CTRP dataset based on differentially expressed genes between the 3D tetra-culture model and GSCs grown in sphere culture as defined by RNA-seq. cache = ./cache/cord-013784-zhgjmt2j.txt txt = ./txt/cord-013784-zhgjmt2j.txt === reduce.pl bib === id = cord-016261-jms7hrmp author = Liu, Chunmei title = Profiling and Searching for RNA Pseudoknot Structures in Genomes date = 2005 pages = extension = .txt mime = text/plain words = 4330 sentences = 221 flesch = 53 summary = Profiling models based solely on sequence content such as Hidden Markov Model (HMM) [12] may miss structural homologies when directly used to search genomes for noncoding RNAs containing complex secondary structures. ERPIN searches genomes by sequentially looking for single stem loop motifs contained in the noncoding RNA gene, and reports a hit when significant alignment scores are observed for all the motifs at their corresponding locations. In this paper, we propose a new method to search for RNA pseudoknot structures using a model of multiple CMs. Unlike the model of Brown and Wilson, we use independent CM components to profile the interleaving stems in a pseudoknot. Finally, in order to test the ability of our program to cope with noncoding RNA genes with complex pseudoknot structures, we carried out an experiment where the complete DNA genomes of two bacteria were searched to find the locations of the tmRNA genes. cache = ./cache/cord-016261-jms7hrmp.txt txt = ./txt/cord-016261-jms7hrmp.txt === reduce.pl bib === id = cord-016045-od0fr8l0 author = Liu, Ming title = Epidemic-Logistics Network Considering Time Windows and Service Level date = 2019-10-04 pages = extension = .txt mime = text/plain words = 5287 sentences = 363 flesch = 60 summary = So, question researched in this study is: Based on the epidemic model analysis, how can we distribute the emergency materials to the whole EMDPs with a time windows constraint? In order to evaluate the practical efficiency of the proposed methodology, parameters of the SIR epidemic model are given as follows, b = d = 10 −5 , β = 10 −5 , α = 0.01, γ = 0.03, and initializing S = 10,000, I = 100, show the fitness and route length vary with iterate times using the new hybrid GA, respectively. Over To satisfy the emergency demand of epidemic diffusion, an efficient emergency service network, which considers how to locate the regional distribution center (RDC) and how to allocate all affected areas to these RDCs, should be urgently designed. cache = ./cache/cord-016045-od0fr8l0.txt txt = ./txt/cord-016045-od0fr8l0.txt === reduce.pl bib === id = cord-007255-jmjolo9p author = Pulliam, Juliet R. C. title = Ability to replicate in the cytoplasm predicts zoonotic transmission of livestock viruses date = 2009-02-15 pages = extension = .txt mime = text/plain words = 2458 sentences = 117 flesch = 42 summary = The database contains information on the 3 molecular characteristics hypothesized to influence the potential of a virus to cross host species: site of replication (X SR ; whether replication is completed in the cytoplasm or requires nuclear entry), genomic material (X GM ; RNA or DNA), and segmentation of the viral genome (X Seg ; segmented or nonsegmented). Hypothesis testing allowed us to determine how likely it was that the observed patterns were due to chance, whereas model-based prediction allowed us to determine what trait or set of traits was the best predictor of a livestock virus's ability to infect humans and to estimate the probability that a particular virus species would be able to jump host species, given knowledge of the traits of interest. To examine the magnitude and relative importance of the effects that the 3 molecular characteristics of interest have on the ability of the viral species in the database to infect humans, we developed a set of logistic regression models. cache = ./cache/cord-007255-jmjolo9p.txt txt = ./txt/cord-007255-jmjolo9p.txt === reduce.pl bib === id = cord-026384-ejk9wjr1 author = Crilly, Colin J. title = Predicting the outcomes of preterm neonates beyond the neonatal intensive care unit: What are we missing? date = 2020-05-19 pages = extension = .txt mime = text/plain words = 6059 sentences = 321 flesch = 44 summary = Our review provides a comprehensive analysis and critique of risk prediction models developed for preterm neonates, specifically predicting functional outcomes instead of mortality, to reveal areas of improvement for future studies aiming to develop risk prediction tools for this population. 17 published a systematic review of risk factor models for neurodevelopmental outcomes in children born very preterm or very low birth weight (VLBW). In this article, we conduct an in-depth, narrative review of the current risk models available for predicting the functional outcomes of preterm neonates, evaluating their relative strengths and weaknesses in variable and outcome selection, and considering how risk model development and validation can be improved in the future. Risk factor models for neurodevelopmental outcomes in children born very preterm or with very low birth weight: a systematic review of methodology and reporting Is the CRIB score (Clinical Risk Index for babies) a valid tool in predicting neurodevelopmental outcome in extremely low birth weight infants? cache = ./cache/cord-026384-ejk9wjr1.txt txt = ./txt/cord-026384-ejk9wjr1.txt === reduce.pl bib === id = cord-024501-nl0gsr0c author = Tan, Chunyang title = MSGE: A Multi-step Gated Model for Knowledge Graph Completion date = 2020-04-17 pages = extension = .txt mime = text/plain words = 3236 sentences = 206 flesch = 55 summary = In this paper, we first integrate iterative mechanism into knowledge graph embedding and propose a multi-step gated model which utilizes relations as queries to extract useful information from coarse to fine in multiple steps. First gate mechanism is adopted to control information flow by the interaction between entity and relation with multiple steps. In this paper, we propose a Multi-Step Gated Embedding (MSGE) model for link prediction in KGs. During every step, gate mechanism is applied several times, which is used to decide what features are retained and what are excluded at the dimension level, corresponding to the multi-step reasoning procedure. All results demonstrate our motivation that controlling information flow in a multi-step way is beneficial for link prediction task in knowledge graphs. In this paper, we propose a multi-step gated model MSGE for link prediction task in knowledge graph completion. cache = ./cache/cord-024501-nl0gsr0c.txt txt = ./txt/cord-024501-nl0gsr0c.txt === reduce.pl bib === id = cord-009481-6pm3rpzj author = Parnell, Gregory S. title = Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model date = 2009-12-11 pages = extension = .txt mime = text/plain words = 6493 sentences = 378 flesch = 50 summary = In the second section, we describe a canonical model for resource allocation decision making for an intelligent adversary problem using an illustrative bioterrorism example with notional data. (16) In our example, we will use four of the recommendations: model the decisions of intelligent adversaries, include risk management, simplify the model by not assigning probabilities to the branches of uncertain events, and do not normalize the risk. (29) In our defenderattacker-defender decision analysis model, we have the two defender decisions (buy vaccine, add a Bio Watch city), the agent acquisition for the attacker is uncertain, the agent selection and target of attack is another decision, the consequences (fatalities and economic) are uncertain, the defender decision after attack to mitigate the maximum possible casualties, and the costs of defender decisions are known. We use multiple objective decision analysis with an additive value (risk) model to assign risk to the defender consequences. cache = ./cache/cord-009481-6pm3rpzj.txt txt = ./txt/cord-009481-6pm3rpzj.txt === reduce.pl bib === id = cord-001603-vlv8x8l8 author = Ul-Haq, Zaheer title = 3D Structure Prediction of Human β1-Adrenergic Receptor via Threading-Based Homology Modeling for Implications in Structure-Based Drug Designing date = 2015-04-10 pages = extension = .txt mime = text/plain words = 5592 sentences = 298 flesch = 51 summary = ORCHESTRAR is specifically designed for homology or comparative protein modeling that identifies structurally conserved regions (SCRs), models loops using model-based and ab-initio methods, models side chains, and combine them all to prepare a final model. Initially, a homology model was generated by ORCHESTRAR that lacks a region of 45 amino acid residues (209-254) of the cytoplasmic loop of TM5 located within the target sequence but absent in the template structure. Two conserved disulfide bridges which are important for cell surface expression, ligand binding, receptor activation and maintenance of the secondary structure are located in EL-2 and EL-3 regions at positions Cys81-Cys166 and Cys159-Cys165, respectively (Table 5 ). The docking results reveals that Ser178 and Phe168 are crucial residues in ligand binding by providing H-bonding, and π-π interactions, respectively, thus helps in the activation of hsβADR1. cache = ./cache/cord-001603-vlv8x8l8.txt txt = ./txt/cord-001603-vlv8x8l8.txt === reduce.pl bib === id = cord-027228-s32v6bmd author = Subramanian, Vigneshwar title = Editorial: Why is modeling COVID-19 so difficult? date = 2020-06-19 pages = extension = .txt mime = text/plain words = 1117 sentences = 62 flesch = 51 summary = Disease spread depends heavily on the prevalence of COVID-19, which is not precisely known, and on policy interventions such as social distancing, which are a moving target and not intrinsically measurable. For example, the University of Texas model uses phone geolocation data as a proxy for social distancing and assumes the intervention remains constant across the forecasted time period 5 . Assumptions may also change over time as information emerges and their performance is reassessed; for example, the Columbia model updated contact tracing assumptions to the current parameters to model loosening social distancing restrictions as states reopen 6 . The general workflow involved in developing such a model is as follows: first, the outcome of interest is defined; second, relevant predictors or risk factors are identified; third, the effects of each predictor variable are estimated, for example in a regression analysis; and finally, the model is validated 7 . cache = ./cache/cord-027228-s32v6bmd.txt txt = ./txt/cord-027228-s32v6bmd.txt === reduce.pl bib === id = cord-026827-6vjg386e author = Awan, Ammar Ahmad title = HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow date = 2020-05-22 pages = extension = .txt mime = text/plain words = 6142 sentences = 347 flesch = 56 summary = To address these problems, we create HyPar-Flow—a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6[Formula: see text] speedup over Horovod-based data-parallel training, 2) 110[Formula: see text] speedup over single-node on 128 Stampede2 nodes, and 3) 481[Formula: see text] speedup over single-node on 512 Frontera nodes. To achieve performance, we need to investigate if applying widely-used and important HPC techniques like 1) efficient placement of processes on CPU cores, 2) pipelining via batch splitting, and 3) overlap of computation and communication can be exploited for improving performance of model-parallel and hybrid-parallel training. cache = ./cache/cord-026827-6vjg386e.txt txt = ./txt/cord-026827-6vjg386e.txt === reduce.pl bib === id = cord-027119-zazr8uj5 author = Taif, Khasrouf title = Cast Shadow Generation Using Generative Adversarial Networks date = 2020-05-25 pages = extension = .txt mime = text/plain words = 4029 sentences = 221 flesch = 56 summary = Generative Adversarial Networks have been implemented widely to perform graphical tasks, as it requires minimum to no human interaction, which gives GANs a great advantage over conventional deep learning methods, such as image-to-image translation with single D, G semi-supervised model [7] or unsupervised dual learning [26] . We apply image-to-image translation to our own image set to generate correct cast shadows for 3D rendered images in a semi-supervised manner using colour labels. We start with the assumption that GANs can generate both soft and hard shadows on demand, using colour labels and given a relatively small training image set. This paper explored a framework based on conditional GANs using a pix2pix Tensorflow port to perform computer graphic functions, by instructing the network to successfully generate shadows for 3D rendered images given training images paired with conditional colour labels. cache = ./cache/cord-027119-zazr8uj5.txt txt = ./txt/cord-027119-zazr8uj5.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-022219-y7vsc6r7 author = PEIFFER, ROBERT L. title = Animals in Ophthalmic Research: Concepts and Methodologies date = 2013-11-17 pages = extension = .txt mime = text/plain words = 24854 sentences = 1191 flesch = 46 summary = While the majority of investigations have had as their objective ultimate correlation with normal and abnormal function and structure of the human eye, laboratory studies have provided an abundance of comparative information that emphasizes that while there are numerous and amazing similarities in the peripheral visual system among the vertebrate (and even the invertebrate) animals, significant differences exist that are important to both researcher and clinician in selection of a research model and in extrapolation of data obtained from one species to another, and even among different species subdivisions. The use of laboratory animals in the investigation of infectious ocular disease has included rats, hamsters, guinea pigs, rabbits, cats, dogs, and subhuman primates. Ames and Hastings (1956) described a technique for rapid removal of the rabbit retina, together with a stump of optic nerve, for use in short-term culture experi ments including in vitro studies of retinal response to light (Ames and Gurian, 1960) . cache = ./cache/cord-022219-y7vsc6r7.txt txt = ./txt/cord-022219-y7vsc6r7.txt === reduce.pl bib === id = cord-025843-5gpasqtr author = Wild, Karoline title = Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models date = 2020-05-09 pages = extension = .txt mime = text/plain words = 5032 sentences = 378 flesch = 47 summary = title: Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models Although most of them are not limited to a specific infrastructure and able to manage multi-cloud applications, they all require a central orchestrator that processes the deployment model and executes all necessary tasks to deploy and orchestrate the application components on the respective infrastructure. We introduce a global declarative deployment model that describes a composite cross-organizational application, which is split to local parts for each participant. Based on the split declarative deployment models, workflows are generated which form the deployment choreography and coordinate the local deployment and cross-organizational data exchange. For the deployment execution we use an hybrid approach: Based on the LDM a local deployment workflow model is generated in step four that orchestrates the local deployment and cross-organizational information exchange activities. cache = ./cache/cord-025843-5gpasqtr.txt txt = ./txt/cord-025843-5gpasqtr.txt === reduce.pl bib === === reduce.pl bib === id = cord-024341-sw2pdnh6 author = Aksyonov, Konstantin title = Development of Cloud-Based Microservices to Decision Support System date = 2020-05-05 pages = extension = .txt mime = text/plain words = 3009 sentences = 236 flesch = 60 summary = Thus, the urgent task is to choose a dynamic model of a business process and build on its basis a web-service of simulation. 1) accounting for various types of resources [9, 10] ; 2) accounting for the status of operations and resources at specific times; 3) accounting for the conflicts on common resources and means [11, 12] ; 4) modeling of discrete processes; 5) accounting for complex resources (resource instances with properties, in the terminology of queuing systems -application (transaction)); 6) application of a situational approach (the presence of a language for describing situations (a language for representing knowledge) and mechanisms for diagnosing situations and finding solutions (a logical inference mechanism according to the terminology of expert systems); 7) implementation of intelligent agents (DM models); 8) description of hierarchical processes. A service should take one from the model domain with a specific identifier and refer to its many tasks for simulation. cache = ./cache/cord-024341-sw2pdnh6.txt txt = ./txt/cord-024341-sw2pdnh6.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-024515-iioqkydg author = Zhong, Qi title = Protecting IP of Deep Neural Networks with Watermarking: A New Label Helps date = 2020-04-17 pages = extension = .txt mime = text/plain words = 4588 sentences = 233 flesch = 57 summary = To mitigate this threat, in this paper, we propose an innovative framework to protect the intellectual property of deep learning models, that is, watermarking the model by adding a new label to crafted key samples during training. The intuition comes from the fact that, compared with existing DNN watermarking methods, adding a new label will not twist the original decision boundary but can help the model learn the features of key samples better. Extensive experimental results show that, compared with the existing schemes, the proposed method performs better under small perturbation strength or short key samples' length in terms of classification accuracy and ownership verification efficiency. -Effectiveness and efficiency: the false positive rate for key samples should be minimized, and a reliable ownership verification result needs to be obtained with few queries to the remote DNN API; -Robustness: the watermarked model can resist several known attacks, for example, pruning attack and fine-tuning attack. cache = ./cache/cord-024515-iioqkydg.txt txt = ./txt/cord-024515-iioqkydg.txt === reduce.pl bib === === reduce.pl bib === id = cord-018899-tbfg0vmd author = Brauer, Fred title = Epidemic Models date = 2011-10-03 pages = extension = .txt mime = text/plain words = 19642 sentences = 1293 flesch = 65 summary = For example, one of the fundamental results in mathematical epidemiology is that most mathematical epidemic models, including those that include a high degree of heterogeneity, usually exhibit "threshold" behavior, which in epidemiological terms can be stated as follows: If the average number of secondary infections caused by an average infective is less than one, a disease will die out, while if it exceeds one there will be an epidemic. [Technically, the attack rate should be called an attack ratio, since it is dimensionless and is not a rate.] The final size relation (9.3) can be generalized to epidemic models with more complicated compartmental structure than the simple SIR model (9.2), including models with exposed periods, treatment models, and models including quarantine of suspected individuals and isolation of diagnosed infectives. Compartmental models for epidemics are not suitable for describing the beginning of a disease outbreak because they assume that all members of a population are equally likely to make contact with a very small number of infectives. cache = ./cache/cord-018899-tbfg0vmd.txt txt = ./txt/cord-018899-tbfg0vmd.txt === reduce.pl bib === id = cord-025348-sh1kehrh author = Jurj, Sorin Liviu title = Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities date = 2020-05-02 pages = extension = .txt mime = text/plain words = 7396 sentences = 311 flesch = 54 summary = This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user's own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. cache = ./cache/cord-025348-sh1kehrh.txt txt = ./txt/cord-025348-sh1kehrh.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-027337-eorjnma3 author = Fratrič, Peter title = Integrating Agent-Based Modelling with Copula Theory: Preliminary Insights and Open Problems date = 2020-05-22 pages = extension = .txt mime = text/plain words = 4860 sentences = 253 flesch = 50 summary = The motivation for such a framework is illustrated on a artificial market functioning with canonical asset pricing models, showing that dependencies specified by copulas can enrich agent-based models to capture both micro-macro effects (e.g. herding behaviour) and macro-level dependencies (e.g. asset price dependencies). Section 2 provides some background: it elaborates on the combined need of agent-based modeling and of quantitative methods, illustrating the challenges on a running example based on canonical trader models for asset pricing, and gives a short presentation on copula theory. In other words, by this formula, it is possible to calculate the probability of rare events, and therefore estimate systematic risk, based on the dependencies of aggregation variables and on the knowledge of micro-behaviour specified by group density functions of the agent-based models. cache = ./cache/cord-027337-eorjnma3.txt txt = ./txt/cord-027337-eorjnma3.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-029311-9769dgb6 author = Nemati, Hamed title = Validation of Abstract Side-Channel Models for Computer Architectures date = 2020-06-13 pages = extension = .txt mime = text/plain words = 8427 sentences = 464 flesch = 55 summary = While there are information flow analyses that try to counter these threats [3, 15] , these approaches use models that abstract from many features of modern processors, like caches and pipelining, and their effects on channels that can be accessed by an attacker, like execution time and power consumption. In step three we use symbolic execution to syn-thesize the weakest relation on program states that guarantees indistinguishability in the observational model (Sect. Through this relation, the observational model is used to drive the generation of test cases -pairs of states that satisfy the relation and can be used as inputs to the program (Sect. The following observational model attempts to overapproximate information flows for data-caches by relying on the fact that accessing two different addresses that only differ in their cache offset produces the same cache effects: Notice that by making the program counter observable, this model assumes that the attacker can infer the sequence of instructions executed by the program. cache = ./cache/cord-029311-9769dgb6.txt txt = ./txt/cord-029311-9769dgb6.txt === reduce.pl bib === === reduce.pl bib === id = cord-028789-dqa74cus author = Ouhami, Maryam title = Deep Transfer Learning Models for Tomato Disease Detection date = 2020-06-05 pages = extension = .txt mime = text/plain words = 2695 sentences = 145 flesch = 55 summary = The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. In [6] , the study is based on a database of 120 images of infected rice leaves divided into three classes bacterial leaf blight, brown spot, and leaf smut (40 images for each class), Authors have converted the RGB images to an HSV color space to identify lesions, with a segmentation accuracy up to 96.71% using k-means. In plant disease detection field, many researchers have chosen deep models DensNets and VGGs for their high performance in standard computer vision tasks. In this paper we have studied three deep learning models in order to deal with the problem of plant disease detection. From the study that has been conducted it is possible to conclude that DensNet has a suitable architecture for the task of plants disease detection based on crop images. Using deep learning for image-based plant disease detection cache = ./cache/cord-028789-dqa74cus.txt txt = ./txt/cord-028789-dqa74cus.txt === reduce.pl bib === id = cord-021426-zo9dx8mr author = Peiffer, Robert L. title = Models in Ophthalmology and Vision Research date = 2013-10-21 pages = extension = .txt mime = text/plain words = 14366 sentences = 750 flesch = 44 summary = This chapter reviews the anatomy and physiology of the rabbit eye from a comparative perspective, summarizes documented spontaneous ocular conditions, discusses experimentally induced disease in general terms, and concludes with a summary of ob servations regarding the rabbit as a model for broad categories of research. This chapter reviews the anatomy and physiology of the rabbit eye from a comparative perspective, summarizes documented spontaneous ocular conditions, discusses experimentally induced disease in general terms, and concludes with a summary of observations regarding the rabbit as a model for broad categories of research. The choroidal thickness varies, being thickest posteriorly and thinning toward the ora ciliaris retinae; it tends to be thicker inferiorly compared to superiorly and is thickest and most heavily pigmented in the region of the visual streak, an area that lies well above the posterior pole of the globe on either side of and below the optic disk. Because the rabbit has a merangiotic retina, it is a less than ideal choice for an experimental model to study retinal vascular diseases of humans. cache = ./cache/cord-021426-zo9dx8mr.txt txt = ./txt/cord-021426-zo9dx8mr.txt === reduce.pl bib === id = cord-026742-us7llnva author = Gonçalves, Judite title = Effects of self-employment on hospitalizations: instrumental variables analysis of social security data date = 2020-06-15 pages = extension = .txt mime = text/plain words = 8629 sentences = 400 flesch = 48 summary = Our main findings, based on a sample of about 6,500 individuals followed monthly from 2005 to 2011 and who switch between self-employment and wage work along that period, suggest that self-employment has a positive effect on health as it reduces the likelihood of hospital admission by at least half. A recent study finds significantly lower work-related stress among self-employed individuals without employees compared with wage workers, using longitudinal data from Australia and controlling for individual fixed effects (Hessels et al. The main research question in this study is "What is the impact of self-employment on the likelihood of hospital admission?" We answer this question based on a large sample of administrative social security records representative of the working-age population in Portugal, that includes almost 130,000 self-employed and wage workers followed between January 2005 and December 2011. cache = ./cache/cord-026742-us7llnva.txt txt = ./txt/cord-026742-us7llnva.txt === reduce.pl bib === id = cord-015147-h0o0yqv8 author = nan title = Oral Communications and Posters date = 2014-09-12 pages = extension = .txt mime = text/plain words = 73711 sentences = 3862 flesch = 43 summary = Cyclooxygenases (COX) catalyze the first step in the synthesis of prostaglandins (PG) from arachidonic acid.COX-1 is constitutively expressed.The COX-2 gene is an immediate early-response gene that is induced by variety of mitogenic and inflammatory stimuli.Levels of COX-2 are increased in both inflamed and malignant tissues.In inflamed tissues, there is both pharmacological and genetic evidence that targeting COX-2 can either improve (e.g., osteoarthritis) or exacerbate symptoms (e.g., inflammatory bowel disease).Multiple lines of evidence suggest that COX-2 plays a significant role in carcinogenesis.The most specific data that support a cause-and effect relationship between COX-2 and tumorigenesis come from genetic studies.Overexpression of COX-2 has been observed to drive tumor formation whereas COX-2 deficiency protects against several tumor types.Selective COX-2 inhibitors protect against the formation and growth of experimental tumors.Moreover, selective COX-2 inhibitors are active in preventing colorectal adenomas in humans.Increased amounts of COX-2-derived PGE2 are found in both inflamed and neoplastic tissues.The fact that PGE2 can stimulate cell proliferation, inhibit apoptosis and induce angiogenesis fits with evidence that induction of COX-2 contributes to both wound healing and tumor growth.Taken together, it seems likely that COX-2 induction contributes to wound healing in response to injury but reduces the threshold for carcinogenesis. cache = ./cache/cord-015147-h0o0yqv8.txt txt = ./txt/cord-015147-h0o0yqv8.txt === reduce.pl bib === id = cord-004584-bcw90f5b author = nan title = Abstracts: 8th EBSA European Biophysics Congress, August 23rd–27th 2011, Budapest, Hungary date = 2011-08-06 pages = extension = .txt mime = text/plain words = 106850 sentences = 5038 flesch = 41 summary = Our goals are two-fold: (1) to monitor conformational changes in each domain upon its binding to specific ligands and then to correlate the observed changes with structural differences between the CRDs and (2) to investigate the interaction between the CRDs and lipid model membranes. Cholesterol-assisted lipid and protein interactions such as the integration into lipid nanodomains are considered to play a functional part in a whole range of membrane-associated processes, but their direct and non-invasive observation in living cells is impeded by the resolution limit of [200nm of a conventional far-field optical microscope. Therefore, to investigate the dynamic and complex membrane lateral organization in living cells, we have developed an original approach based on molecule diffusion measurements performed by fluorescence correlation spectroscopy at different spatial scales (spot variable FCS, svFCS) (1). cache = ./cache/cord-004584-bcw90f5b.txt txt = ./txt/cord-004584-bcw90f5b.txt === reduce.pl bib === id = cord-031143-a1qyadm6 author = Pinto Neto, Osmar title = Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19 date = 2020-08-30 pages = extension = .txt mime = text/plain words = 5288 sentences = 245 flesch = 53 summary = RESULTS: The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. The red circles (deaths) and blue circles (active cases) indicate real data up to June 18 Table 3 Inverse of the model optimized coefficients of γ, δ, ζ, and ε representing latent, infectious, hospitalization, and critical cases mean duration in days, as well as the model estimated basic reproductive number (R 0 ) and the death rate (DR) for June 18, 2020, for Germany, Brazil, Spain, Italy, South Korea, Portugal, Switzerland, Thailand, and USA, respectively. cache = ./cache/cord-031143-a1qyadm6.txt txt = ./txt/cord-031143-a1qyadm6.txt === reduce.pl bib === id = cord-031232-6cv8n2bf author = de Weck, Olivier title = Handling the COVID‐19 crisis: Toward an agile model‐based systems approach date = 2020-08-27 pages = extension = .txt mime = text/plain words = 7906 sentences = 343 flesch = 52 summary = In this paper, authors from several of the key countries involved in COVID‐19 propose a holistic systems model that views the problem from a perspective of human society including the natural environment, human population, health system, and economic system. 34 In order to take into account and to avoid such paradoxical consequences, one must choose a systems approach to analyze the COVID-19 crisis, integrating all existing domains of knowledge into a common understanding of the crisis, in order to obtain a global vision, both in space and time and at different possible observation scales, and thus giving a chance to find the global optimum for human society as a whole. • The lifecycle of the social system can be analyzed to first order in terms of wealth and health, where these features can be, respectively, In a systems approach, we will thus have to construct the different possible global lifecycle scenarios that can be achieved in this way (see Figure 4 for an illustration of this classical process), to evaluate their probabilities and to define means to mitigate the worst consequences. cache = ./cache/cord-031232-6cv8n2bf.txt txt = ./txt/cord-031232-6cv8n2bf.txt === reduce.pl bib === id = cord-030683-xe9bn1cc author = Wang, Wenxi title = A Study of Symmetry Breaking Predicates and Model Counting date = 2020-03-13 pages = extension = .txt mime = text/plain words = 6667 sentences = 345 flesch = 55 summary = We study the use of CNF-level and domain-level symmetry breaking predicates in the context of the state-of-the-art in model counting, specifically the leading approximate model counter ApproxMC and the recently introduced exact model counter ProjMC. Domain-specific predicates are particularly useful, and in many cases can provide full symmetry breaking to enable highly efficient model counting up to isomorphism. The other option is to ensure the formula that is input to the model counter includes symmetry breaking predicates [20, 21] , i.e., additional constraints that only allow canonical solutions from each isomorphism class, so the model counter can report the desired count. A key lesson of our study (in the context of the model counting problems considered) is: if non-isomorphic solution counts are desired, use full symmetry breaking predicates at the domain-level whenever feasible -even if it is straightforward to compute the number of non-isomorphic solutions from the number of all solutions, or even if the symmetry breaking constraints have to be written manually. cache = ./cache/cord-030683-xe9bn1cc.txt txt = ./txt/cord-030683-xe9bn1cc.txt === reduce.pl bib === id = cord-031957-df4luh5v author = dos Santos-Silva, Carlos André title = Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era date = 2020-09-02 pages = extension = .txt mime = text/plain words = 16609 sentences = 954 flesch = 43 summary = 19 Plant AMPs are the central focus of the present review, comprising information on their structural features (at genomic, gene, and protein levels), resources, and bioinformatic tools available, besides the proposition of an annotation routine. 26 Plant AMPs are also classified into families considering protein sequence similarity, cysteine motifs, and distinctive patterns of disulfide bonds, which determine the folding of the tertiary structure. 27, 31 These AMP categories will be detailed in the next sections, together with other groups here considered (Impatienlike, Macadamia [β-barrelins], Puroindoline (PIN), and Thaumatin-like protein [TLP]) and the recently described αhairpinin AMPs. The description includes comments on their structure, pattern for regular expression (REGEX) analysis (when available), functions, tissue-specificity, and scientific data availability. 179 As to the TLP structure, this protein presents characteristic thaumatin signature (PS00316): 180, 181 Most of the TLPs have molecular mass ranging from 21 to 26 kDa, 163 possessing 16 conserved cysteine residues (Supplementary Figure S8) involved in the formation of 8 disulfide bonds, 182 which help in the stability of the molecule, allowing a correct folding even under extreme conditions of temperature and pH. cache = ./cache/cord-031957-df4luh5v.txt txt = ./txt/cord-031957-df4luh5v.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-034834-zap82dta author = Bai, Xiao title = A Review of Micro-Based Systemic Risk Research from Multiple Perspectives date = 2020-06-27 pages = extension = .txt mime = text/plain words = 14932 sentences = 691 flesch = 41 summary = Meanwhile, cross-disciplinary research methods from other disciplines have been introduced, such as the introduction of complex network models when studying the structural stability of the system, linking the contagious effects of financial systemic risks to the transmission pathways of infectious diseases or bio-food chains [1] [2] [3] [4] [5] [6] , establishing new measures to measure systemic risk [7] [8] [9] [10] . Therefore, although the academic community still has differences in the definition of systemic risks, by comparing the concepts of systemic risk and financial crisis, and summarizing the definition of systemic risk in the academic world, the concept of systemic risk can be defined from an economic perspective: triggered by macro or micro-events, the institutions in the system are subjected to negative impacts, and more organizations are involved in risk diffusion and the existence of internal correlations strengthens the feedback mechanism, causing the system as a whole to face the risk of collapse. cache = ./cache/cord-034834-zap82dta.txt txt = ./txt/cord-034834-zap82dta.txt === reduce.pl bib === id = cord-033010-o5kiadfm author = Durojaye, Olanrewaju Ayodeji title = Potential therapeutic target identification in the novel 2019 coronavirus: insight from homology modeling and blind docking study date = 2020-10-02 pages = extension = .txt mime = text/plain words = 8125 sentences = 375 flesch = 53 summary = RESULTS: This study describes the detailed computational process by which the 2019-nCoV main proteinase coding sequence was mapped out from the viral full genome, translated and the resultant amino acid sequence used in modeling the protein 3D structure. Our current study took advantage of the availability of the SARS CoV main proteinase amino acid sequence to map out the nucleotide coding region for the same protein in the 2019-nCoV. The predicted secondary structure composition shows a high degree of alpha helix and beta sheets, respectively, occupying 45 and 47% of the total residues with the percentage loop occupancy at 8% regarded as comparative modeling, constructs atomic models based on known structures or structures that have been determined experimentally and likewise share more than 40% sequence homology. cache = ./cache/cord-033010-o5kiadfm.txt txt = ./txt/cord-033010-o5kiadfm.txt === reduce.pl bib === id = cord-033882-uts6wfqw author = Khakharia, Aman title = Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning date = 2020-10-16 pages = extension = .txt mime = text/plain words = 5853 sentences = 381 flesch = 64 summary = The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9% ± 3.9% was developed for 10 high population and high density countries. The data on the spread of COVID-19 in the top 10 densely populated countries, viz., India, Bangladesh, the Democratic Republic of Congo, Pakistan, China, Philippines, Germany, Indonesia, Ethiopia, and Nigeria were analyzed. The best outbreak prediction model was selected for each country depending on the accuracy values obtained decisions. Let us represent the Prediction plots for the number of COVID-19 patients that would rise in the next 5 days for some countries, where an exponential increase in the curve is expected or the rise in the cases would remain constant. cache = ./cache/cord-033882-uts6wfqw.txt txt = ./txt/cord-033882-uts6wfqw.txt === reduce.pl bib === id = cord-034843-cirltmy4 author = Nabipour, M. title = Deep Learning for Stock Market Prediction date = 2020-07-30 pages = extension = .txt mime = text/plain words = 8847 sentences = 451 flesch = 54 summary = Employing the whole of tree-based methods, RNN, and LSTM techniques for regression problems and comparing their performance in Tehran stock exchange is a recent research activity presented in this study. Six tree-based models namely Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost, and also three neural networks-based algorithms (ANN, RNN, and LSTM) are employed in the prediction of the four stock market groups. This study employed tree-based models (Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost) and neural networks (ANN, RNN, and LSTM) to correctly forecast the values of four stock market groups (Diversified Financials, Petroleum, Non-metallic minerals, and Basic metals) as a regression problem. This study employed tree-based models (Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost) and neural networks (ANN, RNN, and LSTM) to correctly forecast the values of four stock market groups (Diversified Financials, Petroleum, Non-metallic minerals, and Basic metals) as a regression problem. cache = ./cache/cord-034843-cirltmy4.txt txt = ./txt/cord-034843-cirltmy4.txt === reduce.pl bib === id = cord-048325-pk7pnmlo author = Hanley, Brian title = An object simulation model for modeling hypothetical disease epidemics – EpiFlex date = 2006-08-23 pages = extension = .txt mime = text/plain words = 8900 sentences = 524 flesch = 59 summary = RESULTS: EpiFlex indicates three phenomena of interest for public health: (1) R(0 )is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. EpiFlex uses a dynamic network to model the interactions between hosts at a particular location based on the skew provided and the demographic segments movement cycles. The EpiFlex system iterates through all areas in a model and allocates hosts, putting them in their initial locations, per the movement definitions for the demographic group. cache = ./cache/cord-048325-pk7pnmlo.txt txt = ./txt/cord-048325-pk7pnmlo.txt === reduce.pl bib === id = cord-034846-05h2no14 author = Singer, Gonen title = Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic date = 2020-08-07 pages = extension = .txt mime = text/plain words = 7264 sentences = 360 flesch = 50 summary = We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. We use the proposed measure to develop ordinal decision-tree-based ensemble approaches, i.e., ordinal AdaBoost and random forest models, which are known to outperform individual classifiers. The main objectives of this study are fourfold: (i) to extend the weighted information gain measure such that the classification error can be measured from a statistical value that is not necessarily defined by a single class; (ii) to develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used; (iii) to examine the advantage of combining ordinal decision-tree-based ensemble approaches with non-ordinal individual classifiers to leverage the strengths of each type of classifier; and (iv) to examine the ability to carry out multi-class identification of different levels of a daily growth factor using ordinal decision-tree-based ensemble approaches. cache = ./cache/cord-034846-05h2no14.txt txt = ./txt/cord-034846-05h2no14.txt === reduce.pl bib === id = cord-034839-6xctzwng author = Bień-Barkowska, Katarzyna title = Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets date = 2020-07-20 pages = extension = .txt mime = text/plain words = 9893 sentences = 480 flesch = 58 summary = We aim to contribute to this strand of research by proposing a new self-exciting probability peaks-over-threshold (SEP-POT) model with the prerogative of being adequately tailored to the dynamics of real-world extreme events in financial markets. The point-process POT model approximates the time-varying conditional probability of an extreme loss over a given day with the help of a conditional intensity function that characterizes the arrival rate of such extreme events. According to such a point process approach to POT models, the first factor on the left-hand side of Equation (3) (i.e., the conditional probability of a threshold exceedance over day t + 1) can be derived based on the (time varying) conditional intensity function as follows: The dynamic versions of the POT models benefit from both (1) the point process theory, which allows for the time-varying intensity rate of threshold exceedances, and hence, the clustering of extreme losses, and (2) the EVT, which allows us to account for the tail risk of financial instruments. cache = ./cache/cord-034839-6xctzwng.txt txt = ./txt/cord-034839-6xctzwng.txt === reduce.pl bib === id = cord-024061-gxv8y146 author = Alkhamis, Moh A. title = Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks date = 2020-04-21 pages = extension = .txt mime = text/plain words = 12855 sentences = 540 flesch = 36 summary = Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Our phylodynamic analyses included comparisons between commonly inferred evolutionary posterior parameters (e.g., substitution rate/site/year, divergence times, phylogeographic root state posterior probabilities, significant dispersal route between states) under different combinations of node-age and branch rate prior models. Epidemiology of Swine Influenza in the U.S. Based on the results of the best fitting phylodynamic models for both HA and PB2 segments, evolutionary rates of currently circulating human-like H3 viruses in the United States remain high with no apparent signs of substantial declines (Figures 2B,D) and were similar to what was inferred elsewhere (117). cache = ./cache/cord-024061-gxv8y146.txt txt = ./txt/cord-024061-gxv8y146.txt === reduce.pl bib === id = cord-035388-n9hza6vm author = Xu, Jie title = Federated Learning for Healthcare Informatics date = 2020-11-12 pages = extension = .txt mime = text/plain words = 6143 sentences = 352 flesch = 43 summary = This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. For both provider (e.g., building a model for predicting the hospital readmission risk with patient Electronic Health Records (EHR) [71] ) and consumer (patient)-based applications (e.g., screening atrial fibrillation with electrocardiograms captured by smartwatch [79] ), the sensitive patient data can stay either in local institutions or with individual consumers without going out during the federated model learning process, which effectively protects the patient privacy. Federated learning is a problem of training a high-quality shared global model with a central server from decentralized data scattered among large number of different clients (Fig. 1) . cache = ./cache/cord-035388-n9hza6vm.txt txt = ./txt/cord-035388-n9hza6vm.txt === reduce.pl bib === id = cord-103913-jgko7b0j author = Macedo, A. M. S. title = A comparative analysis between a SIRD compartmental model and the Richards growth model date = 2020-08-06 pages = extension = .txt mime = text/plain words = 3034 sentences = 182 flesch = 57 summary = We illustrate the use of the map between these two models by fitting the fatality curves of the COVID-19 epidemic data in Italy, Germany, Sweden, Netherlands, Cuba, and Japan. Here we improve on this analysis in two ways: (i) we extend the SIR model to a SIRD model by incorporating the deceased compartment, which is then used as the basis for the map onto the Richards model; (ii) the parameters of the SIRD model are allowed to have a time dependence, which is crucial to gain some efficacy in describing realistic cumulative epidemic curves of COVID-19. where C(t) is the cumulative number of cases/deaths at time t, r is the growth rate at the early stage, K is the final epidemic size, and the parameter α measures the asymmetry with respect to the s-shaped curve of the standard logistic model, which is recovered for α = 1. cache = ./cache/cord-103913-jgko7b0j.txt txt = ./txt/cord-103913-jgko7b0j.txt === reduce.pl bib === id = cord-034181-ji4empe6 author = Saqib, Mohd title = Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model date = 2020-10-23 pages = extension = .txt mime = text/plain words = 4637 sentences = 277 flesch = 55 summary = The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. Furthermore, one issue occurs when working with time-series data (as COVID-19 confirmed cases) is over-fitting particularly when estimating models with large numbers of parameters over relatively short periods and the solution to the over-fitting problem, is to take a Bayesian approach (using Ridge Regularization) which allows us to impose certain priors on depended variables [26] . In the Bayesian regression approach, we can take into account Other models are developed with good accuracy but as well as data become available, those entire algorithms will not able to survive without a few evaluations due to the dynamic nature of pandemic escalation of the COVID-19 but the proposed model corrects the distributions for model parameters and forecasting results using parameters distributions. cache = ./cache/cord-034181-ji4empe6.txt txt = ./txt/cord-034181-ji4empe6.txt === reduce.pl bib === id = cord-102359-k1xxz4hc author = Klotsa, Daphne title = Electronic Transport in DNA date = 2005-04-04 pages = extension = .txt mime = text/plain words = 6669 sentences = 412 flesch = 62 summary = In most models of electronic transport [13, 60] it has been assumed that the transmission channels are along the long axis of the DNA molecule [61] and that the conduction path is due to π-orbital overlap between consecutive bases [52] ; density-functional calculations [37] have shown that the bases, especially Guanine, are rich in π-orbitals. The main advantage of both methods is that they work reliably (i) for short DNA strands ranging from 13 (DFT studies [37] ) base pairs up to 30 base pairs length which are being used in the nanoscopic transport measurements [15] as well as (ii) for somewhat longer DNA sequences as modelled in the electron transfer results and (iii) even for complete DNA sequences which contain, e.g. for human chromosomes up to 245 million base pairs [2] . The fishbone and ladder models studied in the present paper give qualitatively similar results, i.e. a gap in the DOS on the order of the hopping energies to the backbone, extended states for periodic DNA sequences and localised states for any non-zero disorder strength. cache = ./cache/cord-102359-k1xxz4hc.txt txt = ./txt/cord-102359-k1xxz4hc.txt === reduce.pl bib === id = cord-119104-9d421si9 author = Huynh, Tin Van title = BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models date = 2020-09-06 pages = extension = .txt mime = text/plain words = 1816 sentences = 134 flesch = 63 summary = title: BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models In this article, we present our approach at WNUT-2020 Task 2 to identify Tweets containing information about COVID-19 on the social networking platform Twitter or not. • Firstly, we implemented four different models based on neural networks and transformers such as Bi-GRU-CNN, BERT, RoBERTa, XLNet to solve the WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. In this paper, we propose an ensemble method that combines the deep learning models with the transfer learning models to identify information about COVID-19 from users' tweets. In this paper, we used the SOTA transfer learning models, such as BERT (Devlin et al., 2019) , RoBERTa (Liu et al., 2019) , and XLNet (Yang et al., 2019) with fine-tuning techniques for the problem of identifying informative tweet about COVID-19. cache = ./cache/cord-119104-9d421si9.txt txt = ./txt/cord-119104-9d421si9.txt === reduce.pl bib === id = cord-125330-jyppul4o author = Crokidakis, Nuno title = Modeling the evolution of drinking behavior: A Statistical Physics perspective date = 2020-08-24 pages = extension = .txt mime = text/plain words = 3629 sentences = 218 flesch = 55 summary = The standard medical way of categorizing alcohol consumption [15] is in three groups -nonconsumers, moderate (or social) consumers and risk (or excessive) consumers; thus, modeling of the interactions and consequent changes of an individual from one group to another is governed by interaction parameters. This transition M → R can also occur spontaneously, with probability α, if a given agent increase his/her alcohol consumption -this is the only migration pathway from one group to another, in this model, that does not depend on the population of the receiving compartment, since it corresponds to a self-induced progression from Moderate (M) to Risk (R) drinking. The transitions among the compartments are ruled by probabilities, representing the social interactions among individuals, as well as spontaneous decisions, in particular from moderate evolving into risk drinkers, and we studied the model through analytical and numerical calculations. cache = ./cache/cord-125330-jyppul4o.txt txt = ./txt/cord-125330-jyppul4o.txt === reduce.pl bib === id = cord-117688-20gfpbyf author = Cakmakli, Cem title = Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model date = 2020-07-03 pages = extension = .txt mime = text/plain words = 7551 sentences = 423 flesch = 58 summary = This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. A real-time estimation and forecasting exercise starting from April show that the proposed model with time varying parameters indeed provide timely information on the current stance of the pandemic ahead of the competing models. (2020) use a least squares based approach on a rolling window of daily observations and document the time variation of parameters in the SIRD based model using Chinese data. Independent of the analysis of COVID-19 pandemic, observation-driven models for count data are considered in many different cases. Finally, we explore whether this capability of the TVP-SIRD model in reflecting the stance of the pandemic in a timely manner indeed proved to be useful in forecasting the number of active cases. cache = ./cache/cord-117688-20gfpbyf.txt txt = ./txt/cord-117688-20gfpbyf.txt === reduce.pl bib === id = cord-122344-2lepkvby author = Hayashi, Hiroaki title = What's New? Summarizing Contributions in Scientific Literature date = 2020-11-06 pages = extension = .txt mime = text/plain words = 7260 sentences = 383 flesch = 44 summary = To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. The new task's goal is to generate two summaries simultaneously, one strictly focused on the summarized article's novelties and contributions, the other introducing the context of the work and previous efforts. Recent trends in abstractive text summarization show a shift of focus from designing task-specific architectures trained from scratch (See et al., 2017; Paulus et al., 2018) to leveraging large-scale Transformer-based models pre-trained on vast amounts of data (Liu & Lapata, 2019; Lewis et al., 2020) , often in multi-task settings (Raffel et al., 2019) . In this paper, we propose disentangled paper summarization, a new task in scientific paper summarizing where models simultaneously generate contribution and context summaries. cache = ./cache/cord-122344-2lepkvby.txt txt = ./txt/cord-122344-2lepkvby.txt === reduce.pl bib === id = cord-103502-asphso2s author = Herrgårdh, Tilda title = An organ-based multi-level model for glucose homeostasis: organ distributions, timing, and impact of blood flow date = 2020-10-21 pages = extension = .txt mime = text/plain words = 8160 sentences = 409 flesch = 60 summary = Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. However, neither this model, nor any of the previously mentioned multi-level models, have subdivided the glucose uptake into the individual contributions of all of the main insulin-responding and glucose-utilizing organs: adipose tissue, muscle, and liver. The final combined model (Q4) can fit to all of the new data for glucose uptake in all organs (Fig 6) , as well as to all previous data, such as the postprandial glucose and insulin fluxes and concentrations in (Dalla Man et al. cache = ./cache/cord-103502-asphso2s.txt txt = ./txt/cord-103502-asphso2s.txt === reduce.pl bib === id = cord-118553-ki6bbuod author = Piccolomini, Elena Loli title = Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model date = 2020-03-22 pages = extension = .txt mime = text/plain words = 1842 sentences = 107 flesch = 55 summary = In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, using the data from the Italian Protezione Civile from February 24th 2020. Since several restricting measures have been imposed by the Italian government at different times, starting from March 8th 2020, we propose a modification of SEIRD by introducing a time dependent transmitting rate. The SIR model and its later modifications, such as Susceptible-Exposed-Infected-Removed (SEIR) [2] are commonly used by the epidemic medical community in the study of outbreaks diffusion.In these models, the population is divided into groups. Hopefully, these measures will affect the spread of the COVID-19 virus reducing the number of infected people and the value of the parameter R0. In this paper we propose a SEIRD model accounting for five different groups, Susceptible, Exposed, Infected, Recovery and Dead. cache = ./cache/cord-118553-ki6bbuod.txt txt = ./txt/cord-118553-ki6bbuod.txt === reduce.pl bib === id = cord-129272-p1jeiljo author = Broniec, William title = Using VERA to explain the impact of social distancing on the spread of COVID-19 date = 2020-03-30 pages = extension = .txt mime = text/plain words = 1932 sentences = 113 flesch = 45 summary = We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the spread of COVID-19. In this article, we describe VERA_Epidemiology (or just VERA for short), an interactive AI tool that enables users to build conceptual models of the impact of social distancing on COVID-19. We describe the use of VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity. We describe the use of VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity. The conceptual models in Figure 2 illustrate an interaction between social distancing and COVID-19 cases. Now that we have illustrated the core techniques in VERA, we describe the use of VERA develop the SIR model for understanding the relationship between social distancing and the spread of COVID-19. cache = ./cache/cord-129272-p1jeiljo.txt txt = ./txt/cord-129272-p1jeiljo.txt === reduce.pl bib === id = cord-128991-mb91j2zs author = Agapiou, Sergios title = Modeling of Covid-19 Pandemic in Cyprus date = 2020-10-05 pages = extension = .txt mime = text/plain words = 7453 sentences = 419 flesch = 58 summary = Here we report our work including results from statistical and mathematical models used to understand the epidemiology of COVID-19 in Cyprus, during the time period starting from the beginning of March till the end of May 2020. We use change-point detection, count time series methods and compartmental models for short and long term projections, respectively. Testing approaches in the Republic of Cyprus included: a) targeted testing of suspect cases and their contacts; of repatriates at the airport and during their 14-day quarantine; of teachers and students when schools re-opened in mid-May; of employees in essential services that continued their operation throughout the first pandemic wave (e.g., customer services, public domain); and of health-care workers in public hospitals, and b) population screenings following random sampling in the general population of most districts and in two municipalities with increased disease burden. cache = ./cache/cord-128991-mb91j2zs.txt txt = ./txt/cord-128991-mb91j2zs.txt === reduce.pl bib === === reduce.pl bib === id = cord-127900-78x19fw4 author = Leung, Abby title = Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies date = 2020-10-06 pages = extension = .txt mime = text/plain words = 5032 sentences = 279 flesch = 55 summary = More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening. Only by incorporating real world contact networks into compartment models, one can disconnect network hubs to realistically simulate the effect of closure. We focus on the effects of 4 widely adopted NPIs: 1) quarantining infected and exposed individuals, 2) social distancing, 3) closing down of non-essential work places and schools, and 4) the use of face masks. • We show that structure of the contact networks significantly changes the epidemic curves and the current compartment based models are subject to overestimating the scale of the spread • We demonstrate the degree of effectiveness of different NPIs depends on the assumed underlying structure of the contact networks cache = ./cache/cord-127900-78x19fw4.txt txt = ./txt/cord-127900-78x19fw4.txt === reduce.pl bib === id = cord-103280-kf6mqv4e author = Bergs, Thomas title = Determination of Johnson-Cook material model parameters for AISI 1045 from orthogonal cutting tests using the Downhill-Simplex algorithm date = 2020-12-31 pages = extension = .txt mime = text/plain words = 7779 sentences = 455 flesch = 45 summary = title: Determination of Johnson-Cook material model parameters for AISI 1045 from orthogonal cutting tests using the Downhill-Simplex algorithm Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. These results motivated for the development of a methodology capable to determine material model parameters robust and inversely from the machining process, which can be used with lower computational effort. By using the Downhill-Simplex-Algorithm, it was possible to determine material model parameters within 17 iterations and achieving an average deviation between the experiment and the simulations below 10 %. Therefore, a sequential approach, starting with an initial set of machining simulation based on a design of computer experiments (DOCE) and analysis of the numerical results in terms of cutting forces and temperatures was used. cache = ./cache/cord-103280-kf6mqv4e.txt txt = ./txt/cord-103280-kf6mqv4e.txt === reduce.pl bib === id = cord-104133-d01joq23 author = Arthur, Ronan F. title = Adaptive social contact rates induce complex dynamics during epidemics date = 2020-07-14 pages = extension = .txt mime = text/plain words = 5240 sentences = 300 flesch = 55 summary = We develop a model for adaptive optimal control of the effective social contact rate within a Susceptible-Infectious-Susceptible (SIS) epidemic model using a dynamic utility function with delayed information. To represent endogenous behavior-change, we start with the classical discrete-time 112 susceptible-infected-susceptible (SIS) model [28] , which, when incidence is relatively 113 small compared to the total population [29, 30] , can be written in terms of the recursions 114 In order to introduce human behavior, we 121 substitute for b a time-dependent b t , which is a function of both b 0 , the probability that 122 disease transmission takes place on contact, and a dynamic social rate of contact c t 123 whose optimal value, c * t , is determined at each time t as in economic epidemiological 124 models [31] , namely cache = ./cache/cord-104133-d01joq23.txt txt = ./txt/cord-104133-d01joq23.txt === reduce.pl bib === id = cord-130240-bfnav9sn author = Friston, Karl J. title = Dynamic causal modelling of COVID-19 date = 2020-04-09 pages = extension = .txt mime = text/plain words = 13594 sentences = 688 flesch = 51 summary = The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Specifically, the posterior densities (i.e., Bayesian beliefs) over states and parameters-and the precision of random fluctuations-are optimised by maximising a variational bound on the model's marginal likelihood, also known as model evidence. This figure reports the differences among countries in terms of selected parameters of the generative model, ranging from the effective population size, through to the probability of testing its denizens. In this example (based upon posterior expectations for the United Kingdom and Bayesian parameter averages over countries), death rates (per day) decrease progressively with social distancing. cache = ./cache/cord-130240-bfnav9sn.txt txt = ./txt/cord-130240-bfnav9sn.txt === reduce.pl bib === id = cord-103435-yufvt44t author = van Aalst, Marvin title = Constructing and analysing dynamic models with modelbase v1.0 - a software update date = 2020-10-02 pages = extension = .txt mime = text/plain words = 4085 sentences = 208 flesch = 37 summary = Background Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Results and Discussion We provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. Most recently, deterministic models simulating the dynamics of infectious diseases gained the interest of the general public during our combat of the Covid-19 pandemic, when a large number of ODE based mathematical models has been developed and discussed even in nonscientific journals (see for example [3] [4] [5] ). Implementation modelbase is a Python package to facilitate construction and analysis of ODE based mathematical models of biological systems. We are presenting here updates of our modelling software that has been developed to simplify the building process of mathematical models based on ODEs. modelbase is fully embedded in the Python programming language. cache = ./cache/cord-103435-yufvt44t.txt txt = ./txt/cord-103435-yufvt44t.txt === reduce.pl bib === === reduce.pl bib === id = cord-102850-0kiypige author = Huang, C.-C. title = A Machine Learning Study to Improve Surgical Case Duration Prediction date = 2020-06-12 pages = extension = .txt mime = text/plain words = 4728 sentences = 252 flesch = 53 summary = The results are reported in 225 In Fig. 3 , we plotted scatter plots of actual versus predicted duration on the external 234 testing set for the average models of surgeon-and procedure-specific, and the XGB 235 model. Moreover, 251 three of the features which we computed from surgeons' data (i.e. total surgical minutes 252 performed by the surgeon within the last 7 days and on the same day, and number of Accurate prediction of operation case duration is vital in elevating OR efficiency and 257 reducing cost. It has been reported in the past studies that primary surgeons contributed the 301 largest variability in operation case duration prediction compared to other factors 302 attributed to patients [2, 16, 23] . 356 We propose extracting additional information from operation and surgeons' data to 357 be used as predictor variables for ML algorithm training since their importance was 358 high in the XGB model. cache = ./cache/cord-102850-0kiypige.txt txt = ./txt/cord-102850-0kiypige.txt === reduce.pl bib === id = cord-048461-397hp1yt author = Coelho, Flávio C title = Epigrass: a tool to study disease spread in complex networks date = 2008-02-26 pages = extension = .txt mime = text/plain words = 4006 sentences = 212 flesch = 53 summary = BACKGROUND: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. RESULTS: A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. The Epigrass system is driven by a graphical user interface(GUI), which handles several input files required for model definition and manages the simulation and output generation (figure 2). To run a network epidemic model in Epigrass, the user is required to provide three separate text files (Optionally, also a shapefile with the map layer): cache = ./cache/cord-048461-397hp1yt.txt txt = ./txt/cord-048461-397hp1yt.txt === reduce.pl bib === id = cord-132307-bkkzg6h1 author = Blanco, Natalia title = Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness date = 2020-07-06 pages = extension = .txt mime = text/plain words = 3759 sentences = 164 flesch = 51 summary = The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The early part of the curve was fit and statistical parameters were generated using Prism 8 (GraphPad) using the non-linear regression module using the program standard centered second order polynomial (quadratic), exponential growth, and the Gompertz growth model as defined by Prism 8, and a simple user-defined simple square model (N = At 2 + C) where N is the total number of cases, A and C are the fitting constants, and t is the number of days from the beginning of the epidemic curve. The total number of cases for each of 28 countries was plotted with time and several model equations were fit to the early part of the data before mitigating effects from public health policies began to change the rate of disease spread. cache = ./cache/cord-132307-bkkzg6h1.txt txt = ./txt/cord-132307-bkkzg6h1.txt === reduce.pl bib === === reduce.pl bib === id = cord-140977-mg04drna author = Maltezos, S. title = Methodology for Modelling the new COVID-19 Pandemic Spread and Implementation to European Countries date = 2020-06-27 pages = extension = .txt mime = text/plain words = 3985 sentences = 211 flesch = 59 summary = Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country's characteristics and the parameters of the parametrization. where the function c(t) applied in an epidemic spread represents the rate of the infected individuals as the new daily reported cases (DRC) and coincides with the function I(t) in the SIR model, as we can see in the following. The more analytical approach, in the general case from the mathematical point of view, comes from the fundamental study of the epidemic growth and includes a number of terms in a form of double summation related to the inverse Laplace Transform of a rational function given in [8] , referring to the "Earlier stages of an epidemic in a large population". cache = ./cache/cord-140977-mg04drna.txt txt = ./txt/cord-140977-mg04drna.txt === reduce.pl bib === id = cord-143539-gvt25gac author = Marmarelis, Myrl G. title = Latent Embeddings of Point Process Excitations date = 2020-05-05 pages = extension = .txt mime = text/plain words = 5357 sentences = 355 flesch = 54 summary = By performing synthetic experiments on short records as well as an investigation into options markets and pathogens, we demonstrate that learning the embedding alongside a point process model uncovers the coherent, rather than spurious, interactions. The propagation of disease [1] , news topics [2] , crime patterns [3, 4] , neuronal firings [5] , and market trade-level activity [6, 7] naturally suit the form of diachronic point processes with an underlying causal-interaction network. Furnished with the causality estimates in Eq. 6 (the "Expectation" step), we perform projected gradient ascent by setting partial derivatives of the complete-data log-likelihood with respect to each kernel parameter to zero (the "Maximization" step). We demonstrated the viability of estimating embeddings for events in an interpretable metric space tied to a self-exciting point process. The block point process model for continuous-time event-based dynamic networks Latent self-exciting point process model for spatial-temporal networks cache = ./cache/cord-143539-gvt25gac.txt txt = ./txt/cord-143539-gvt25gac.txt === reduce.pl bib === id = cord-159103-dbgs2ado author = Rieke, Nicola title = The Future of Digital Health with Federated Learning date = 2020-03-18 pages = extension = .txt mime = text/plain words = 6703 sentences = 326 flesch = 46 summary = The medical FL use-case is inherently different from other domains, e.g. in terms of number of participants and data diversity, and while recent surveys investigate the research advances and open questions of FL [14, 11, 15] , we focus on what it actually means for digital health and what is needed to enable it. Transfer Learning, for example, is a well-established approach of model-sharing that makes it possible to tackle problems with deep neural networks that have millions of parameters, despite the lack of extensive, local datasets that are required for training from scratch: a model is first trained on a large dataset and then further optimised on the actual target data. To adopt this approach into a form of collaborative learning in a FL setup with continuous learning from different institutions, the participants can share their model with a peer-to-peer architecture in a "round-robin" or parallel fashion and train in turn on their local data. cache = ./cache/cord-159103-dbgs2ado.txt txt = ./txt/cord-159103-dbgs2ado.txt === reduce.pl bib === === reduce.pl bib === id = cord-123800-pxhott2p author = Pandey, Gaurav title = SEIR and Regression Model based COVID-19 outbreak predictions in India date = 2020-04-01 pages = extension = .txt mime = text/plain words = 3163 sentences = 178 flesch = 60 summary = title: SEIR and Regression Model based COVID-19 outbreak predictions in India In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. For analysis and prediction of number of COVID-19 patients in India, the following models have been used. Recovered person was not sick again during the calculation period Now, considering 70% of India's population to be approximately 966 million in susceptible class (S) and assuming only 1 person got infected in the initial part with average incubation period of 5.2, average infectious period of 2.9 and R 0 equal to 4, the SEIR model without intervention is shown in Figure 3 with the assumptions mentioned above. In this study, two machine learning models SEIR and Regression were used to analyse and predict the change in spread of COVID-19 disease. cache = ./cache/cord-123800-pxhott2p.txt txt = ./txt/cord-123800-pxhott2p.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-161039-qh9hz4wz author = Tripathy, Shrabani S. title = Flood Evacuation During Pandemic: A multi-objective Framework to Handle Compound Hazard date = 2020-10-03 pages = extension = .txt mime = text/plain words = 2923 sentences = 164 flesch = 48 summary = This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. Various studies have used optimization models for flood evacuation to minimize losses considering factors like travel time and distance, cost of evacuation, and usage of infrastructure (5, 6, 14, (17) (18) (19) . Jagatsinghpur is a coastal (east coast) district in the state Odisha, India (Figure 2 The first step in designing any evacuation strategy is to identify the villages with high flood hazard. Shelter location-allocation model for flood evacuation planning A spatiotemporal optimization model for the evacuation of the population exposed to flood hazard cache = ./cache/cord-161039-qh9hz4wz.txt txt = ./txt/cord-161039-qh9hz4wz.txt === reduce.pl bib === id = cord-006229-7yoilsho author = nan title = Abstracts of the 82(nd) Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) and the 18(th) Annual Meeting of the Network Clinical Pharmacology Germany (VKliPha) in cooperation with the Arbeitsgemeinschaft für Angewandte Humanpharmakologie e.V. (AGAH) date = 2016-02-06 pages = extension = .txt mime = text/plain words = 133493 sentences = 6804 flesch = 42 summary = It directly activates Protein Kinase A (PKA) or the Exchange protein directly activated by cAMP (Epac) which is a guanine exchange factor (GEF) for the small monomeric GTPase Rap. As Human umbilical vein endothelial cells (HUVEC) express both cAMP effectors (Epac1 and PKA), we investigated the role of cAMP-signaling using a spheroid based sprouting assay as an in vitro model for angiogenesis. After activation, S1P receptors regulate important processes in the progression of renal diseases, such as mesangial cell migration Methods and Results: Here we demonstrate that dexamethasone treatment lowered S1P 1 mRNA and protein expression levels in rat mesangial cells measured by TaqMan® and Western blot analyses. The aim of this study was to investigate the relevance of IGFBP5 in cardiogenesis and cardiac remodeling and its role as a potential target for ameliorating stress-induced cardiac remodeling Methods and Results: We investigated the expression of Igfbp5 in murine cardiac tissue at different developmental stages by qPCR normalized to Tpt1 (Tumor Protein, Translationally-Controlled 1). cache = ./cache/cord-006229-7yoilsho.txt txt = ./txt/cord-006229-7yoilsho.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-162105-u0w56xrp author = Centeno, Raffy S. title = How much did the Tourism Industry Lost? Estimating Earning Loss of Tourism in the Philippines date = 2020-04-21 pages = extension = .txt mime = text/plain words = 3562 sentences = 219 flesch = 59 summary = Based on the Akaike's Information Criterion (AIC) and Root Mean Squared Error, ARIMA(1,1,1)$times$(1,0,1)$_{12}$ was identified to be the better model among the others with an AIC value of $-414.51$ and RMSE of $47884.85$. The objective of this research is to forecast the monthly earnings loss of the tourism industry during the COVID-19 pandemic by forecasting the monthly foreign visitor arrivals using Seasonal Autoregressive Integrated Moving Average. These patterns suggest a seasonal autoregressive integrated moving average (SARIMA) approach in modeling and forecasting the monthly foreign visitor arrivals in the Philippines. Akaike Information Criterion and Root Mean Squared Error were used to identify which model was used to model and forecast the monthly foreign visitor arrivals in the Philippines. 1. The order of SARIMA model used to forecast the monthly foreign visitor arrival is ARIMA (1,1,1)×(1,0,1) 12 since it produced a relatively low AIC of −414.51 and the lowest RMSE of 47884.85 using an out-of-sample data. cache = ./cache/cord-162105-u0w56xrp.txt txt = ./txt/cord-162105-u0w56xrp.txt === reduce.pl bib === id = cord-154170-7pnz98o6 author = Ponciano, Jos'e Miguel title = Poverty levels, societal and individual heterogeneities explain the SARS-CoV-2 pandemic growth in Latin America date = 2020-05-22 pages = extension = .txt mime = text/plain words = 3253 sentences = 175 flesch = 46 summary = Latin America is experiencing severe impacts of the SARS-CoV-2 pandemic, but poverty and weak public health institutions hamper gathering the kind of refined data needed to inform classical SEIR models of epidemics. Here we show that a multi-model, multi-stages modeling approach helps elucidate i) early epidemic growth in fourteen Latin-American countries ii) the role of poverty in shaping the growth rate of the number of cases and iii) the probability that the number of cases of SARS-CoV-2 exceeds any given amount within arbitrarily defined small windows of time, starting from the present. We draw on prior work in conservation biology, population dynamics and epidemiological theory to complement the current suite of deterministic epidemiological models, characterize the role of urban poverty in shaping the region's SARS-CoV-2 epidemics, and develop a methodology to generate short (5-15 days), sequentially updatable, process-based forecasts. cache = ./cache/cord-154170-7pnz98o6.txt txt = ./txt/cord-154170-7pnz98o6.txt === reduce.pl bib === === reduce.pl bib === id = cord-164964-vcxx1s6k author = Kharkwal, Himanshu title = University Operations During a Pandemic: A Flexible Decision Analysis Toolkit date = 2020-10-20 pages = extension = .txt mime = text/plain words = 7390 sentences = 381 flesch = 51 summary = There exist several models for each of these components developed at different times as the knowledge about the disease evolved, along with available data such as list of courses for Fall 2020, course selections, mask use policy, number of in person courses, and number of students, faculty, and staff on campus. For this study, we analyze the cumulative infected students due to community transmission of COVID-19 in section 3, hence the fraction of agents who leave the system (severe illness or mortality) or get recovered is immaterial for our simulations because neither of the states impact new infections. Although the current focus is on the pandemic operations of a major university, the framework is flexible enough to analyze the spread of infectious diseases involving human interactions in a big campus if any kind, given relevant models and parameters. Figure 6 : Impact of different mask types on cumulative infected students due to the community transmission of COVID-19 within university campus cache = ./cache/cord-164964-vcxx1s6k.txt txt = ./txt/cord-164964-vcxx1s6k.txt === reduce.pl bib === id = cord-126012-h7er0prc author = Diaz, Victor Hugo Grisales title = COVID-19: Forecasting mortality given mobility trend data and non-pharmaceutical interventions date = 2020-09-25 pages = extension = .txt mime = text/plain words = 3165 sentences = 156 flesch = 52 summary = We develop a novel hybrid epidemiological model and a specific methodology for its calibration to distinguish and assess the impact of mobility restrictions (given by Apple's mobility trends data) from other complementary non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19. Using the calibrated model, we estimate that mobility restrictions contribute to 47 % (US States) and 47 % (worldwide) of the overall suppression of the disease transmission rate using data up to 13/08/2020. At the same time, we evaluate the effectiveness of restrictions on mobility (i.e., walking, driving and transport) on the reduction of the disease transmission rate and hence the control of the cumulative number of infected and deceased individuals. In this contribution, our previous model [5] is extended to predict mortality and to include a term to estimate the reduction on the contagious rates given reported mobility data. cache = ./cache/cord-126012-h7er0prc.txt txt = ./txt/cord-126012-h7er0prc.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-147202-clje3b2r author = Ghanam, Ryad title = SEIRD Model for Qatar Covid-19 Outbreak: A Case Study date = 2020-05-26 pages = extension = .txt mime = text/plain words = 3867 sentences = 272 flesch = 66 summary = This work provides a tutorial on building a compartmental model using Susceptibles, Exposed, Infected, Recovered and Deaths status through time. Figure shows the plots of the Active Infections, Recovered and Deaths data for Qatar for the days since February . In addition to changes in infection rates α, impulse functions can be used to model dramatic one time shifts in transitions between states. Recall that β A is associated with the Dirac delta function for impulse to model the jump in transition rate from Exposed to Infected at day . Figure shows the model ts for Active Infections, Recovered and Deaths with posterior predictive bands. This work has demonstrated how to build a SEIRD model for the Covid-outbreak in the State of Qatar, include interventions, estimate model parameters and generate posterior predictive intervals using a Bayesian framework. cache = ./cache/cord-147202-clje3b2r.txt txt = ./txt/cord-147202-clje3b2r.txt === reduce.pl bib === === reduce.pl bib === id = cord-135004-68y19dpg author = Russo, Carlo title = Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction date = 2020-10-27 pages = extension = .txt mime = text/plain words = 3206 sentences = 161 flesch = 48 summary = Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard. Furthermore, we extended the use of lesion features extracted from the latent space of the segmentation models using the LesionEncoder framework, which replaces the classic imaging / radiomic features, such as volumetric parameters, intensity, morphologic, histogram-based and textural features, which showed high predictive power in patient OS prediction. cache = ./cache/cord-135004-68y19dpg.txt txt = ./txt/cord-135004-68y19dpg.txt === reduce.pl bib === id = cord-130967-cvbpgvso author = Dinamarca, Jos'e Luis title = Clinical concepts might be included in health-related mathematic models date = 2020-04-23 pages = extension = .txt mime = text/plain words = 1499 sentences = 77 flesch = 63 summary = It would be ideal to correct and refine the referred model, bearing in mind clinical concepts described, to take advantage of the proposal and generate a more accurate response, which can serve as an input both in the implementation of measures and in the prediction of the behavior of a pandemic like the current one. The construc on of mathema cal models that allow comprehensive approach of decision-making in situa ons of absence of robust evidence is important. This, because the authors minimize the impact of three relevant situa ons that are substan al issue of the integral process: First, in rela on to a contextual jus fica on, they use the assump on that different country-reali es are comparable. It would be ideal to correct and refine the presented model, bearing in mind the concepts described, to take advantage of the proposal and generate a more accurate response, which can serve as an input both in the implementa on of measures and in the predic on of the behavior of a pandemic like the current one. cache = ./cache/cord-130967-cvbpgvso.txt txt = ./txt/cord-130967-cvbpgvso.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-171231-m54moffr author = Habli, Ibrahim title = Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models date = 2020-05-17 pages = extension = .txt mime = text/plain words = 2233 sentences = 110 flesch = 45 summary = When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. Similar to engineered safety-critical systems, e.g. flight control software or pacemakers, the rigour and transparency with which these simulation models are developed should be proportionate to their criticality to, and influence on, public health policy -this is true for COVID-19 but also holds for other models used to support such critical decision-making. In safety-critical systems engineering it is common practice to produce an assurance case -a structured, explicit argument supported by evidence [3] . We argue that such a case has the potential to enable a wider understanding, and a critical review, of the expected benefits, limitations and assumptions that underpin the development of the simulation models and the extent to which these issues, including the different sources of uncertainty, are considered in the policy decision-making process. cache = ./cache/cord-171231-m54moffr.txt txt = ./txt/cord-171231-m54moffr.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-133917-uap1vvbm author = Grave, Mal'u title = Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction Epidemiological Models date = 2020-10-22 pages = extension = .txt mime = text/plain words = 5926 sentences = 409 flesch = 57 summary = In this work, we extend the formulation of SEIRD compartmental models to diffusion-reaction systems of partial differential equations to capture the continuous spatio-temporal dynamics of COVID-19. We implement the whole model in texttt{libMesh}, an open finite element library that provides a framework for multiphysics, considering adaptive mesh refinement and coarsening. We study a compartmental SEIRD model (susceptible, exposed, infected, recovered, deceased) that incorporates spatial spread through diffusion terms [16, 22, 8, 9, 23] . Adaptive mesh refinement and coarsening [24] can resolve population dynamics from local (street, city) to regional (district, state), providing an accurate spatio-temporal description of the infection spreading. Note that the EPIDEMIC model's dynamics does not represent the actual COVID19 dynamics since, in the case of COVID19, the exposed population may be asymptomatic and recover without becoming infected and still spread the virus. In this section we briefly introduce the Galerkin finite element formulation, the time discretization, and the the libMesh implementation, supporting adaptive mesh refinement and coarsening. cache = ./cache/cord-133917-uap1vvbm.txt txt = ./txt/cord-133917-uap1vvbm.txt === reduce.pl bib === id = cord-185125-be11h9wn author = Baldea, Ioan title = What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia? date = 2020-05-25 pages = extension = .txt mime = text/plain words = 2402 sentences = 146 flesch = 51 summary = In the unprecedented difficulty created by the COVID-19 pandemic outbreak, 1 mathematical modeling developed by epidemiologists over many decades 2-7 may make an important contribution in helping politics to adopt adequate regulations to efficiently fight against the spread of SARS-CoV-2 virus while mitigating negative economical and social consequences. As an aggravating circumstance, one should also add the difficulty not encountered in the vast majority of previous studies: how do the input parameters needed in model simulations change in time under so many restrictive measures (wearing face masks, social distancing, movement restrictions, isolation and quarantine policies, etc) unknown in the pre-COVID-19 era? Rather, we use raw epidemiological data to validate the logistic growth and straightforwardly extract the time dependent infection rate, which is the relevant model parameter for the specific case considered and makes it possible to compare how efficient different restrictive measures act to mitigate the COVID-19 pandemic, and even to get insight significant for behavioral and social science. cache = ./cache/cord-185125-be11h9wn.txt txt = ./txt/cord-185125-be11h9wn.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-167889-um3djluz author = Chen, Jianguo title = A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date = 2020-07-04 pages = extension = .txt mime = text/plain words = 12248 sentences = 768 flesch = 50 summary = The progress of CT image inspection based on AI usually includes the following steps: Region Of Interest (ROI) segmentation, lung tissue feature extraction, candidate infection region detection, and COVID-19 classification. Data sources Methods Country/region Huang [82] Yang [231] , WHO [216] CNN, LSTM, MLP, GRU China Hu [80, 81] The Paper [148] , WHO [216] MAE, clustering China Yang [233] Baidu [16] SEIR, LSTM China Fong [51, 52] NHC [139] SVM, PNN China Ai [3] WHO [54, 216] ANFIS, FPA China, USA Rizk [168] WHO [216] ISACL-MFNN USA, Italy, Spain Giuliani [62] Italy [144] EMTMGL Italy Ayyoubzadeh [14] Worldometer [218] , Google [201] LR, LSTM Iran Marini [129, 130] Swiss population Enerpol Switzerland Lai [110] IATA [126] , Worldpop [219] ML Global Punn [155] JHU CSSE [49] SVR, PR, DNN, LSTM, RNN Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model cache = ./cache/cord-167889-um3djluz.txt txt = ./txt/cord-167889-um3djluz.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-195082-7tnwkxuh author = Oodally, Ajmal title = Modeling dependent survival data through random effects with spatial correlation at the subject level date = 2020-10-12 pages = extension = .txt mime = text/plain words = 4801 sentences = 342 flesch = 61 summary = Estimates are obtained through a stochastic approximation version of the Expectation Maximization algorithm combined with a Monte-Carlo Markov Chain, for which convergence is proven. Li and Ryan (2002) developed a semi-parametric spatial frailty model with Monte Carlo simulations and Laplace approximation of a rank based marginal likelihood. Along the same lines, Lin (2012) estimated parameters of a log-normal spatial frailty model using a two-iteration approach based on an approximate likelihood function, alternating between the estimation of the regression parameter and the variance components. For instance, we use as initial values for the regression parameter β and baseline components the estimated values obtained when fitting the data by a piecewise constant proportional hazards model. Furthermore, using the villages as clusters in the marginal and shared frailty models to analyse the malaria data set has serious impact on some of the parameter estimates. Convergent stochastic algorithm for parameter estimation in frailty models using integrated partial likelihood cache = ./cache/cord-195082-7tnwkxuh.txt txt = ./txt/cord-195082-7tnwkxuh.txt === reduce.pl bib === id = cord-191876-03a757gf author = Weinert, Andrew title = Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment date = 2020-08-03 pages = extension = .txt mime = text/plain words = 3981 sentences = 223 flesch = 54 summary = We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. In response, we previously identified and determined that the OpenSky Network [4] , a community network of ground-based sensors that observe aircraft equipped with Automatic Dependent Surveillance-Broadcast (ADS-B) out, would provide sufficient and appropriate data to develop new models [5] . Additionally to address that the four aircraft registries do not contain all registered aircraft globally, a second level directory titled "Unknown" was created and populated with directories corresponding to each hour of data. This hierarchy ensures that there are no more than 1000 directories per level, as recommended by the LLSC, while organizing the data to easily enable comparative analysis between years or different types of aircraft. cache = ./cache/cord-191876-03a757gf.txt txt = ./txt/cord-191876-03a757gf.txt === reduce.pl bib === === reduce.pl bib === id = cord-229393-t3cpzmwj author = Srivastava, Ajitesh title = Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic date = 2020-04-23 pages = extension = .txt mime = text/plain words = 3671 sentences = 228 flesch = 62 summary = By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends and provide extremely accurate predictions of confirmed cases at the level of countries and states of the United States. We do so by proposing two measures: (i) Contact Reduction Score that measure how much a region has reduced transmission; (ii) and Epidemic Reduction Score that measures how much reduction in confirmed cases a region has achieved compared to a hypothetical scenario where the trends had remained the same as a reference day in the past. Applying such machine learning-based models to a finer level (from countries to states/cities) and larger scale (more 'regions' of the world) brings unique challenges in terms of unreported/noisy data and large number of model parameters, which will be explored in a future work. To incorporate the fast evolving trend of COVID-19 due to changing policies, we use weighted least squared to learn parameters β p i and δ p i from available reported data. cache = ./cache/cord-229393-t3cpzmwj.txt txt = ./txt/cord-229393-t3cpzmwj.txt === reduce.pl bib === id = cord-208252-e0vlaoii author = Calvetti, Daniela title = Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model date = 2020-05-09 pages = extension = .txt mime = text/plain words = 7814 sentences = 351 flesch = 49 summary = A Bayesian particle filtering algorithm is used to update dynamically the relevant cohort and simultaneously estimate the transmission rate as the new data on the number of new infections and disease related death become available. When we apply the model and particle filter algorithm to COVID-19 infection data from several counties in Northeastern Ohio and Southeastern Michigan we found the proposed reproduction number $R_0$ to have a consistent dynamic behavior within both states, thus proving to be a reliable summary of the success of the mitigation measures. The equilibrium value, which can be analytically calculated from the model parameters, corresponds well to the model-based estimated ratio and can be used to define a dynamically changing effective basic reproduction number R 0 for the epidemic, facilitating the comparison of model predictions with other models. cache = ./cache/cord-208252-e0vlaoii.txt txt = ./txt/cord-208252-e0vlaoii.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-219817-dqmztvo4 author = Oghaz, Toktam A. title = Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model date = 2020-04-14 pages = extension = .txt mime = text/plain words = 5198 sentences = 289 flesch = 45 summary = Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. The shortage of labeled data for text analysis has encouraged researchers to develop novel unsupervised algorithms that consider co-occurrence of words in documents as well as emerging new techniques such as exploiting an additional source of information similar to Wikipedia knowledge-based topic models [37, 38] . We believe that what differentiates a narrative model 2 from topic analysis and summarization approaches is the ability to extract relevant sequences of text relative to the corresponding series of events associated with the same topic over time. Finally, we demonstrate that our proposed model discovers time localized topics over events that approximates the distribution of user activities on social media platforms. Our focus in the present work is on probabilistic topic modeling and extractive text summarization to provide descriptive narratives for the underlying events that occurred over a period of time. cache = ./cache/cord-219817-dqmztvo4.txt txt = ./txt/cord-219817-dqmztvo4.txt === reduce.pl bib === id = cord-214774-yro1iw80 author = Srivastava, Anuj title = Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown Measures date = 2020-04-25 pages = extension = .txt mime = text/plain words = 4987 sentences = 321 flesch = 60 summary = This paper develops an agent-level simulation model, termed ALPS, for simulating the spread of an infectious disease in a confined community. From an epidemiological perspective, as large amount of infection, containment, and recovery data from the this pandemic becomes available over time, the community is currently relying essentially on simulation models to help assess situations and to evaluate options [1] . In this paper we develop a mathematical simulation model, termed ALPS, to replicate the spread of an infectious disease, such as COVID-19, in a confined community and to study the influence of some governmental interventions on final outcomes. [10] construct a detailed agent-based model for spread of infectious diseases, taking into account population demographics and other social conditions, but they do not consider countermeasures such as lockdowns in their simulations. In this section we develop our simulation model for agent-level interactions and spread of the infections across a population in a well-defined geographical domain. cache = ./cache/cord-214774-yro1iw80.txt txt = ./txt/cord-214774-yro1iw80.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-222868-k3k0iqds author = Goswami, Anindya title = Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning date = 2020-08-02 pages = extension = .txt mime = text/plain words = 9749 sentences = 520 flesch = 60 summary = Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments. While the former used only the moneyness parameter (ratio of spot and strike values) and time-to-maturity as inputs to their learning model, the latter also used historical volatility, interest rate, and lagged prices of the underlying asset and option contract. Model evaluation metrics for models trained and tested on BANKNIFTY options contract price data From the results shown in Table 5 and Table 4 , it is evident that Approach III ANN models perform significantly better than all other proposed models. Table 11 presents the values of the performance metrics, for when the pre-trained Approach III models (constructed in sections 5.2 and 5.4) are tested on 2019 − 2020 data for the NIFTY50 Index. cache = ./cache/cord-222868-k3k0iqds.txt txt = ./txt/cord-222868-k3k0iqds.txt === reduce.pl bib === id = cord-225640-l0z56qx4 author = Ghamizi, Salah title = Data-driven Simulation and Optimization for Covid-19 Exit Strategies date = 2020-06-12 pages = extension = .txt mime = text/plain words = 4956 sentences = 242 flesch = 54 summary = We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. As illustrated in Figure 1 , we propose to combine a genetic algorithm (to search for policy schedules), a deep learning model (to predict the evolution of the effective reproduction number induced by a given policy schedule) and an epidemiological model (to forecast, based on the computed effective reproduction numbers, the effect of the scheduled policies on public health over time, e.g. deaths and hospitalization occupancy). Epidemiological models predict the state of a population struck by a pandemic over time, based on state transition parameters and the evolution of the effective reproductive number, R t , of the disease. cache = ./cache/cord-225640-l0z56qx4.txt txt = ./txt/cord-225640-l0z56qx4.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-229937-fy90oebs author = Amaro, J. E. title = Global analysis of the COVID-19 pandemic using simple epidemiological models date = 2020-05-14 pages = extension = .txt mime = text/plain words = 4902 sentences = 278 flesch = 59 summary = The Death or 'D' model is a simplified version of the SIR (susceptible-infected-recovered) model, which assumes no recovery over time, and allows for the transmission-dynamics equations to be solved analytically. The evolution of the COVID-19 pandemic in several countries (China, Spain, Italy, France, UK, Iran, USA and Germany) shows a similar behavior in concord with the D-model trend, characterized by a rapid increase of death cases followed by a slow decline, which are affected by the earliness and efficiency of the lockdown effect. These results are in agreement with more accurate calculations using the extended SIR model with a parametrized solution and more sophisticated Monte Carlo grid simulations, which predict similar trends and indicate a common evolution of the pandemic with universal parameters. Additionally, D-model calculations are benchmarked with more sophisticated and reliable calculations using the extended SIR (ESIR) and Monte Carlo Planck (MCP) models -also developed in this work -which provide similar results, but allow for a more coherent spatial-time disentanglement of the various effects present during a pandemic. cache = ./cache/cord-229937-fy90oebs.txt txt = ./txt/cord-229937-fy90oebs.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-232238-aicird98 author = Ferrario, Andrea title = A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations date = 2020-10-09 pages = extension = .txt mime = text/plain words = 6857 sentences = 329 flesch = 39 summary = However, at the basis of any discussion on post-hoc explanations lies the assumption that the machine learning model whose outcomes have to be explained remains "stable" or does not change, in a given time frame of interest [2, 9, 19] . This time delay may lead to the emergence of unfavorable cases-called "unfortunate counterfactual events" (UCE) in these notes-where the retraining of the machine learning model invalidates the efforts of an individual who successfully implemented the scenario originally recommended by a feasible, actionable and possibly sparse counterfactual explanation. As noted in Section 3, the degree of certainty of counterfactual scenarios is computed as result of the machine learning model retraining, i.e., only after the generation of the corresponding counterfactual explanation (at time 0 ). In Table 1 we enumerate all possible cases that emerge from the change in time of data points, machine learning models and their outcomes, when considering the implementation of counterfactual scenarios. cache = ./cache/cord-232238-aicird98.txt txt = ./txt/cord-232238-aicird98.txt === reduce.pl bib === id = cord-225347-lnzz2chk author = Chakraborty, Tanujit title = Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges date = 2020-10-10 pages = extension = .txt mime = text/plain words = 10203 sentences = 585 flesch = 53 summary = Several statistical and machine learning methods for real-time forecasting of the new and cumulative confirmed cases of COVID-19 are developed to overcome limitations of the epidemiological model approaches and assist public health planning and policy-making [25, 91, 6, 26, 23] . As such, we aim to perform a meaningful data analysis, including the study of time series characteristics, to provide a suitable and comprehensive knowledge foundation for the future step of selecting an apt forecasting method. Five time series COVID-19 datasets for the USA, India, Russia, Brazil, and Peru UK are considered for assessing twenty forecasting models (individual, ensemble, and hybrid). Results for USA COVID-19 data: Among the single models, ARIMA (2, 1, 4) performs best in terms of accuracy metrics for 15-days ahead forecasts. Results for India COVID-19 data: Among the single models, ANN performs best in terms of accuracy metrics for 15-days ahead forecasts. cache = ./cache/cord-225347-lnzz2chk.txt txt = ./txt/cord-225347-lnzz2chk.txt === reduce.pl bib === === reduce.pl bib === id = cord-252903-pg0l92zb author = Abueg, M. title = Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state date = 2020-09-02 pages = extension = .txt mime = text/plain words = 7326 sentences = 333 flesch = 42 summary = In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. We use data at the county level to match the population, demographic, and occupational structure of the region, and calibrate the model with epidemiological data from Washington state and Google's Community Mobility Reports for a time-varying infection rate ( 21 ) . Estimated total infected percentage, total deaths, and peak hospitalized under a 50% reopening scenario (an increase of 50% of the difference between pre-lockdown and post-lockdown network interactions) at various exposure notification adoption rates for King, Pierce, and Snohomish Counties, assuming no change to social distancing after the (t) β baseline and 15 manual contact tracers per 100k people. cache = ./cache/cord-252903-pg0l92zb.txt txt = ./txt/cord-252903-pg0l92zb.txt === reduce.pl bib === === reduce.pl bib === id = cord-246317-wz7epr3n author = Wang, Wei-Yao title = EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods date = 2020-07-05 pages = extension = .txt mime = text/plain words = 3270 sentences = 229 flesch = 61 summary = We preprocess original tweet data to pre-trained language model, then fine-tune to multi-label classification model. Our study can be mainly divided into three topics, including multi-label classification, pre-trained models, and ensemble methods. Also, deep learning models are introduced to solve the multi-label classification problem, and have been proved that such models are able to extract high-level features from raw data. Secondly, a strength pre-trained language model can generate deep contextual word representation which means a word token can have several representation in different sentences. (2) Our goal aims to get better performance instead of efficiency, we use RoBERTa-base, BERT-basecased, and BERT-base-uncased to individually train language model and fine-tune to multi-label classification model. Since RoBERTa and BERT use different input formats, and our dataset has pair of sequences text and reply in each tweet, we convert input sentences based on corresponding models. cache = ./cache/cord-246317-wz7epr3n.txt txt = ./txt/cord-246317-wz7epr3n.txt === reduce.pl bib === id = cord-241351-li476eqy author = Liu, Junhua title = CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding date = 2020-05-11 pages = extension = .txt mime = text/plain words = 3860 sentences = 243 flesch = 50 summary = However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. While prior works report remarkable performance on various crisis classification tasks using NN models and word embeddings, no studies are found to leverage the most recent Natural Language Understanding (NLU) techniques, such as attention-based deep classification models [21] and document-level contextual embeddings [22] , which reportedly improve state-of-the-art performance for many challenging natural language problems from upstream tasks such as Named Entity Recognition and Part of Speech Tagging, to downstream tasks such as Machine Translation and Neural Conversation. In this work, we investigate the transformer approach for crisis classification tasks and propose CrisisBERT, a transformer-based classification model that surpasses conventional linear and deep learning models in performance and robustness. cache = ./cache/cord-241351-li476eqy.txt txt = ./txt/cord-241351-li476eqy.txt === reduce.pl bib === id = cord-244657-zp65561y author = Hawryluk, Iwona title = Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection date = 2020-09-08 pages = extension = .txt mime = text/plain words = 7250 sentences = 371 flesch = 49 summary = In this paper we introduce a variation of the TI method, here referred to as referenced TI, which computes a single model's evidence in an efficient way by using a reference density such as a multivariate normal where the normalising constant is known. We show that referenced TI, an asymptotically exact Monte Carlo method of calculating the normalising constant of a single model, in practice converges to the correct result much faster than other competing approaches such as the method of power posteriors. In each referenced TI scenario, we note that even if the reference approximation is poor, the estimate of the normalising constant based on Equation 3 remains asymptotically exact -only the speed of convergence may be reduced (provided assumptions such matching support of end-point densities remains). In the primary application discussed later, regarding relatively complex high-dimensional Bayesian hierarchical models, we use this approach to generate a reference density and normalising constant. cache = ./cache/cord-244657-zp65561y.txt txt = ./txt/cord-244657-zp65561y.txt === reduce.pl bib === id = cord-259426-qbolo3k3 author = Tadesse, Trhas title = Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020 date = 2020-10-22 pages = extension = .txt mime = text/plain words = 5279 sentences = 264 flesch = 54 summary = title: Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020 Therefore, this study investigated the predictors of COVID-19 prevention practice using the Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. Three hundred ninety-one (62.3%), 337 (53.7%), 312 (49.7), 497 (79.1%), 303 (48.2%) and 299 (52.4%) of the respondents had high perceived susceptibility, severity, benefit, barrier, cues to action and self-efficacy to COVID-19 prevention practice, respectively. Therefore, this study was aimed at assessing predictors of COVID-19 prevention practice among Higher Education employees in Addis Ababa Ethiopia using a Health Belief Model. A multicentered cross-sectional study design was used to assess predictors of COVID-19 prevention practices using a Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. The questionnaire was used to gather employees' demographic data, knowledge about COVID-19 and its prevention, Health Belief Model constructs (perceived susceptibility, perceived severity, perceived benefit, perceived barrier, and cues to action self-efficacy), and practice of COVID-19 prevention. cache = ./cache/cord-259426-qbolo3k3.txt txt = ./txt/cord-259426-qbolo3k3.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-266189-b3b36d72 author = Dignum, Frank title = Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation date = 2020-06-15 pages = extension = .txt mime = text/plain words = 7608 sentences = 416 flesch = 63 summary = In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions. Based on data from previous pandemics, initial economic policies were based on the expectation of getting back to normal within a limited amount of time, with many governments soldering the costs for the current period, it is increasingly clear that impact may be way above what governments can cope with, and a new 'normal' economy will need to be found (Bénassy-Quéré et al. In this section, we describe the epidemics, economics and social science models that are needed to support decision makers on policies concerning the COVID-19 crisis and the complexity of combining these models. We model the direct and indirect effect on the spread of the virus when schools are closed and people work from home. cache = ./cache/cord-266189-b3b36d72.txt txt = ./txt/cord-266189-b3b36d72.txt === reduce.pl bib === id = cord-248050-apjwnwky author = Vrugt, Michael te title = Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model date = 2020-03-31 pages = extension = .txt mime = text/plain words = 5112 sentences = 331 flesch = 53 summary = title: Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model We present an extended model for disease spread based on combining an SIR model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. In this article, we present a dynamical density functional theory (DDFT) [18] [19] [20] [21] for epidemic spreading that allows to model the effect of social distancing and isolation on infection numbers. While DDFT is not an exact theory (it is based on the assumption that the density is the only slow variable in the system [50, 51] ), it is nevertheless a significant improvement compared to the standard diffusion equation as it allows to incor-porate the effects of particle interactions and generally shows excellent agreement with microscopic simulations. cache = ./cache/cord-248050-apjwnwky.txt txt = ./txt/cord-248050-apjwnwky.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-255557-k0xat0u7 author = Mao, Liang title = Modeling monthly flows of global air travel passengers: An open-access data resource date = 2015-10-31 pages = extension = .txt mime = text/plain words = 4783 sentences = 254 flesch = 55 summary = title: Modeling monthly flows of global air travel passengers: An open-access data resource Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). First, we refined existing models developed by Huang et al.(2013) to a finer temporal scale and predicted the monthly air passenger flows between directly connected airports worldwide. Second, we attempt to understand the monthly WAN as a dynamic by measuring the variation of air passenger flows by month, by route, and by airport. Our model views the air passenger flow as an outcome of spatial interactions between a pair of origin and destination airports, which can be formulated into a multiplicative function of node and link characteristics, as shown in Eq. Fig. 4 shows the monthly variation of the WAN in terms of its flight routes, passenger volume, and role of airports. cache = ./cache/cord-255557-k0xat0u7.txt txt = ./txt/cord-255557-k0xat0u7.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-264136-jjtsd4n3 author = Ferstad, Johannes Opsahl title = A model to forecast regional demand for COVID-19 related hospital beds date = 2020-03-30 pages = extension = .txt mime = text/plain words = 2758 sentences = 147 flesch = 49 summary = [6, 7] In order to plan their response, hospital and public health officials need to understand how many people in their area are likely to require hospitalization for COVID-19; how these numbers compare to the number of available intensive care and acute care beds; and how to project the impact of socialdistancing measures on utilization. To facilitate use by hospital and public health officials, the model is deployed through an interactive online website that allows users to generate dynamic, static, and spatial estimates of the number and rate of severe, critical, and mortality case rates for each county or group of counties. In this report, we describe an online, real-time, interactive simulation model to facilitate local policy making and regional coordination by providing estimates of hospital bed demand and the impact of measures to slow the spread of the infection. cache = ./cache/cord-264136-jjtsd4n3.txt txt = ./txt/cord-264136-jjtsd4n3.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-264994-j8iawzp8 author = Fitzpatrick, Meagan C. title = Modelling microbial infection to address global health challenges date = 2019-09-20 pages = extension = .txt mime = text/plain words = 7105 sentences = 345 flesch = 32 summary = Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Compartmental models analysing the interplay between vaccine uptake and disease dynamics confirmed the hypothesis that increases in vaccination were a response to the pertussis infection risk 61 , and showed that incorporating this interplay can improve epidemiological forecasts. cache = ./cache/cord-264994-j8iawzp8.txt txt = ./txt/cord-264994-j8iawzp8.txt === reduce.pl bib === id = cord-274513-0biyfhab author = Baumgartner, M. T. title = Assessing the relative contributions of healthcare protocols for epidemic control: an example with network transmission model for COVID-19 date = 2020-07-22 pages = extension = .txt mime = text/plain words = 5076 sentences = 249 flesch = 46 summary = In this study, we used an individual-based age-structured network model to assess the effective roles of different healthcare protocols such as the use of personal protection equipment and social distancing at neighborand city-level scales. Our results revealed that the model was more sensitive to changes in the parameter representing the rate of contact among people from different neighborhoods, which defends the social distancing at the city-level as the most effective protocol for the control of the disease outbreak. By varying model parameters related to these protocols, we were able to discuss better scenarios considering the delay in the infection peak and lower numbers of cases, as well as activities with a low potential to boost the outbreak. Given the specified model structure, those results forecasting early wave peaks emerged under moderate to high probabilities of the individual-level exposure to SARS-CoV-2 virus (high β), in combination with higher encountering rates among people (v and k) ( Figure 1 ; Table S1 ). cache = ./cache/cord-274513-0biyfhab.txt txt = ./txt/cord-274513-0biyfhab.txt === reduce.pl bib === id = cord-263620-9rvlnqxk author = Li, Zhi-Chun title = Fifty years of the bottleneck model: A bibliometric review and future research directions date = 2020-09-30 pages = extension = .txt mime = text/plain words = 19069 sentences = 935 flesch = 48 summary = These insights cover various aspects, such as behavioral analysis (e.g., the nature of shifting peak, inefficiency of unpriced equilibria, behavioral difference of heterogeneous commuters, connection between morning and evening commutes, effects of commuter scheduling preferences), demand management (e.g., congestion / emission / parking pricing and tradable credit schemes, relationship between bottleneck congestion tolling and urban structure), and supply management (e.g., bottleneck / parking capacity expansion). The travel behavior analysis mainly focuses on the analysis of the trip and/or activity scheduling behavior of travelers through building various travel choice behavior models, such as departure time / route / parking / mode choices, morning vs evening commutes, piecewise constant vs time-varying scheduling preferences, normal congestion vs hypercongestion, homogeneous vs heterogeneous users, individual vs household, deterministic vs stochastic situations, single vs multiple bottlenecks, and analytical approach vs DTA (dynamic traffic assignment) approach. These extensions include considerations of other travel choice dimensions (e.g., route / parking / mode choices), morning-evening commutes, time-varying scheduling preferences, vehicle physical length in queue and hypercongestion, heterogeneous users, household travel and carpooling, stochastic models and information, multiple bottlenecks, and DTA-approach bottlenecks. cache = ./cache/cord-263620-9rvlnqxk.txt txt = ./txt/cord-263620-9rvlnqxk.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-262524-ununcin0 author = Bankhead, Armand title = A Simulation Framework to Investigate in vitro Viral Infection Dynamics date = 2011-12-31 pages = extension = .txt mime = text/plain words = 4245 sentences = 206 flesch = 49 summary = In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles. We also show that the model can explain the experimentally observed virus titer data and allows a deeper understanding of the infection dynamics in the in vitro experiments. Infectious: Assembled virion is being released from the host cell according to the release function (Section 2.4) By examining the experimental viral titer data shown in Figure 1 we derived temporal delay of the state transition between Containing and Infectious. p BP represents the probability of a virus-receptor binding event leading to a cell's infection by a single viral particle during a given model time step. cache = ./cache/cord-262524-ununcin0.txt txt = ./txt/cord-262524-ununcin0.txt === reduce.pl bib === id = cord-270249-miys1fve author = Liu, Xianbo title = COVID-19: data-driven dynamics, statistical and distributed delay models, and observations date = 2020-08-06 pages = extension = .txt mime = text/plain words = 8122 sentences = 407 flesch = 56 summary = Based on the parameter identification approach described in this section, the COVID-19 infection dynamics for several countries from North America, South America, Europe, and Asia is found to be captured well by using the generalized logistic function Fig. 4 . By contrast, the outcome of composite global model shown in Fig. 9 , which is comprised of 148 identified sub-models, matches the worldwide COVID-19 data with good consistency for both the total number of infection cases and daily increments. The quarantine rate ζ and the infection rate β are the only two parameters that the authors can use to control against the spreading of the virus in the improved SEIQR model with distributed time delays, given by Eqs. (iii) Based on the data-driven COVID-19 dynamics studied with the distributed delay model, it is evident the measures taken in countries such as China and South Korea were effective in dropping the reproduction number R 1 to be below 0.5. cache = ./cache/cord-270249-miys1fve.txt txt = ./txt/cord-270249-miys1fve.txt === reduce.pl bib === id = cord-266424-wchxkdtj author = Lofstedt, Jeanne title = Model to Predict Septicemia in Diarrheic Calves date = 2008-06-28 pages = extension = .txt mime = text/plain words = 4633 sentences = 228 flesch = 46 summary = 12, 16 No single laboratory test has emerged as being completely reliable for the early diagnosis of septicemia in farm animal neonates, 12, 17 therefore, various scoring systems and predictive models using easily obtainable historical, clinical, and clinicopathologic data have been developed for this purpose. For a period of time, routine blood cultures were performed on all diarrheic calves presented to the Atlantic Veterinary College Teaching Hospital regardless of whether the clinical or clinicopathologic findings indicated a diagnosis of septicemia. The prevalence of septicemia in this study was identical to that reported for calves with diarrhea, depression, and/or weakness on a veal raising facility, 14 which suggests that the predictive values of the models developed herein may be relevant to other calf populations. cache = ./cache/cord-266424-wchxkdtj.txt txt = ./txt/cord-266424-wchxkdtj.txt === reduce.pl bib === id = cord-266090-f40v4039 author = Gao, Wei title = New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system date = 2020-08-03 pages = extension = .txt mime = text/plain words = 2737 sentences = 177 flesch = 51 summary = title: New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system According to the report presented by the World Health Organization, a new member of viruses, namely, coronavirus, shortly 2019-nCoV, which arised in Wuhan, China, on January 7, 2020, has been introduced to the literature. Whereas the obtained results show the effectiveness of the theoretical method considered for the governing system, the results also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. The obtained results show the effectiveness of the theoretical method considering the governing system and also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. In this subsection, by using VIM we numerically investigate the Bats-Hosts-Reservoir-People coronavirus model. Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative Application of variational iteration method to nonlinear differential equations of fractional order cache = ./cache/cord-266090-f40v4039.txt txt = ./txt/cord-266090-f40v4039.txt === reduce.pl bib === id = cord-273815-7ftztaqn author = Gupta, R. K. title = Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date = 2020-07-26 pages = extension = .txt mime = text/plain words = 4918 sentences = 288 flesch = 40 summary = We also assessed the discrimination of each candidate model for standardised outcomes of: (a) our composite endpoint of clinical deterioration; and (b) mortality, across a range of pre-specified time horizons from admission (7 days, 14 days, 30 days and any time during hospital admission), by calculating time-dependent AUROCs (with cumulative sensitivity and dynamic specificity) 18 . In order to further benchmark the performance of candidate prognostic models, we then computed AUROCs for a limited number of univariable predictors considered to be of highest importance a priori, based on clinical knowledge and existing data, for prediction of our composite endpoints of clinical deterioration and mortality (7 days, 14 days, 30 days and any time during hospital admission). We compared net benefit for each prognostic model (for its original intended endpoint) to the strategies of treating all patients, treating no patients, and using the most discriminating univariable predictor for either deterioration (i.e. oxygen saturation on air) or mortality (i.e. patient age) to stratify treatment (Supplementary Figure 9 ). cache = ./cache/cord-273815-7ftztaqn.txt txt = ./txt/cord-273815-7ftztaqn.txt === reduce.pl bib === id = cord-258316-uiusqr59 author = Spil, Ton A.M. title = Are serious games too serious? Diffusion of wearable technologies and the creation of a diffusion of serious games model date = 2020-08-18 pages = extension = .txt mime = text/plain words = 7512 sentences = 401 flesch = 53 summary = A key theoretical contribution of this research is the identification of habit as a potential dependent variable for the intention to use wearables and the development of a diffusion model for serious games. We question the actual adoption and effectiveness of wearables and serious games -the principle of revealing and challenge prevailing beliefs and social practices -by making use of the IT adoption model as discussed in the previous section based on insights from innovation and adoption researchers like Davis, Bagozzi, and Warshaw (1989) , DeLone and McLean (1993) , Rogers (1983) and Venkatesh et al. We study how the adoption of serious wearable games can be improved -the principle of taking a value position -in order to help improve health on both an individual and societal level -the principles of individual emancipation and improvements in society -and try to improve diffusion models for serious games by identifying habit as a potential dependent variable for the intention to use wearables -the principle of improvements in social theories. cache = ./cache/cord-258316-uiusqr59.txt txt = ./txt/cord-258316-uiusqr59.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-264408-vk4lt83x author = Ruiz, Sara I. title = Animal Models of Human Viral Diseases date = 2017-06-23 pages = extension = .txt mime = text/plain words = 34464 sentences = 1865 flesch = 47 summary = Well-developed animal models are necessary to understand disease progression, pathogenesis, and immunologic responses to viral infections in humans. NHPs including marmosets, cotton-top tamarins, and rhesus macaques infected with Norwalk virus are monitored for the extent of viral shedding; however, no clinical disease is observed in these models. Intracerebral and IN routes of infection resulted in a fatal disease that was highly dependent on dose while intradermal (ID) and subQ inoculations caused only 50% fatality in mice regardless of the amount of virus (liu et al., 1970) . Ferrets infected with Hendra or Nipah virus display the same clinical disease as seen in the hamster model and human cases (Bossart et al., 2009; Pallister et al., 2011) . Characterization studies with IFNAr −/− mice challenged with different routes (IP, IN, IM, and subQ) showed that CCHFV causes acute disease with high viral loads, pathology in liver and lymphoid tissues, increased proinflammatory response, severe thrombocytopenia, coagulopathy, and death, all of which are characteristics of human disease . cache = ./cache/cord-264408-vk4lt83x.txt txt = ./txt/cord-264408-vk4lt83x.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-276218-dcg9oq6y author = Kim, Jihoon title = Human organoids: model systems for human biology and medicine date = 2020-07-07 pages = extension = .txt mime = text/plain words = 10681 sentences = 496 flesch = 38 summary = The use of classical cell line and animal model systems in biomedical research during the late twentieth and early twenty-first centuries has been successful in many areas, such as improving our understanding of cellular signalling pathways, identifying potential drug targets and guiding the design of candidate drugs for pathologies including cancer and infectious disease. The advent of human induced pluripotent stem cell (iPSC) technology and diverse human AdSC culture methods has made it possible, for the first time, to generate laboratory models specific to an individual 32 . A number of studies have used 3D human stem cell-derived systems, including neurosphere culture and brain organoid models, to reveal the effect of ZIKV infection on human brain development 80, 81 . cache = ./cache/cord-276218-dcg9oq6y.txt txt = ./txt/cord-276218-dcg9oq6y.txt === reduce.pl bib === id = cord-269873-4hxwo5kt author = R., Mohammadi title = Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images date = 2020-10-01 pages = extension = .txt mime = text/plain words = 3378 sentences = 199 flesch = 52 summary = OBJECTIVE: This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. MATERIAL AND METHODS: In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. To this end, the present study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection and diagnosis of COVID-19 infection in chest X-rays. In this study, a CNN-based model was used to detect COVID-19 from the chest X-ray images. In this study, we proposed four pre-trained deep CNN models, including VGG-16, VGG-19, MobileNet, and InceptionResNetV2 for discriminating COVID-19 cases from chest X-ray images. In this study, we presented four pre-trained deep CNN models such as VGG16, VGG19, MobileNet, and InceptionResNetV2 are used for transfer learning to detect and classify COVID-19 from chest radiography. cache = ./cache/cord-269873-4hxwo5kt.txt txt = ./txt/cord-269873-4hxwo5kt.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-277128-g90hp8j7 author = Rajendran, Sukumar title = Accessing Covid19 Epidemic Outbreak in Tamilnadu and the Impact of Lockdown through Epidemiological Models and Dynamic systems date = 2020-09-17 pages = extension = .txt mime = text/plain words = 2783 sentences = 175 flesch = 53 summary = To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R(0)) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. The model utilizes population dynamics and conditional dependencies such as new cases, deaths, social distancing, and herd immunity over a stipulated timeperiod to simulate probable outcomes. The factors governing the spread are infectious agents, modes of transmission, susceptibility, and immunity (Chowell et al., 2006 The case fatality rate(CFR) is highly variable and increases with severe respiratory symptoms in adults with comorbid conditions . The mapping of transmission of covid19 is done through contact tracing, thereby isolating individuals infected by the epidemic at different epicentres of the society denoted by . cache = ./cache/cord-277128-g90hp8j7.txt txt = ./txt/cord-277128-g90hp8j7.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-268779-qbn3i2nq author = Alrasheed, Hend title = COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis date = 2020-10-23 pages = extension = .txt mime = text/plain words = 10876 sentences = 628 flesch = 53 summary = In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. We aimed to match the model simulations with empirical data and then used the model to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to predict the percentage of individuals that must be vaccinated to stop the outbreak (when a vaccine becomes available). Volz [35] modeled SIR dynamics on a static random network, which represents the population structure of susceptible and infected individuals and their contact patterns with an arbitrary degree distribution. cache = ./cache/cord-268779-qbn3i2nq.txt txt = ./txt/cord-268779-qbn3i2nq.txt === reduce.pl bib === id = cord-268142-lmkfxme5 author = Schafrum Macedo, Aline title = Animal modeling in bone research—Should we follow the White Rabbit? date = 2019-09-26 pages = extension = .txt mime = text/plain words = 3706 sentences = 296 flesch = 49 summary = title: Animal modeling in bone research—Should we follow the White Rabbit? Our aim here is to provide a broad overview of animal modeling and its ethical implications, followed by a narrower focus on bone research and the role rabbits are playing in the current scenario. 12 Five key bioethical points are considered when assessing the moral status of animal subjects in research: the presence of life, the ability to feel and perceive stimuli, the level of cognitive behavior, the degree of sociability, and the ability to proliferate. Animal models have taught us much about bone disorders and have been central to developing many treatments throughout history. 8, 17, 51 Rabbits are appealing models for bone research. Rabbits have potential as bone models but conclusive studies are still lacking. Animal models for implant biomaterial research in bone: a review The laboratory rabbit: an animal model of atherosclerosis research Osteoporosis-bone remodeling and animal models cache = ./cache/cord-268142-lmkfxme5.txt txt = ./txt/cord-268142-lmkfxme5.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-269559-gvvnvcfo author = Kergaßner, Andreas title = Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany date = 2020-08-03 pages = extension = .txt mime = text/plain words = 4492 sentences = 257 flesch = 54 summary = Here, we combine a spatially resolved county-level infection model for Germany with a memory-based integro-differential approach capable of directly including medical data on the course of disease, which is not possible when using traditional SIR-type models. Based on the history of S, other quantities and subgroups can be determined directly from including medical data on the various courses and infectiousness levels of the disease via corresponding integration weights: We distinguish between the states infectious γ I , symptomatic γ S , tested and quarantined γ Q , hospitalized γ H , in intensive care γ ICU , recovered γ R and deceased γ D . Figure 6 shows the model predicted spatial distribution at county resolution of infectious, symptomatic, hospitalized, and patients in intensive care, following from the individual disease courses in Fig. 1 . cache = ./cache/cord-269559-gvvnvcfo.txt txt = ./txt/cord-269559-gvvnvcfo.txt === reduce.pl bib === === reduce.pl bib === id = cord-280064-rz8cglyt author = Gwizdałła, Tomasz title = Viral disease spreading in grouped population date = 2020-08-27 pages = extension = .txt mime = text/plain words = 6851 sentences = 392 flesch = 56 summary = In the section devoted to the presentation of results, we concentrate on the epidemic curves, which are presented in two forms, i.e., the number of new cases and the number of recovered persons (in the absence of the mortality rate), and on the analysis of intervention, considered as the minimization of the number of contacts between neighbors in the network. This continuous approach enables easy calculation of one of the most interesting values describing the potential effect of an outbreak, i.e., the basic reproduction number, which is a simple function of the ODE parameters: Although continuous models based on the ODEs give many interesting and practical results, it is well known [3] that there exists a large stochastic effect in the epidemic process. Considering once more the effect of intervention (see plots (b) and (d) in Fig. 3 and 4) , we can observe that, with intervention included, the duration of the epidemic does not strongly depend on the parameters of the model. cache = ./cache/cord-280064-rz8cglyt.txt txt = ./txt/cord-280064-rz8cglyt.txt === reduce.pl bib === id = cord-283678-xdma6vyo author = Séférian, Roland title = Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6 date = 2020-08-18 pages = extension = .txt mime = text/plain words = 10700 sentences = 570 flesch = 44 summary = PURPOSE OF REVIEW: The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). SUMMARY: Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP). Our review of available ESMs suggests that the current generation of marine biogeochemical models has not much evolved toward comprehensive couplings between Earth system components and ocean biogeochemistry or toward improved treatment of biophysical and biogeochemical feedback with respect to their predecessors (F1 and F4 in Fig. 1 ). cache = ./cache/cord-283678-xdma6vyo.txt txt = ./txt/cord-283678-xdma6vyo.txt === reduce.pl bib === id = cord-289325-jhokn5bu author = Lachiany, Menachem title = Effects of distribution of infection rate on epidemic models date = 2016-08-11 pages = extension = .txt mime = text/plain words = 6823 sentences = 446 flesch = 59 summary = We show that when variability in infection rates is included in standard susciptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) models the total number of infected individuals in the late dynamics can be orders lower than predicted from the early dynamics. As will be further shown, the initial dynamics are only affected by the first moment of the distribution (the expected values of β), while the total number of infected individuals during the outbreak in the SIR model or the steady-state infected fraction in the SIS model can be strongly affected by the following moments. Thus, in some distributions, it is impossible to predict the "outcome" of the epidemics from the observed initial dynamics and the resulting estimate of Ro. To examine the behavior of the infected class as a function of time, we developed a moment closure scheme, and we use the following notations: cache = ./cache/cord-289325-jhokn5bu.txt txt = ./txt/cord-289325-jhokn5bu.txt === reduce.pl bib === id = cord-290952-tbsccwgx author = Ullah, Saif title = Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study date = 2020-07-03 pages = extension = .txt mime = text/plain words = 6464 sentences = 357 flesch = 51 summary = In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. cache = ./cache/cord-290952-tbsccwgx.txt txt = ./txt/cord-290952-tbsccwgx.txt === reduce.pl bib === === reduce.pl bib === id = cord-283092-t3yqsac3 author = Shah, Kamal title = Qualitative Analysis of a Mathematical Model in the Time of COVID-19 date = 2020-05-25 pages = extension = .txt mime = text/plain words = 3345 sentences = 235 flesch = 60 summary = In this article, a qualitative analysis of the mathematical model of novel corona virus named COVID-19 under nonsingular derivative of fractional order is considered. Under the new nonsingular derivative, we, first of all, establish some sufficient conditions for existence and uniqueness of solution to the model under consideration. For the semianalytical results, we extend the usual Laplace transform coupled with Adomian decomposition method to obtain the approximate solutions for the corresponding compartments of the considered model. From Figure 1 , we see that at when the rate of healthy immigrants is zero, it means that protection rate is increasing and hence the population of infected class is decreasing while the population of healthy class is increasing at different rates due to fractional order derivative by evaluating the solution up to twenty terms via using MATAB. cache = ./cache/cord-283092-t3yqsac3.txt txt = ./txt/cord-283092-t3yqsac3.txt === reduce.pl bib === id = cord-285897-ahysay2l author = Wu, Guangyao title = Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study date = 2020-07-02 pages = extension = .txt mime = text/plain words = 3803 sentences = 178 flesch = 42 summary = OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Therefore, our objective is to develop and validate a prognostic machine-learning model based on clinical, laboratory, and radiological variables of COVID-19 patients at hospital admission for severity risk assessment during hospitalization, and compare the performance with that of PSI as a representative clinical assessment method. This international multicenter study analyzed individually and in combination, clinical, laboratory and radiological characteristics for COVID-19 patients at hospital admission, to retrospectively develop and prospectively validate a prognostic model and tool to assess the severity of the illness, and its progression, and to compare these with PSI scoring. cache = ./cache/cord-285897-ahysay2l.txt txt = ./txt/cord-285897-ahysay2l.txt === reduce.pl bib === === reduce.pl bib === id = cord-274732-mh0xixzh author = Faizal, W.M. title = Computational fluid dynamics modelling of human upper airway: a review date = 2020-06-26 pages = extension = .txt mime = text/plain words = 8820 sentences = 499 flesch = 44 summary = RESULTS: This review found that the human upper airway was well studied through the application of computational fluid dynamics, which had considerably enhanced the understanding of flow in HUA. However, to predict the flow accurately in the study of the upper airway, the selected numerical method must have the capability to simulate the low-Reynolds number turbulence model in a complex geometry [51] . This article presents review on the experimental and numerical method such as, computational fluid dynamics approach, and its application in the analysis of human upper airway (HUA), including the fluid-structure interaction. Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow Fluid structure interaction simulations of the upper airway in obstructive sleep apnea patients before and after maxillomandibular advancement surgery cache = ./cache/cord-274732-mh0xixzh.txt txt = ./txt/cord-274732-mh0xixzh.txt === reduce.pl bib === id = cord-276782-3fpmatkb author = Garbey, M. title = A Model of Workflow in the Hospital During a Pandemic to Assist Management date = 2020-05-02 pages = extension = .txt mime = text/plain words = 5717 sentences = 295 flesch = 61 summary = The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. In some hospitals, the floor might be shared by patients who are 92 recovering from COVID-19 and palliative care patients.Despite this, we will separate 93 these functional units in our model to clarify the workflow process according to what 94 each patient stage requires in terms of resources and time to deliver adequate care. Number of Staff required at each care unit per beds in reference to the Workflow of Figure 1 Let us describe the data set we are using to construct our model. cache = ./cache/cord-276782-3fpmatkb.txt txt = ./txt/cord-276782-3fpmatkb.txt === reduce.pl bib === id = cord-281543-ivhr2no3 author = Richardson, Eugene T title = Pandemicity, COVID-19 and the limits of public health ‘science’ date = 2020-04-17 pages = extension = .txt mime = text/plain words = 1881 sentences = 132 flesch = 56 summary = 17 In the case of Ebola outbreak in West Africa, epidemiologists attributed amplified transmission to local populations' beliefs in misinformation or their 'strange' funerary practices-in essence, diverting the public's gaze from legacies of the transatlantic slave trade (or Maafa), 18 colonialism, 19 indirect rule, 20 structural adjustment 21 and extractive foreign companies as determinants. 40 As they start to sift back through the determinative web of human rights abuses-that is, the pathologies of power 41that set the stage for these health inequalities, they may begin to see that they contribute a great deal to the production and reproduction of structural injustice because of the social position they occupy and the violence that has been committed in their names. Mathematical modeling of the West Africa Ebola epidemic cache = ./cache/cord-281543-ivhr2no3.txt txt = ./txt/cord-281543-ivhr2no3.txt === reduce.pl bib === id = cord-288183-pz3t29a7 author = McKibbin, Warwick J. title = Chapter 15 A Global Approach to Energy and the Environment The G-Cubed Model date = 2013-12-31 pages = extension = .txt mime = text/plain words = 20679 sentences = 1069 flesch = 53 summary = Macroeconomic policy issues in Japan have been examined using G-Cubed by McKibbin (2002) and Callen and McKibbin (2003) where the experience of Japan during the 1990s was captured by the model as a serious of policy errors particularly in announcing fiscal expansion and generating crowding out through asset markets, but then not delivering the fiscal spending causing a persistent downward drop in GDP; in India by McKibbin and Singh (2003) where nominal income targeting was shown to be a far better monetary regime than inflation targeting given the prevalence of supply side rather than demand-side shocks in the Indian economy; in China by McKibbin and Tang (2000) and McKibbin and Huang (2000) where financial reforms where found to have profound effects on economic growth and the balance of payments adjustment but that a loss in confidence in China could devastate economic growth; and in Asia in McKibbin and Le (2004) and McKibbin and Chanthapun (1999) where flexible exchange rate regimes were found to be far better at insulating East Asian economies against global economic shocks that pegging to either the US dollar or a common Asia currency. cache = ./cache/cord-288183-pz3t29a7.txt txt = ./txt/cord-288183-pz3t29a7.txt === reduce.pl bib === === reduce.pl bib === id = cord-281122-dtgmn9e0 author = Ribeiro, Matheus Henrique Dal Molin title = Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods date = 2020-09-22 pages = extension = .txt mime = text/plain words = 4956 sentences = 268 flesch = 72 summary = Hence, there is a lack of discussion concerning the predictive 130 capacity of machine learning-based approaches for diseases such as measles, meningitis, 131 and chikungunya on the forecast task; 132 • In the modeling aspect, only four papers focused on ensemble approaches such as bag-133 ging and boosting or models combined by average. Also, σ 2 e and σ 2 θ are the variance of errors, weights and biases, respectively; Considering that QRF uses the quantiles in the prediction process, the α-quantile of CDF 299 is stated as the probability that the number of notifications is lower than Q α if the given p t 300 is equal to α, where the estimate of α is stated as follows: The PLS regression approach is a technique to analyze multivariate data, in which the 306 aim is to relate one or two output variables (Y) with several inputs (X). cache = ./cache/cord-281122-dtgmn9e0.txt txt = ./txt/cord-281122-dtgmn9e0.txt === reduce.pl bib === === reduce.pl bib === id = cord-280683-5572l6bo author = Liu, Laura title = Panel forecasts of country-level Covid-19 infections() date = 2020-10-16 pages = extension = .txt mime = text/plain words = 7198 sentences = 494 flesch = 61 summary = We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. In addition to forecasts from our panel data model, we also consider forecasts based on location-level time series estimates of our trend-break model and a simple SIR model. Once we decompose the set of locations into those that experienced the Covid-19 outbreak early (prior to 2020-03-28) and those that experience the outbreak later on, then we find some evidence that for the late group the panel density forecasts are more accurate than the time-series forecasts. First, as in Section 4, we generate time-series forecasts based on the trend-break model (3) for each location. cache = ./cache/cord-280683-5572l6bo.txt txt = ./txt/cord-280683-5572l6bo.txt === reduce.pl bib === id = cord-288342-i37v602u author = Wang, Zhen title = Coupled disease–behavior dynamics on complex networks: A review date = 2015-07-08 pages = extension = .txt mime = text/plain words = 15810 sentences = 776 flesch = 38 summary = Incorporating adaptive behavior into a model of disease spread can provide important insight into population health outcomes, as the activation of social distancing and other nonpharmaceutical interventions (NPIs) have been observed to have the ability to alter the course of an epidemic [50] [51] [52] . The authors studied their coupled "disease-behavior" model in well-mixed populations, in square lattice populations, in random network populations, and in SF network populations, and found that population structure acts as a "double-edged sword" for public health: it can promote high levels of voluntary vaccination and herd immunity given that the cost for vaccination is not too large, but small increases in the cost beyond a certain threshold would cause vaccination to plummet, and infections to rise, more dramatically than in well-mixed populations. The first mathematical models studied the adaptive dynamics of disease-behavior responses in the homogeneously mixed population, assuming that individuals interact with each other at the same contact rate, without restrictions on selecting potential partners. cache = ./cache/cord-288342-i37v602u.txt txt = ./txt/cord-288342-i37v602u.txt === reduce.pl bib === === reduce.pl bib === id = cord-292699-855am0mv author = Engbert, Ralf title = Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics date = 2020-04-17 pages = extension = .txt mime = text/plain words = 5369 sentences = 363 flesch = 54 summary = The key motivation of the current study was to apply sequential data assimilation of the stochastic SEIR model to estimate the contact parameter. An approximative instantaneous negative log-likelihood L(t k , β) of the contact parameter β at observation time t k is obtained from the ensemble Kalman filter (see Model inference based on sequential data assimilation). Forward iteration with the estimated time-varying contact parameter show that the slope of the epidemic curve is approximately reproduced by the model (Fig. 3a ,c; grey lines indicate the ensemble of simulated trajectories; blue points are observed data). In scenario I, we started with the adapted ensemble of internal model states after data assimilation (April 4th) and iterated the model forward with the mean contact parameter estimated in the week March 29th to April 4th after implementation of interventions (Fig. 4 , green area). cache = ./cache/cord-292699-855am0mv.txt txt = ./txt/cord-292699-855am0mv.txt === reduce.pl bib === id = cord-289447-d93qwjui author = Helmy, Mohamed title = Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering date = 2020-10-09 pages = extension = .txt mime = text/plain words = 7405 sentences = 359 flesch = 38 summary = Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. On the other hand, Jervis et al implemented an ML algorithm to model the bacterial ribosome binding sites (RBSs) sequence-phenotype relationship and accurately predicted the optimal high-producers, an approach that directly apply on wide range of metabolic engineering applications [106] . To understand the key regulatory or emergent bottleneck scenarios that limit their industrial applicability, they undertook a large scale -omics based systems biology approach where they performed time-series proteomics and metabolomics measurements, and analyzed the resultant high-throughput data using statistical analytics and genome-scale modeling. Although genome annotation, both structural and functional, affects most of the biomedical research aspects, it has a special impact on metabolic engineering in general and applications in food industry in particular. cache = ./cache/cord-289447-d93qwjui.txt txt = ./txt/cord-289447-d93qwjui.txt === reduce.pl bib === === reduce.pl bib === id = cord-277237-tjsw205c author = Hernandez Vargas, Esteban Abelardo title = In-host Modelling of COVID-19 Kinetics in Humans date = 2020-03-30 pages = extension = .txt mime = text/plain words = 3609 sentences = 273 flesch = 58 summary = Based on the target cell model, COVID-19 infecting time between susceptible cells (mean of 30 days approximately) is much slower than those reported for Ebola (about 3 times slower) and influenza (60 times slower). The best model to fit the data was including immune responses, which suggest a slow cell response peaking between 5 to 10 days post onset of symptoms. [29] improve the fitting respect to the target cell model (Table 2 ) even when very long eclipse phase periods 121 are assumed (e.g 100 days), implying that this mechanism could be negligible on COVID-19 infection. Here, based on the results of the 159 target cell model in Table 2 , we found that COVID-19 infecting time between cells (mean of 30 days 160 approximately) would be slower than those reported for Ebola (about 3 times slower) and influenza (60 161 times slower). Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses cache = ./cache/cord-277237-tjsw205c.txt txt = ./txt/cord-277237-tjsw205c.txt === reduce.pl bib === id = cord-283907-ev1ghlwl author = Cao, Lingyan title = Electrical load prediction of healthcare buildings through single and ensemble learning date = 2020-11-30 pages = extension = .txt mime = text/plain words = 8756 sentences = 418 flesch = 44 summary = Therefore, in this paper, the authors propose a one day-ahead electrical load forecasting model based on single and ensemble machine learning algorithms. In the present study, electrical load forecasting models of healthcare buildings are developed based on single and ensemble machine learning algorithms by taking account multi-factors simultaneously. To address this gap, this study takes into account the occupancy of outpatients, emergency patients, and inpatients and employs single and ensemble machine learning algorithms to predict the electric load demand of healthcare buildings. It can be seen that the electric load prediction for the healthcare buildings includes three steps: (1) Identify the relevant features and gather data, (2) Train single and ensemble learning models with prepared dataset, and (3) Compare the prediction performance of different models. Electrical load forecasting is naturally considered to be a regression problem in machine learning, aiming to accurately predict the energy demand of buildings based on its relationship with a given set of independent input variables. cache = ./cache/cord-283907-ev1ghlwl.txt txt = ./txt/cord-283907-ev1ghlwl.txt === reduce.pl bib === id = cord-295116-eo887olu author = Chimmula, Vinay Kumar Reddy title = Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() date = 2020-05-08 pages = extension = .txt mime = text/plain words = 4708 sentences = 252 flesch = 50 summary = title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Recurrent LSTM networks has capability to address the limitations of traditional time series forecasting techniques by adapting nonlinearities of given COVID-19 dataset and can result state of the art results on temporal data. Accord-COVID-19 forecasting using LSTM Networks ing to this second model within 10 days, Canada is expected to see exponential growth of confirmed cases. cache = ./cache/cord-295116-eo887olu.txt txt = ./txt/cord-295116-eo887olu.txt === reduce.pl bib === id = cord-293893-ibca88xu author = Xie, Tian title = Parallel Evolution and Response Decision Method for Public Sentiment based on System Dynamics date = 2020-05-23 pages = extension = .txt mime = text/plain words = 9806 sentences = 441 flesch = 42 summary = This method is structure-dependent rather than data-dependent and can be implemented in real-time, which makes it helpful to simulate, analyze and guide the evolution processes of dynamic public sentiment in the case of lack of historical knowledge on less-frequently occurring original events. The rationality of the cultivated SD model and the consistency between its simulation results and the real evolution trends of the public sentiment are essential to achieve scenario rehearsal and response effectively in the decision-making processes (Thompson et al., 2016) . In a decision-making process for a non-duplicated public sentiment triggered by a major public health incident or a large-scale project, because the decision makers lack prior data and knowledge, the parameters of the initial equations of the 1-general SD model can be referenced from the developed models of historical cases which are similar with the current event in type, system structure and situation. cache = ./cache/cord-293893-ibca88xu.txt txt = ./txt/cord-293893-ibca88xu.txt === reduce.pl bib === === reduce.pl bib === id = cord-302336-zj3oixvk author = Clift, Ash K title = Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study date = 2020-10-21 pages = extension = .txt mime = text/plain words = 7352 sentences = 320 flesch = 44 summary = 13 The use of primary care datasets with linkage to registries such as death records, hospital admissions data, and covid-19 testing results represents a novel approach to clinical risk prediction modelling for covid-19. Patients entered the cohort on 24 January 2020 (date of first confirmed case of covid-19 in the UK) and were followed up until they had the outcome of interest or the end of the first study period (30 April 2020), which was the date up to which linked data were available at the time of the derivation of the model, or the second time period (1 May 2020 until 30 June 2020) for the temporal cohort validation. 25 D statistics (a discrimination measure that quantifies the separation in survival between patients with different levels of predicted risks) and Harrell's C statistics (a discrimination metric that quantifies the extent to which people with higher risk scores have earlier events) were evaluated at 97 days (the maximum followup period available at the time of the derivation of the model) and 60 days for the second temporal validation, with corresponding 95% confidence intervals. cache = ./cache/cord-302336-zj3oixvk.txt txt = ./txt/cord-302336-zj3oixvk.txt === reduce.pl bib === id = cord-293333-mqoml9o5 author = Scharbarg, Emeric title = From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves date = 2020-10-03 pages = extension = .txt mime = text/plain words = 5785 sentences = 330 flesch = 59 summary = The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. In particular apart the high transmission rate, other two aspects were immediately pointed out by the physicians which did strongly influence the diffusion of the disease and the medical resources: first it was estimated that a large delay of time (10 to 14 days) is present between the moment in which a person becomes infected and can infect, and the instant in which symptoms become evident and the person is isolated and sent to quarantine. The subsystem (2) consisting by I q , R and D q is then further discussed in Section 4: a group of people who are aware of their infection define the flow of admissions in a local hospital and are split into two populations, the patients admitted in conventional hospitalization and the patients admitted in intensive care. cache = ./cache/cord-293333-mqoml9o5.txt txt = ./txt/cord-293333-mqoml9o5.txt === reduce.pl bib === id = cord-299312-asc120pn author = Khoshnaw, Sarbaz H.A. title = A Quantitative and Qualitative Analysis of the COVID–19 Pandemic Model date = 2020-05-25 pages = extension = .txt mime = text/plain words = 2083 sentences = 133 flesch = 39 summary = Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. Interestingly, we identify that 27 transition rates between asymptomatic infected with both reported and unreported 28 symptomatic infected individuals are very sensitive parameters concerning model variables 29 This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. One of the identified key parameters is the transmission rate 515 between asymptomatic infected and reported symptomatic individuals. cache = ./cache/cord-299312-asc120pn.txt txt = ./txt/cord-299312-asc120pn.txt === reduce.pl bib === id = cord-285435-fu90vb2z author = Björklund, Tua A. title = Expanding entrepreneurial solution spaces in times of crisis: Business model experimentation amongst packaged food and beverage ventures date = 2020-11-30 pages = extension = .txt mime = text/plain words = 6817 sentences = 288 flesch = 37 summary = Examining 844 social media posts of 66 ventures between March and May 2020 and interviewing 17 of these ventures, we found ventures to experiment with new business model variations, which not only expanded their set of solutions directly, but resulted in action-based learning leading to longer-term changes and increased capabilities for subsequent value creation. The current study sheds light into how entrepreneurs can experiment with new opportunities and business models to expand entrepreneurial solution spaces in such times of wide-spread collective crisis, examining the activities of packaged food and beverage ventures during the Covid-19 pandemic in Finland. Although further research into the post-crisis effects of such solution space expansions, as well as if, when and how new capabilities are subsequently put to use for business model innovation is still needed, at its best, entrepreneurial experimentation can create new value, capabilities and lasting resilience for both ventures and those in their ecosystem. cache = ./cache/cord-285435-fu90vb2z.txt txt = ./txt/cord-285435-fu90vb2z.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-308652-i6q23olv author = Cobos-Sanchiz, David title = The Importance of Work-Related Events and Changes in Psychological Distress and Life Satisfaction amongst Young Workers in Spain: A Gender Analysis date = 2020-06-30 pages = extension = .txt mime = text/plain words = 7149 sentences = 339 flesch = 50 summary = The aim of this paper is therefore to understand the importance of work-related events and changes experienced in the last year in psychological distress and life satisfaction for young people in Spain, including satisfaction with the job role, self-esteem, and emotional and instrumental social support in the prediction model, all of which will be assessed by analyzing men and women separately. To test the hypotheses and determine the importance of the number of work-related events and changes, job satisfaction, self-esteem and social support in psychological distress, and life satisfaction amongst men and women, hierarchical multiple regression analyses were made. Model 3, with all the independent variables in the equation, predicted 28% In Table 1 are the correlation coefficients between the age, level of studies, number of work-related events and changes, job satisfaction, self-esteem and social support with the psychological distress, and life satisfaction amongst men and women. cache = ./cache/cord-308652-i6q23olv.txt txt = ./txt/cord-308652-i6q23olv.txt === reduce.pl bib === id = cord-296388-ayfdsn07 author = Maziarz, Mariusz title = Agent‐based modelling for SARS‐CoV‐2 epidemic prediction and intervention assessment: A methodological appraisal date = 2020-08-21 pages = extension = .txt mime = text/plain words = 4560 sentences = 240 flesch = 41 summary = CONCLUSIONS: Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference‐making evidence. While the 2009 swine flu pandemic was the motivation for constructing AceMod, the model was not intended to accurately represent the outbreak of the H1N1 strain, but rather as a generalized framework for studying how an infectious disease spreads through the social interactions of Australians. In cases like the current pandemic, effective interventions may best be aimed at the societal level and therefore mechanistic models that integrate social factors, human behaviour and biological aspects (something that the ABM discussed here attempts to do) are arguably best suited for providing understanding and suggesting policy decisions. 10 Our claim that AceMod calibrated for SARS-CoV-2 bears similarity to the actual mechanism of the epidemic depends on the accuracy of the empirical results used as an input for this model. Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal cache = ./cache/cord-296388-ayfdsn07.txt txt = ./txt/cord-296388-ayfdsn07.txt === reduce.pl bib === id = cord-296565-apqm0i58 author = Togati, Teodoro Dario title = General Theorizing and Historical Specificity in the ‘Keynes Versus the Classics’ Dispute’ date = 2020-10-06 pages = extension = .txt mime = text/plain words = 10625 sentences = 397 flesch = 44 summary = For example, both Keynes's theory and standard macro share a common feature: while accepting a universalistic approach to theorizing-stressing behavioural hardcore 'drivers' such as agents' optimizing choices or psychological laws-they are not truly universal in their scope or object of analysis: both theories also include 1 One major question which arises today, for example, is whether the current 'consensus' macro is general enough to accommodate the post-Covid-19 scenario. 17 Based on the ADM and its sophisticated institutional setting, this hard core plays a key role in modern economics because it turns 'choice theory' into the only possible 'natural laws'-i.e. those forms of agents' behaviour that are true whatever the context-generating the basic rules of 'grammar' of economics, from which serious theorists cannot simply depart if they want to be understood by their peers. cache = ./cache/cord-296565-apqm0i58.txt txt = ./txt/cord-296565-apqm0i58.txt === reduce.pl bib === === reduce.pl bib === id = cord-297517-w8cvq0m5 author = Toğaçar, Mesut title = COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches date = 2020-05-06 pages = extension = .txt mime = text/plain words = 4678 sentences = 320 flesch = 58 summary = title: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. [9] performed a classification algorithm using pneumonia data, SVM as a classification method, and InceptionV3, VGG-16 models as a deep learning approach. Using pneumonia and normal chest X-ray images, they set 30% of the dataset as test data and compared the proposed approach with the existing CNNs. They achieved 89.57% classification success. The second dataset is important in this study to compare COVID-19 chest images using deep learning models. cache = ./cache/cord-297517-w8cvq0m5.txt txt = ./txt/cord-297517-w8cvq0m5.txt === reduce.pl bib === id = cord-303187-ny4qr2a2 author = Belo, Vinícius Silva title = Abundance, survival, recruitment and effectiveness of sterilization of free-roaming dogs: A capture and recapture study in Brazil date = 2017-11-01 pages = extension = .txt mime = text/plain words = 7691 sentences = 410 flesch = 44 summary = Despite the perceived need and usefulness of such parameter estimates and recommendations for the most appropriate approaches applicable under such study designs [30] , survival and recruitment estimates of free-ranging dogs had not been obtained using methods of capture and recapture. In this study, we present estimates of abundance, survival and recruitment rates, and the probabilities of capture of two free-roaming dog populations by means of analytical models for open populations, so far unexplored in previous studies. We estimated critical parameters (survival, recruitment and abundance) that describe the population dynamics of free-roaming dogs based on a capture and recapture study design and on models suitable for open populations. Our study demonstrated the increase in population size in both areas, the predominance and greater recruitment of males, the temporal variability in recruitment and in survival probabilities, the lack of effect of sterilization on population dynamics, the influence of abandon and of density-independent factors and a high demographic turnover. cache = ./cache/cord-303187-ny4qr2a2.txt txt = ./txt/cord-303187-ny4qr2a2.txt === reduce.pl bib === id = cord-303651-fkdep6cp author = Thompson, Robin N. title = Key questions for modelling COVID-19 exit strategies date = 2020-08-12 pages = extension = .txt mime = text/plain words = 11567 sentences = 587 flesch = 40 summary = This leads to a roadmap for future research (figure 1) made up of three key steps: (i) improve estimation of epidemiological parameters using outbreak data from different countries; (ii) understand heterogeneities within and between populations that affect virus transmission and interventions; and (iii) focus on data needs, particularly data collection and methods for planning exit strategies in low-to-middle-income countries (LMICs) where data are often lacking. Three key steps are required: (i) improve estimates of epidemiological parameters (such as the reproduction number and herd immunity fraction) using data from different countries ( §2a-d); (ii) understand heterogeneities within and between populations that affect virus transmission and interventions ( §3a-d); and (iii) focus on data requirements for predicting the effects of individual interventions, particularly-but not exclusively-in data-limited settings such as LMICs ( §4a-c). cache = ./cache/cord-303651-fkdep6cp.txt txt = ./txt/cord-303651-fkdep6cp.txt === reduce.pl bib === id = cord-299852-t0mqe7yy author = Janssen, Loes H. C. title = Does the COVID-19 pandemic impact parents’ and adolescents’ well-being? An EMA-study on daily affect and parenting date = 2020-10-16 pages = extension = .txt mime = text/plain words = 8570 sentences = 476 flesch = 51 summary = In this ecological momentary assessment study, we investigated if the COVID-19 pandemic affected positive and negative affect of parents and adolescents and parenting behaviors (warmth and criticism). However, Intolerance of uncertainty, nor any pandemic related characteristics (i.e. living surface, income, relatives with COVID-19, hours of working at home, helping children with school and contact with COVID-19 patients at work) were linked to the increase of parents' negative affect during COVID-19. In addition, we asked parents and adolescents about daily difficulties and helpful activities during the COVID-19 pandemic that possibly influenced their affect in positive and negative ways. During the COVID-19 pandemic, the most reported daily difficulties across the 14 days of EMA for parents were (1) missing social contact with friends (14.6%), (2) concerns about the coronavirus in general (13.5%), (3) irritations with family members (12.8%), (4) worrying about health of others (8.3%), and (5) coronavirus-related news items (8.0%). cache = ./cache/cord-299852-t0mqe7yy.txt txt = ./txt/cord-299852-t0mqe7yy.txt === reduce.pl bib === id = cord-297161-ziwfr9dv author = Sauter, T. title = TESTING INFORMED SIR BASED EPIDEMIOLOGICAL MODEL FOR COVID-19 IN LUXEMBOURG date = 2020-07-25 pages = extension = .txt mime = text/plain words = 2245 sentences = 94 flesch = 51 summary = The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. Such models allow describing the dynamics of mutually exclusive states such as Susceptible (S) which for COVID-19 is assumed to be the entire population of a country, a region or city, the number of Infected (I) and Removed (R) that often combines (deaths and recovered), as well as the number of Exposed (E) for SEIR models. As the number of performed tests strongly influences the dynamic analysis of the COVID-19 pandemic in a country or region, we developed a novel SIR based epidemiological model (SIVRT, Figure 1 ) which allows the integration of this key information. In summary, the novel testing informed SIVRT model structure allows to describe and analyze the COVID-19 pandemic data of Luxembourg in dependency of the number of performed tests. cache = ./cache/cord-297161-ziwfr9dv.txt txt = ./txt/cord-297161-ziwfr9dv.txt === reduce.pl bib === id = cord-290421-9v841ose author = Weston, Dale title = Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews date = 2020-10-01 pages = extension = .txt mime = text/plain words = 9917 sentences = 402 flesch = 34 summary = The current paper presents a synthesis of review literature discussing the application of behaviour change theories within an infectious disease and emergency response context, with a view to informing infectious disease modelling, research and public health practice. Papers were included if they presented a review of theoretical models as applied to understanding preventative health behaviours in the context of emergency preparedness and response, and/or infectious disease outbreaks. Although this is based on key outcomes/ conclusions and not an exhaustive list of all successful theories reported within/ across reviews, the commonly applied behaviour change theories do seem to be identified as relevant for understanding and explaining human behaviour within an infectious disease and emergency response context. Based on these identified theories and our synthesis of review outcomes, and in conjunction with a recent review by Weston and colleagues [26] , we make recommendations to assist researchers, intervention designers, and mathematical modellers to incorporate psychological behaviour change theories within infectious disease and emergency response contexts. cache = ./cache/cord-290421-9v841ose.txt txt = ./txt/cord-290421-9v841ose.txt === reduce.pl bib === id = cord-295786-cpuz08vl author = Castillo-Sánchez, Gema title = Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review date = 2020-11-09 pages = extension = .txt mime = text/plain words = 7120 sentences = 509 flesch = 53 summary = This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. This scoping review aims to identify the current ML techniques used to predict suicide risk based on information posted on social networks. The authors have performed a systematic review to identify relevant papers that use suicide risk assessment models in social networks. To select the relevant studies on this topic, the authors defined the following inclusion criteria: & The studies include algorithms or models to estimate suicide risk using the social network. The research papers were excluded if they were not written in the English language, do not include a specific suicide intervention or do not report information regarding technical aspects of the model/algorithm used to detect suicide risk on social networks. The results of the application of artificial intelligence algorithms or models for suicide risk identification using data collected from social networks have been analyzed in this study. cache = ./cache/cord-295786-cpuz08vl.txt txt = ./txt/cord-295786-cpuz08vl.txt === reduce.pl bib === id = cord-310863-jxbw8wl2 author = PRASAD, J. title = A data first approach to modelling Covid-19 date = 2020-05-26 pages = extension = .txt mime = text/plain words = 7177 sentences = 403 flesch = 64 summary = We use the procedure to fit a set of SIR and SIRD models, with time dependent contact rate, to Covid-19 data for a set of 45 most affected countries. We find that SIR and SIRD models with constant transmission coefficients cannot fit Covid-19 data for most countries (mainly because social distancing, lockdown etc., make those time dependent). Some of the most important problems related to Covid-19 research are (1) estimating the controlling parameters of the pandemic, (2) making short term predictions using mathematical-statistical modeling which can help in mitigating policies (3) simulating the growth of the epidemic by taking into account as many contributing effects as possible and (4) quantifying the impact of mitigation measures, such as lockdown etc [ea20j] . One of the main reasons to consider these models has been that the Covid-19 data is available only for the Susceptible, Infected, Recovered and Dead compartments (for the notations used here and other places in the present work see table (1)). cache = ./cache/cord-310863-jxbw8wl2.txt txt = ./txt/cord-310863-jxbw8wl2.txt === reduce.pl bib === id = cord-296826-870mxd1t author = Taghikhah, Firouzeh title = Integrated modeling of extended agro-food supply chains: A systems approach date = 2020-06-27 pages = extension = .txt mime = text/plain words = 10522 sentences = 534 flesch = 43 summary = Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. In developing the proposed ESSC model considering the heterogeneity of consumers, we take an integrated modeling approach combining agent-based modeling (ABM), discrete event simulation (DES), and system dynamics (SD) to simulate both production and consumption side of the operation and the feedbacks between them. In response to this call, our study presents the development of an extended food SC model that incorporates the dynamics of farmers, processors, retailers, and consumers behavior as well as sustainability aspects. A growing number of studies focuses on improving the productivity of organic agriculture from sustainability perspectives; yet, the relationships between the behavior of final consumers and the decisions of upstream supply chain actors, in this case, farmers, have been poorly analyzed (Naik & Suresh, 2018; Taghikhah et al., 2019) . cache = ./cache/cord-296826-870mxd1t.txt txt = ./txt/cord-296826-870mxd1t.txt === reduce.pl bib === id = cord-299439-xvfab24g author = Fokas, A. S. title = COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA date = 2020-05-12 pages = extension = .txt mime = text/plain words = 2078 sentences = 134 flesch = 56 summary = title: COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA We have recently introduced two novel mathematical models for characterizing the dynamics of the cumulative number of individuals in a given country reported to be infected with COVID-19. 64 In a recent paper (9) we presented a model for the dynamics of the accumulative number of 65 individuals in a given country that are reported at time t to be infected by COVID-19. Here we will show that the Ricatti equation introduced in (9) can also be used for determining the 82 time evolution of the number, N(t), of deaths in a given country caused by the COVID-19 epidemic. Thus, the birational and (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. cache = ./cache/cord-299439-xvfab24g.txt txt = ./txt/cord-299439-xvfab24g.txt === reduce.pl bib === id = cord-298646-wurzy88k author = van der Merwe, René title = Challenge models to assess new therapies in chronic obstructive pulmonary disease date = 2012-09-13 pages = extension = .txt mime = text/plain words = 4775 sentences = 240 flesch = 40 summary = This review focuses on human challenge models with lipopolysaccharide endotoxin, ozone, and rhinovirus, in the early clinical development phases of novel therapeutic agents for the treatment and reduction of exacerbations in COPD. One of the main challenges in developing new therapeutic agents for the treatment or prevention of acute exacerbations of COPD is that their potential success cannot be entirely known until the investigational therapies enter relatively large Phase II studies, assessing clinical outcome over a 3-to 6-month period or longer. 20 In the first reported study of the inflammatory effects of low-level O 3 exposure (80 ppb O 3 for 6.6 hours) in healthy volunteers, 21 there were statistically significant increases in polymorphononuclear neutrophils, prostaglandin E 2 , lactate dehydrogenase, IL-6, α1-antitrypsin, and decreased phagocytosis via the complement receptor. The O 3 -challenge model potentially provides critical decision-making data in understanding whether new compounds have the desired biological effect in healthy volunteers and patients with COPD; hence it can de-risk decisions to move forwards into large Phase II safety and efficacy trials. cache = ./cache/cord-298646-wurzy88k.txt txt = ./txt/cord-298646-wurzy88k.txt === reduce.pl bib === id = cord-301117-egd1gxby author = Barh, Debmalya title = In Silico Models: From Simple Networks to Complex Diseases date = 2013-11-15 pages = extension = .txt mime = text/plain words = 13765 sentences = 670 flesch = 37 summary = Bioinformatics deals with methods for storing, retrieving, and analyzing biological data and protein sequences, structures, functions, pathways, and networks, and recently, in silico disease modeling and simulation using systems biology. Bioinformatics is the computational data management discipline that helps us gather, analyze, and represent this information in order to educate ourselves, understand biological processes in healthy and diseased states, and to facilitate discovery of better animal products. The development of such computational modeling techniques to include diverse types of molecular biological information clearly supports the gene regulatory network inference process and enables the modeling of the dynamics of gene regulatory systems. Understanding the complexity of the disease and its biological significance in health can be achieved by integrating data from the different functional genomics experiments with medical, physiological, and environmental factor information, and computing mathematically. cache = ./cache/cord-301117-egd1gxby.txt txt = ./txt/cord-301117-egd1gxby.txt === reduce.pl bib === id = cord-293562-69nnyq8p author = Imran, Mudassar title = Mathematical analysis of the role of hospitalization/isolation in controlling the spread of Zika fever date = 2018-08-15 pages = extension = .txt mime = text/plain words = 5874 sentences = 365 flesch = 55 summary = We consider a deterministic model for the transmission dynamics of the Zika virus infectious disease that spreads in, both humans and vectors, through horizontal and vertical transmission. We consider a deterministic model for the transmission dynamics of the Zika virus infectious disease that spreads in, both humans and vectors, through horizontal and vertical transmission. An in-depth stability analysis of the model is performed, and it is consequently shown, that the model has a globally asymptotically stable disease-free equilibrium when the basic reproduction number R 0 < 1. An in-depth stability analysis of the model is performed, and it is consequently shown, that the model has a globally asymptotically stable disease-free equilibrium when the basic reproduction number R 0 < 1. Since the only way to control the disease is to isolate patients who have been infected with the Zika virus, we included a new population compartment consisting of hospitalized individuals. cache = ./cache/cord-293562-69nnyq8p.txt txt = ./txt/cord-293562-69nnyq8p.txt === reduce.pl bib === id = cord-297530-7zbvgvk8 author = Kühnert, Denise title = Phylogenetic and epidemic modeling of rapidly evolving infectious diseases date = 2011-08-31 pages = extension = .txt mime = text/plain words = 12826 sentences = 629 flesch = 42 summary = By using Kingman's coalescent as a prior density on trees, Bayesian inference can be used to simultaneously estimate the phylogeny of the viral sequences and the demographic history of the virus population (Drummond et al., 2002 (Drummond et al., , 2005 , see Box 1). A maximum likelihood based method (the single rate dated tips (SRDT) model; Rambaut, 2000) , estimates ancestral divergence times and overall substitution rate on a fixed tree, assuming a strict molecular clock. While the generalized skyline plot is a good tool for data exploration, and to assist in model selection (e.g., Pybus et al., 2003; Lemey et al., 2004) , it infers demographic history based on a single input tree and therefore does not account for sampling error produced by phylogenetic reconstruction nor for the intrinsic stochasticity of the coalescent process. cache = ./cache/cord-297530-7zbvgvk8.txt txt = ./txt/cord-297530-7zbvgvk8.txt === reduce.pl bib === id = cord-305318-cont592g author = Lancaster, Madeline A. title = Disease modelling in human organoids date = 2019-07-01 pages = extension = .txt mime = text/plain words = 10865 sentences = 484 flesch = 39 summary = Thus, more recent approaches have focused on in vitro models derived from stem cells, which allow for a broader array of tissue identities, long-term expansion, better genomic integrity and improved modelling of healthy biology. established the first adult murine-tissue-derived liver organoid culture that sustains the long-term expansion of liver cells in vitro (Huch et al., 2013b) . Addition of an activator of cyclic adenosyl monophosphate (cAMP) signalling and inhibition of TGFβ signalling adapted this culture system to the expansion of adult human liver cells as self-renewing organoids that recapitulate some function of ex vivo liver tissue . (2014) was instrumental in characterizing the early stages of metanephric kidney development, particularly the formation of metanephric mesenchyme (MM), then applying the identified signalling factors to direct differentiation of mouse and human PSCs specifically towards MM cells that could form 3D structures when cocultured with mouse tissues. cache = ./cache/cord-305318-cont592g.txt txt = ./txt/cord-305318-cont592g.txt === reduce.pl bib === id = cord-307340-00m2g55u author = Gerasimov, A. title = Reaching collective immunity for COVID-19: an estimate with a heterogeneous model based on the data for Italy date = 2020-05-25 pages = extension = .txt mime = text/plain words = 2252 sentences = 126 flesch = 46 summary = Because of the high heterogeneity of COVID-19 infection risk across the different age groups, with a higher susceptibility for the elderly, homogeneous models overestimate the level of collective immunity needed for the disease to stop spreading. Because of the high heterogeneity of COVID-19 infection risk across the different age groups, with a higher susceptibility for the elderly, homogeneous models overestimate the level of collective immunity needed for the disease to stop spreading. Here we developed a mathematical model for assessing the minimum incidence of COVID-19 needed to reach collective immunity, which would assure that the epidemic cannot restart the cessation of quarantine measures. While this search yielded several useful references regarding COVID-19 modeling, the basic reproduction number of this disease, and age-related heterogeneity, we did not find an approach similar to ours to modeling COVID-19 dynamics and estimating the total incidence and population immunity. cache = ./cache/cord-307340-00m2g55u.txt txt = ./txt/cord-307340-00m2g55u.txt === reduce.pl bib === id = cord-308115-bjyr6ehq author = Baba, Isa Abdullah title = Fractional Order Model for the Role of Mild Cases in the Transmission of COVID-19 date = 2020-10-20 pages = extension = .txt mime = text/plain words = 2394 sentences = 134 flesch = 42 summary = To execute these measures effectively, there is need to have an in depth study about the number of persons that each infected individual can infect, meanwhile a mathematical model describing the transmission dynamics of the disease should be established. [6] developed a mathematical model (for MERS) inform of nonlinear system of differential equations, in which he considered a camel to be the source of infection that spread the virus to infective human population, then human to human transmission, then to clinic center then to care center. However, they constructed the Lyapunov candidate function to investigate the local and global stability analysis of the equilibriums solution and subsequently obtained the basic reproduction number or roughly, a key parameter describing transmission of the infection. A mathematical model for COVID-19 transmission by using the Caputo fractional derivative A fractional differential equation model for the COVID-19 transmission by using the Caputo-Fabrizio derivative cache = ./cache/cord-308115-bjyr6ehq.txt txt = ./txt/cord-308115-bjyr6ehq.txt === reduce.pl bib === id = cord-307133-bm9z8gss author = Kong, Lingcai title = Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model date = 2016-02-24 pages = extension = .txt mime = text/plain words = 4611 sentences = 247 flesch = 48 summary = Finally, we calibrated the model with the number of daily cases of severe acute respiratory syndrome (SARS) in Beijing in 2003, and the estimated parameters show that the control measures taken at that time were effective. A low level of heterogeneity results in dynamics similar to those predicted by the homogeneous-mixing model with a frequency-dependent transmission term, βSI N . The greatest difference is that at the overall level, the heterogeneity slows the transmission speed and decreases the peak sizes, which means milder disease outbreaks, because in the scenario with a high level of heterogeneity, only a small proportion of susceptible individuals have chances of coming into contact with infectious individuals and becoming infected, which results in a slower increase of the infected population. Our results show that, keeping other conditions identical, the higher is the level of heterogeneity in contact rates, the greater is the difference in the disease dynamics observed from those predicted using the homogeneous-mixing models. cache = ./cache/cord-307133-bm9z8gss.txt txt = ./txt/cord-307133-bm9z8gss.txt === reduce.pl bib === id = cord-309301-ai84el0j author = Li, Yaqi title = Organoid based personalized medicine: from bench to bedside date = 2020-11-02 pages = extension = .txt mime = text/plain words = 17467 sentences = 934 flesch = 41 summary = The mini-gut culture approach has been applied to the generation of organoids derived from the epithelial compartments of a variety of murine and human tissues of ecto-, meso-and endodermal origin, and promotes the study of stem cell biology of other tissues except for intestine. For translational research, tumorderived organoids can be used for biobanking, genetic repair and drug screening studies, both for personalized medicine (to choose the most effective treatment for a specific patient) and drug development (to test a compound library on a specific set of tumor organoids), as well as immunotherapy research similar in liver, small intestine, and colon stem cells, regardless of the large variation in cancer incidence of these organs. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell-and patient-derived tumor organoids cache = ./cache/cord-309301-ai84el0j.txt txt = ./txt/cord-309301-ai84el0j.txt === reduce.pl bib === id = cord-302277-c66xm2n4 author = Bakaletz, Lauren O. title = Developing animal models for polymicrobial diseases date = 2004 pages = extension = .txt mime = text/plain words = 10910 sentences = 537 flesch = 33 summary = Briefly, viral infection compromises the protective functions of the Eustachian tube, alters respiratory-tract secretions, damages the mucosal epithelial lining, interferes with antibiotic efficacy, modulates the immune response and enhances bacterial adherence 77 and colonization 78 to predispose the host to bacterial OM. In otitis media, which is a middle ear infection, a synergistic interaction that results in disease owing to co-infection with an upper respiratory tract virus and three bacterial species -Streptococcus pneumoniae, nontypeable Haemophilus influenzae (NTHI) and Moraxella catarrhalis -is well documented. It seems likely that the transient suppression of RDC migration and the delayed development of an effective adaptive immune response to a second infection might be another mechanism by which influenza virus predisposes the host to bacterial co-infection. Using this criterion, a mouse model of polymicrobial-induced osteoclastogenesis, bacterial penetration, leukocyte recruitment and softtissue necrosis has been developed to clarify the role of cytokines in periodontal disease. cache = ./cache/cord-302277-c66xm2n4.txt txt = ./txt/cord-302277-c66xm2n4.txt === reduce.pl bib === id = cord-311432-js84ruve author = Hossein Rashidi, T. title = Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment date = 2020-04-29 pages = extension = .txt mime = text/plain words = 4238 sentences = 194 flesch = 48 summary = The classical Susceptible-[Exposed]-Infected-Recovered (SEIR/SIR) epidemic models [4] , have 15 been widely developed to simulate the transmission dynamics of COVID 19 [5, 6] and the impact of non-therapeutic interventions -e.g., travel and border restrictions [7, 8] , quarantines and isolations [5, [9] [10] [11] , or social distancing and closure of facilities-on the spread of the outbreak, and in some cases, on the healthcare demand [5, 9, [11] [12] [13] .These studies have been mostly focused on calibrating models for a specific country/region based on the data at the time 20 of the model-development and assuming a multitude of parameters initialized upon prior knowledge such as social contact structure, rate of compliance with the policy and incubation or infection period among others. cache = ./cache/cord-311432-js84ruve.txt txt = ./txt/cord-311432-js84ruve.txt === reduce.pl bib === id = cord-310406-5pvln91x author = Asbury, Thomas M title = Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome date = 2010-09-02 pages = extension = .txt mime = text/plain words = 3014 sentences = 189 flesch = 44 summary = RESULTS: We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In addition, in spite of the many recent efforts to measure and model the genome structure at various resolutions and detail [3] [4] [5] [6] [7] [8] [9] [10] , little work has focused on combining these models into a plausible aggregate, or has taken advantage of the large amount of genomic and epigenomic data available from new high-throughput approaches. The viewer is designed to display data from multiple scales and uses a hierarchical model of the relative positions of all nucleotide atoms in the cell nucleus, i.e., the complete physical genome. An integrated physical genome model can show the interplay between histone modifications and other genomic data, such as SNPs, DNA methylation, the structure of gene, promoter and transcription machinery, etc. In addition to epigenomic data, the physical genome model also provides a platform to visualize highthroughput gene expression data and its interplay with global binding information of transcription factors. cache = ./cache/cord-310406-5pvln91x.txt txt = ./txt/cord-310406-5pvln91x.txt === reduce.pl bib === id = cord-309010-tmfm5u5h author = Dietert, Kristina title = Spectrum of pathogen- and model-specific histopathologies in mouse models of acute pneumonia date = 2017-11-20 pages = extension = .txt mime = text/plain words = 7842 sentences = 414 flesch = 34 summary = Here, we systematically describe and compare the distinctive histopathological features of established models of acute pneumonia in mice induced by Streptococcus (S.) pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Legionella pneumophila, Escherichia coli, Middle East respiratory syndrome (MERS) coronavirus, influenza A virus (IAV) and superinfection of IAV-incuced pneumonia with S. Systematic comparisons of the models revealed striking differences in the distribution of lesions, the characteristics of pneumonia induced, principal inflammatory cell types, lesions in adjacent tissues, and the detectability of the pathogens in histological sections. Transnasal infection with MERS-CoV following adenoviral transduction of human DPP4 yielded an expansive, (Fig 7A) interstitial pneumonia with severe alveolar epithelial cell necrosis and infiltration of mainly macrophages, lymphocytes, and fewer neutrophils (Fig 7B) . Different mouse models of acute pneumonia differ widely, with an obvious strong dependence on pathogen-specific features of virulence and spread, route of infection, infectious dose and other factors. cache = ./cache/cord-309010-tmfm5u5h.txt txt = ./txt/cord-309010-tmfm5u5h.txt === reduce.pl bib === id = cord-301505-np4nr7gg author = Lin, Xin title = Two types of transmembrane homomeric interactions in the integrin receptor family are evolutionarily conserved date = 2006-01-27 pages = extension = .txt mime = text/plain words = 5266 sentences = 279 flesch = 49 summary = Our results show that two models, one involving a GxxxG‐like motif (model I) and an almost opposite form of interaction (model II) are conserved across all α and β integrin types, both in homodimers and homotrimers, with different specificities. 21 Using the TOXCAT assay, 22 a test that measures the oligomerization of a chimeric protein containing a TM helix in the Escherichia coli inner membrane via transcriptional activation of the gene for chloramphenicol acetyltransferase, a sequence critical for integrin ␣IIb-TM homodimerization that involved the GxxxG motif was suggested by Li et al. Our computational results have been obtained independently from any previous experimental data, and clearly show that two right-handed types of homomeric interaction in the transmembrane domain of ␣ and ␤ integrins (models I and II) are evolutionarily conserved. cache = ./cache/cord-301505-np4nr7gg.txt txt = ./txt/cord-301505-np4nr7gg.txt === reduce.pl bib === === reduce.pl bib === id = cord-308219-97gor71p author = Elzeiny, Sami title = Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images date = 2020-09-17 pages = extension = .txt mime = text/plain words = 5697 sentences = 312 flesch = 52 summary = By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models' accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models. In this study, a new stress classification approach is proposed to classify the individual stress state into stressed or non-stressed by converting spatial images of inter-beat intervals of a PPG signal to frequency domain images and we use these pictures to train several CNN models. cache = ./cache/cord-308219-97gor71p.txt txt = ./txt/cord-308219-97gor71p.txt === reduce.pl bib === id = cord-309096-vwbpkpxd author = Magdon-Ismail, Malik title = Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics date = 2020-03-20 pages = extension = .txt mime = text/plain words = 4881 sentences = 412 flesch = 67 summary = We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. We also follow a data-driven machine learning approach to understand early dynamics of COVID-19 on the first 54 days of US confirmed infection reports (downloadable from the European Center For Disease Control). β, asymptomatic infectious force governing exponential spread γ, virulence, the fraction of mild cases that become serious later k, lag time for mild infection to become serious (an incubation time) M 0 , Unconfirmed mild asymptomatic infections at time 0 Figure 1 are the model predictions (blue envelope) and the red circles are the observed infection counts. Our results demonstrate the effectiveness of simple robust models for predicting pandemic dynamics from early data. From this solution as a starting point, we can further optimize γ, β using a gradient descent which minimizes an error-measure that captures how well the parameters β, γ, k, M 0 reproduce the observed dynamics in Figure 2 , see for example Abu-Mostafa et al. cache = ./cache/cord-309096-vwbpkpxd.txt txt = ./txt/cord-309096-vwbpkpxd.txt === reduce.pl bib === id = cord-312366-8qg1fn8f author = Adiga, Aniruddha title = Mathematical Models for COVID-19 Pandemic: A Comparative Analysis date = 2020-10-30 pages = extension = .txt mime = text/plain words = 8797 sentences = 472 flesch = 49 summary = As the pandemic takes hold, researchers begin investigating: (i) various intervention and control strategies; usually pharmaceutical interventions do not work in the event of a pandemic and thus nonpharmaceutical interventions are most appropriate, (ii) forecasting the epidemic incidence rate, hospitalization rate and mortality rate, (iii) efficiently allocating scarce medical resources to treat the patients and (iv) understanding the change in individual and collective behavior and adherence to public policies. Like projection approaches, models for epidemic forecasting can be broadly classified into two broad groups: (i) statistical and machine learning-based data-driven models, (ii) causal or mechanistic models-see 29, 30, 2, 31, 32, 6, 33 and the references therein for the current state of the art in this rapidly evolving field. In the context of COVID-19 case count modeling and forecasting, a multitude of models have been developed based on different assumptions that capture specific aspects of the disease dynamics (reproduction number evolution, contact network construction, etc.). cache = ./cache/cord-312366-8qg1fn8f.txt txt = ./txt/cord-312366-8qg1fn8f.txt === reduce.pl bib === id = cord-313279-15wii9nn author = Trevijano-Contador, Nuria title = Expanding the use of alternative models to investigate novel aspects of immunity to microbial pathogens date = 2014-05-15 pages = extension = .txt mime = text/plain words = 2122 sentences = 107 flesch = 38 summary = In the present issue of Virulence, an article entitled "The maternal transfer of bacteria can mediate trans-generational immune priming in insects" 1 describes an elegant study that illustrates the use of the lepidopteran Galleria mellonella to investigate a specific aspect of immunity to microbes. But in addition, this study opens the scope on the use of non-conventional models and illustrates how they can be used to investigate aspects of immunity against pathogenic microorganisms. These models have been a useful tool for centuries, and the development of their genetic manipulation offers new alternatives to investigate the role of specific factors of the immune system in the defense against pathogens. Among insects, there are two species largely used as model hosts to study microbial virulence, Drosophila melanogaster and Galleria mellonella. melanogaster also a suitable model to investigate the role of host elements in the response against microbial pathogens. cache = ./cache/cord-313279-15wii9nn.txt txt = ./txt/cord-313279-15wii9nn.txt === reduce.pl bib === id = cord-312911-nqq87d0m author = Zou, D. title = Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States date = 2020-05-25 pages = extension = .txt mime = text/plain words = 5032 sentences = 283 flesch = 62 summary = We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (R0). Based on the proposed model, we are able to make accurate predictions on the numbers of confirmed cases and fatality cases for nation, states and and counties. Moreover, our model can also predict the peak dates of active cases and estimate the basic reproduction number (R 0 ) of different states in the US. cache = ./cache/cord-312911-nqq87d0m.txt txt = ./txt/cord-312911-nqq87d0m.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-317993-012hx4kc author = Movia, Dania title = Preclinical Development of Orally Inhaled Drugs (OIDs)—Are Animal Models Predictive or Shall We Move Towards In Vitro Non-Animal Models? date = 2020-07-24 pages = extension = .txt mime = text/plain words = 6885 sentences = 369 flesch = 42 summary = SIMPLE SUMMARY: This commentary focuses on the methods currently available to test the efficacy and safety of new orally inhaled drugs for the treatment of uncurable respiratory diseases, such as chronic obstructive pulmonary disease (COPD), cystic fibrosis or lung cancer, prior to entering human experimentation. Inhalation is the preferred administration method for treating respiratory diseases [13] , as: (i) it delivers the drug directly at the site of action, resulting in a rapid therapeutic onset with considerably lower drug doses, (ii) it is painless and minimally invasive thus improving patients' compliance, and (iii) it avoids first-pass metabolism, providing optimal pharmacokinetic conditions for drug absorption and reducing systemic side effects [14] [15] [16] . In the context of OID preclinical testing, lung organoids can be used for modeling respiratory diseases and, therefore, as a platform for screening the efficacy of inhalation therapies [115, 116] . cache = ./cache/cord-317993-012hx4kc.txt txt = ./txt/cord-317993-012hx4kc.txt === reduce.pl bib === === reduce.pl bib === id = cord-318562-jif88gof author = Jiménez-Liso, Maria Rut title = Changing How We Teach Acid-Base Chemistry: A Proposal Grounded in Studies of the History and Nature of Science Education date = 2020-08-15 pages = extension = .txt mime = text/plain words = 9892 sentences = 412 flesch = 48 summary = Controversial moments in science from 1923, when three researchers (Bronsted, Lowry, and Lewis) independently enunciated two theories from two different paradigms (dissociation and valence electron), underpin our first sequence with an explicit NoS approach for both lower secondary school and upper secondary or university levels. In this theoretical article examining teaching practice, we want to focus on the historical development of acid-base theories (Arrhenius, Bronsted-Lowry and Lewis) to analyse the steps to follow to design sequences of activities for different NoS approaches. We examine conventional teaching approaches to the topic and its consequences in terms of students' alternative conceptions and their difficulties to transfer and apply knowledge and to recognize acid-base models' limits of applicability. The science education literature is replete with examples of the consequences for students' learning of this typical way of teaching acid-base content focused on the definition of its concepts and with two or three theories introduced simultaneously. cache = ./cache/cord-318562-jif88gof.txt txt = ./txt/cord-318562-jif88gof.txt === reduce.pl bib === id = cord-310844-7i92mk4x author = Hryhorowicz, Magdalena title = Application of Genetically Engineered Pigs in Biomedical Research date = 2020-06-19 pages = extension = .txt mime = text/plain words = 9011 sentences = 475 flesch = 37 summary = Animal studies are conducted to develop models used in gene function and regulation research and the genetic determinants of certain human diseases. Short pregnancy, short generation interval, and high litter size make the production of transgenic pigs less time-consuming in comparison with other livestock species This review describes genetically modified pigs used for biomedical research and the future challenges and perspectives for the use of the swine animal models. It was demonstrated that precise integration of the human CFTR gene at a porcine safe harbor locus through CRISPR/Cas9-induced HDR-mediated knock-in allowed the achievement of persistent in vitro expression of the transgene in transduced cells. The study showed that multiple genetically modified porcine hearts were protected from complement activation and myocardial natural killer cell infiltration in an ex vivo perfusion model with human blood [86] . Biomedical applications for which genetically engineered pigs are generated include modeling human diseases, production of pharmaceutical proteins, and xenotransplantation. cache = ./cache/cord-310844-7i92mk4x.txt txt = ./txt/cord-310844-7i92mk4x.txt === reduce.pl bib === id = cord-315462-u2dj79yw author = Hewitt, Judith A. title = ACTIVating Resources for the COVID-19 Pandemic: In vivo Models for Vaccines and Therapeutics date = 2020-10-01 pages = extension = .txt mime = text/plain words = 8953 sentences = 469 flesch = 44 summary = The selection of appropriate animal models of infection, disease manifestation, and efficacy measurements is important for vaccines and therapeutics to be compared under ACTIV's umbrella using Master Protocols with standardized endpoints and assay readouts. Models of SARS-CoV-2 infection include mice (ACE2 transgenic strains, mouse adapted virus, and AAV transduced ACE2 mice), hamsters, rats, ferrets and non-human primates (NHPs). Following infection by the intranasal route, golden Syrian Hamsters demonstrate clinical features, viral kinetics, histopathological changes, and immune responses that closely mimic the mild to moderate disease described in human COVID-19 patients (Chan et al., 2020b; Imai et al., 2020; Sia et al., 2020) . In an initial study of SARS-CoV-2 infection of hACE2-hamsters, clinical signs were observed including elevated body temperatures, slow or reduced mobility, weight loss and mortality (1 out of 4 animals). Human angiotensin-converting enzyme 2 transgenic mice infected with SARS-CoV-2 develop severe and fatal respiratory disease. cache = ./cache/cord-315462-u2dj79yw.txt txt = ./txt/cord-315462-u2dj79yw.txt === reduce.pl bib === id = cord-319378-li77za5e author = Schroeder, Wheaton L. title = Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis date = 2020-09-11 pages = extension = .txt mime = text/plain words = 15111 sentences = 818 flesch = 57 summary = Even with the addition of exchange and transport reactions, the current Exophiala dermatitidis draft model has relatively few reactions which are capable of holding flux as determined by FVA, see ''General steps on how to use iEde2091'' and accompanying code for a description on how to apply FVA). The stoichiometries for the reactions selected by the first CPs solution (taken from the first database file) should be added to a copy of ll OPEN ACCESS STAR Protocols 1, 100105, September 18, 2020 the second draft Exophiala dermatitidis model in order to make the third draft E. As with step 3, the best solutions should be selected from the second application of OptFill, and the stoichiometries of the reactions in the optimal CPs solution should be added to a copy of the third draft Exophiala dermatitidis model to produce the fourth draft E. cache = ./cache/cord-319378-li77za5e.txt txt = ./txt/cord-319378-li77za5e.txt === reduce.pl bib === id = cord-313046-3g2us5zh author = Taghizadeh, L. title = Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic date = 2020-06-03 pages = extension = .txt mime = text/plain words = 5180 sentences = 355 flesch = 55 summary = We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. In this work, we propose Bayesian inference for the analysis of the COVID-19 data in order to estimate the crucial unknown quantities of the pandemic models. We use an adaptive MCMC method to find the probability distributions and confidence intervals of the epidemiological models parameters using the Austrian infection data. In this section, we present simulation results of Bayesian inversion and the adaptive MCMC method (see Algorithm 1) for the two epidemic models, namely the logistic and the SIR models, using the data of the COVID-19 outbreak in Austria. According to Bayesian analysis, the unknown parameters of the logistic and SIR models using the data of COVID-19 outbreak in Austria were found and summarized in Table 1 and Table 3 , respectively. cache = ./cache/cord-313046-3g2us5zh.txt txt = ./txt/cord-313046-3g2us5zh.txt === reduce.pl bib === id = cord-318900-dovu6kha author = Pitschel, T. title = SARS-Cov-2 proliferation: an analytical aggregate-level model date = 2020-08-22 pages = extension = .txt mime = text/plain words = 3841 sentences = 215 flesch = 53 summary = An intuitive mathematical model describing the virus proliferation is presented and its parameters estimated from time series of observed reported CoViD-19 cases in Germany. Approximating the model evolution as continuous process even at small time intervals 1 Caution in the usage of numbers from pure incidence analysis is required: As consequence of the way the raw data is obtained in [HLWea20] , only infectiousness around the moment of symptom onset is in fact fully observed. Therefore, at the present state of this text, such estimation can only serve to determine reasonable bounds on the parameters of the model, rather than to give a reliable forecast of expect number of eventual infections. In this study a novel model for virus proliferation dynamics was developed and with it the SARS-Cov-2 outbreak in Germany retraced on an aggregate level, using CoViD-19 case count data by the Robert-Koch Institute in Berlin. cache = ./cache/cord-318900-dovu6kha.txt txt = ./txt/cord-318900-dovu6kha.txt === reduce.pl bib === id = cord-311086-i4e0rdxp author = Adekola, Hafeez Aderinsayo title = Mathematical modeling for infectious viral disease: The COVID‐19 perspective date = 2020-08-17 pages = extension = .txt mime = text/plain words = 3277 sentences = 169 flesch = 41 summary = The SEIR model with suitable adaptations has been widely applied for various disease epidemics such as chickenpox and SARS, and its relevance has been advanced for the analysis of the dynamic transmission of COVID-19 in this context. This sixchambered model was used to study the transmission mechanism of COVID-19 and the implemented prevention and control measures, with the aid of time series and kinetic modal analysis, a basic reproductive number value of 4.01 was obtained (Li, Geng, et al., 2020) . Although the mathematical models for the COVID-19 have majorly forecast few areas relating to pathogen spread such as the basic reproductive number of the SARS-CoV-2, population control measures, percentage of asymptomatic people (Nandal, 2020) . Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan cache = ./cache/cord-311086-i4e0rdxp.txt txt = ./txt/cord-311086-i4e0rdxp.txt === reduce.pl bib === id = cord-319291-6l688krc author = Hung, Chun-Min title = Alignment using genetic programming with causal trees for identification of protein functions date = 2006-09-01 pages = extension = .txt mime = text/plain words = 8941 sentences = 632 flesch = 52 summary = Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. The hybrid model, namely Alignment using Genetic programming with Causal Tree (AGCT), is a heuristic evolutionary method that contains three basic components: (i) genetic programming with innerexchanged individual strategy, (ii) causal trees [4, 28, 31] with probabilistic reasoning, and (iii) construction of hierarchical homologies with local block-to-block alignment using the methods of moment invariant and robust points matching (RPM) [24] . cache = ./cache/cord-319291-6l688krc.txt txt = ./txt/cord-319291-6l688krc.txt === reduce.pl bib === id = cord-318079-jvx1rh7g author = Hinch, R. title = OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing date = 2020-09-22 pages = extension = .txt mime = text/plain words = 5363 sentences = 283 flesch = 45 summary = The ABM was developed to simulate different non-pharmaceutical interventions including lockdown, physical distancing, self-isolation on symptoms, testing and contact tracing. A previous study of social contacts for infectious disease modelling, based on participants being asked to recall their interactions over the past day, has estimated the mean number of interactions that individuals have by age group [12] . We present OpenABM-Covid19, a COVID-19-specific agent-based model suitable for simulating the epidemic in different settings and assessing non-pharmaceutical interventions, including contact tracing using a mobile phone app. Further, on developing symptoms or during interventions such as contact tracing, the interaction pattern of individuals change to only include those in the household. One of the key aims of OpenABM-Covid19 was to model non-pharmaceutical interventions and, in particular, different forms of contact tracing. OpenABM-Covid19 is a versatile tool to model the COVID-19 epidemic in different settings and simulate different non-pharmaceutical interventions including contact tracing. cache = ./cache/cord-318079-jvx1rh7g.txt txt = ./txt/cord-318079-jvx1rh7g.txt === reduce.pl bib === === reduce.pl bib === id = cord-321715-bkfkmtld author = Redelings, Benjamin D title = Incorporating indel information into phylogeny estimation for rapidly emerging pathogens date = 2007-03-14 pages = extension = .txt mime = text/plain words = 9793 sentences = 546 flesch = 54 summary = To see if indel information improves phylogenetic resolution we compare the number of bi-partitions that are supported under the joint model and the traditional sequential approach, in which topology reconstruction assumes a previously determined alignment. These parameters include a multiple alignment A that specifies the positional homology between the sequences Y, an evolutionary tree (τ, T) where τ is an unrooted bifurcating tree topology and T = (t 1 , ..., t 2N -3 ) is a vector of branch lengths along the edges in τ, and vectors Θ and Λ are parameters that characterize the letter substitution and indel processes respectively. We therefore propose a new pairwise alignment prior that maintains a fixed sequence length distribution φ even when the indel probability varies from branch to branch. Since the joint model balances substitution and indel information as well as taking alignment ambiguity into account we assume that these differences represent an improvement in the accuracy of estimation. cache = ./cache/cord-321715-bkfkmtld.txt txt = ./txt/cord-321715-bkfkmtld.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-321735-c40m2o5l author = Manca, Davide title = A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under Covid-19 pandemic date = 2020-06-04 pages = extension = .txt mime = text/plain words = 7164 sentences = 323 flesch = 53 summary = Besides the predicted numbers, those models allowed also forecasting the different phases of the pandemic and quantifying some basic indicators about the daily variations, the key times, the key figures, the expected decrease, the progressive reach of a maximum plateau before facing with the decrease of ICU beds for Covid-19 which we are measuring right now. Usually, patients remain in ICU wards at least fifteen days (with twenty-day stay the standard value) (Cutuli, 2020) and, respect to Covid-19 emergency, this quite a long time allows describing the whole ICU beds inflation period with curves such as the logistic (Hosmer et al., 2013) or the Gompertz (Panik, 2014) ones. The models of Section 2.3 applied to the case study of Lombardy and Italy proved their efficiency in reproducing real data and were used to forecast the evolution of key parameters as the number of ICU patients and deaths on both short and long-time horizons. cache = ./cache/cord-321735-c40m2o5l.txt txt = ./txt/cord-321735-c40m2o5l.txt === reduce.pl bib === id = cord-321852-e7369brf author = Wang, Bo title = AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system date = 2020-11-10 pages = extension = .txt mime = text/plain words = 6468 sentences = 373 flesch = 50 summary = In this paper, we introduce a automatically AI system that can provide the probability of infection and the ranked IDs. Specifically, the proposed system which consists of classification and segmentation will save about 30-40% of the detection time for physicians and promote the performance of COVID-19 detection. Using the dataset, we train and evaluate several deep learning based models to detect and segment the COVID-19 regions. [34] also build a U-Net based segmentation model to separate lung lesions and extract the radiologic characteristics in order to predict the hospital stay of a patient. [42] develop three widelyused models, i.e., ResNet-50 [43] , Inception-V3 [44] , and Inception-ResNet-V2 [45] , to detect COVID-19 lesion in X-ray images and among them ResNet-50 achieves the best classification performance. The positive data for the segmentation models were those images with arbitrary lung lesion regions, regardless of whether the lesions were COVID-19 or not. cache = ./cache/cord-321852-e7369brf.txt txt = ./txt/cord-321852-e7369brf.txt === reduce.pl bib === id = cord-320141-892v3b7m author = Boshra, Mina title = 3D printing in critical care: a narrative review date = 2020-09-30 pages = extension = .txt mime = text/plain words = 4532 sentences = 240 flesch = 46 summary = Our search produced 31 papers that described possible uses of 3DP in critical care which can be divided into three main themes: Medical education (Med-Ed), patient care, and clinical equipment modification (CEM) ( Table 1) . This review shows that 3DP can have a variety of utilities in the field of critical care including medical education, patient care, and development of clinical equipment; however, Med-Ed takes the lead as the most common utility of 3DP with over 70% of the papers found discussing the use of 3DP models to train medical students and/or residents. This narrative review has summarized the major uses of 3DP in the field of critical care which were found to be mainly within the realms of medical education (e.g. simulation models and training modules), patient care (e.g. wound care and personalized splints), and clinical equipment modification (e.g. 3DP laryngoscope handle). cache = ./cache/cord-320141-892v3b7m.txt txt = ./txt/cord-320141-892v3b7m.txt === reduce.pl bib === id = cord-319933-yp9ofhi8 author = Ruiz, Sara I. title = Chapter 38 Animal Models of Human Viral Diseases date = 2013-12-31 pages = extension = .txt mime = text/plain words = 28834 sentences = 1797 flesch = 46 summary = An experimental study with cell culture-adapted hepatitis Avirus in guinea pigs challenged by oral or intraperitoneal routes did not result in clinical disease, increase in liver enzymes, or seroconversion. 32 NHPs including marmosets, cotton-top tamarins, and rhesus macaques infected with Norwalk virus can be monitored for the extent of viral shedding; however, no clinical disease is observed in these models. 66, 67 Intracerebral and intranasal routes of infection resulted in a fatal disease that was highly dependent on dose, while intradermal and subcutaneous inoculations caused only 50% fatality in mice regardless of the amount of virus. A mouse-adapted (MA) strain of Dengue virus 2 introduced into AG129 mice developed vascular leak syndrome similar to the severe disease seen in humans. [138] [139] [140] [141] [142] [143] [144] Inoculation of WNV into NHPs intracerebrally resulted in the development of either encephalitis, febrile disease, or an asymptomatic infection, depending on the virus strain and dose. cache = ./cache/cord-319933-yp9ofhi8.txt txt = ./txt/cord-319933-yp9ofhi8.txt === reduce.pl bib === id = cord-320914-zf54jfol author = Parrish, Rebecca title = A Critical Analysis of the Drivers of Human Migration Patterns in the Presence of Climate Change: A New Conceptual Model date = 2020-08-19 pages = extension = .txt mime = text/plain words = 9935 sentences = 510 flesch = 42 summary = Finally, we apply this model to a case study of Malawi to demonstrate how doing so can improve understanding of the local context and result in well-grounded and policy-relevant insights into the true impacts of climate change on migration. By conducting an in-depth literature review of Malawi's political, demographic, environmental, social and economic makeup and then applying the conceptual approach described above by considering the impacts of climate change (primary, secondary and tertiary) to each key factor, we arrive at the case-specific model shown in Figure 2 below. By conducting an in-depth literature review of Malawi's political, demographic, environmental, social and economic makeup and then applying the conceptual approach described above by considering the impacts of climate change (primary, secondary and tertiary) to each key factor, we arrive at the case-specific model shown in Figure 2 below. cache = ./cache/cord-320914-zf54jfol.txt txt = ./txt/cord-320914-zf54jfol.txt === reduce.pl bib === id = cord-320953-1st77mvh author = Overton, ChristopherE. title = Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example date = 2020-07-04 pages = extension = .txt mime = text/plain words = 15721 sentences = 734 flesch = 48 summary = These include interpreting symptom progression and fatality ratios with delay distributions and right-censoring, exacerbated by exponential growth in cases leading to the majority of case data being on recently infected individuals; lack of clarity and consistency in denominators; inconsistency of case definitions over time and the eventual impact of interventions and changes to behaviour on transmission dynamics. We then develop a household-based contact tracing model, with which we investigate the extinction probability under weaker isolation policies paired with contact tracing, thus shedding light on possible combinations of interventions that allow us to feasibly manage the infection while minimising the social impact of control policies. Applying household isolation at 65% adherence ( 0.65 W α = ) manages to reduce the spread of infection, but appears insufficient in this model and with baseline parameters for controlling the outbreak in the long-term, unless other intervention strategies that reduce the global transmission (increasing ε) are adopted at the same time. cache = ./cache/cord-320953-1st77mvh.txt txt = ./txt/cord-320953-1st77mvh.txt === reduce.pl bib === id = cord-316393-ozl28ztz author = Enrique Amaro, José title = Global analysis of the COVID-19 pandemic using simple epidemiological models date = 2020-10-22 pages = extension = .txt mime = text/plain words = 5293 sentences = 294 flesch = 58 summary = The Death or 'D' model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations – also developed in this work – predict similar trends and suggest a common pandemic evolution with universal parameters. Additionally, D-model calculations are benchmarked with more sophisticated and reliable calculations using the extended SIR (ESIR) and Monte Carlo Planck (MCP) models -also developed in this work -which provide similar results, but allow for a more coherent spatial-time disentanglement of the various effects present during a pandemic. cache = ./cache/cord-316393-ozl28ztz.txt txt = ./txt/cord-316393-ozl28ztz.txt === reduce.pl bib === id = cord-322806-g01wmmbx author = Sturniolo, S. title = Testing, tracing and isolation in compartmental models date = 2020-05-19 pages = extension = .txt mime = text/plain words = 9749 sentences = 531 flesch = 53 summary = This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. It provides a logical framework for understanding the propagation of an May 14, 2020 1/23 infectious disease through a population and allows different interventions to be explored, including testing and contact tracing of infected individuals as possible strategies to ease social distancing restrictions. In this paper we develop an extension to the classic Susceptible-Exposed-Infectious-Removed 1 (SEIR) model [16, 52, 53] simulated with ODEs to include testing, contacttracing, and isolation (TTI) strategies. To answer this we adapt the standard Susceptible-Exposed-Infectious-Removed (SEIR) compartmental model [16, 52] to incorporate contact tracing as well as testing and isolation of cohorts of people. Overlapping compartments represent model states that are not mutually exclusive, so that it is possible for an individual to belong in more than one of them e.g. be infected and contact-traced, or exposed and tested. cache = ./cache/cord-322806-g01wmmbx.txt txt = ./txt/cord-322806-g01wmmbx.txt === reduce.pl bib === === reduce.pl bib === id = cord-284617-uwby8r3y author = Area, Iván title = Determination in Galicia of the required beds at Intensive Care Units date = 2020-10-06 pages = extension = .txt mime = text/plain words = 2595 sentences = 168 flesch = 61 summary = By using a recent mathematical compartmental model that includes the super-spreader class and developed by Ndaïrou, Area, Nieto, and Torres, a procedure to estimate in advance the number of required beds at intensive care units is presented. We have employed a compartmental mathematical model for COVID19 to estimate in advance the number of required beds at intensive care units. Following previous works [8] [9] [10] , in [11] a model including the super-spreader class [14, 15] has been presented, and applied to give an estimation of the infected and death individuals in Wuhan. The usefulness of our model is then illustrated in Section 3 of numerical simulations, where by using the real data from Galicia we estimate the number of required beds at ICUs and compare the predictions with the real data. cache = ./cache/cord-284617-uwby8r3y.txt txt = ./txt/cord-284617-uwby8r3y.txt === reduce.pl bib === id = cord-322577-5bboc1z0 author = Parola, Anna title = Mental Health Through the COVID-19 Quarantine: A Growth Curve Analysis on Italian Young Adults date = 2020-10-02 pages = extension = .txt mime = text/plain words = 6597 sentences = 319 flesch = 47 summary = Despite several recent psychological researches on the coronavirus disease 2019 (COVID-19) pandemic highlighting that young adults represent a high risk category, no studies specifically focused on young adults' mental health status have been carried out yet. This study aimed to assess and monitor Italian young adults' mental health status during the first 4 weeks of lockdown through the use of a longitudinal panel design. The Syndromic Scales of Adult Self-Report 18-59 were used to assess the internalizing problems (anxiety/depression, withdrawn, and somatic complaints), externalizing problems (aggressive, rule-breaking, and intrusive behavior), and personal strengths. CONCLUSIONS: The results contributed to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to plan and develop efficient intervention projects able to take care of young adults' mental health in the long term. This study assessed and monitored Italian young adults' mental health status during the firsts 4 weeks of lockdown imposed by the government during the COVID-19 outbreak, from March 16 to April 16. cache = ./cache/cord-322577-5bboc1z0.txt txt = ./txt/cord-322577-5bboc1z0.txt === reduce.pl bib === id = cord-320666-cmqj8get author = Walach, H. title = What association do political interventions, environmental and health variables have with the number of Covid-19 cases and deaths? A linear modeling approach date = 2020-06-22 pages = extension = .txt mime = text/plain words = 7117 sentences = 444 flesch = 56 summary = Results: We fitted two models with log-linearly linked variables on gamma-distributed outome variables (CoV2 cases and Covid-19 related deaths, standardized on population). Population standardized cases were best predicted by number of tests, life-expectancy in a country, and border closure (negative predictor, i.e. preventive). Population standardized deaths were best predicted by time, the virus had been in the country, life expectancy, smoking (negative predictor, i.e. preventive), and school closures (positive predictor, i.e. accelerating). The model predicting Covid-19 related deaths is presented in Table 3 : Here the duration the infection had been in the country is a significant positive predictor, and so is life expectancy. The major findings of this modeling study using population data for 40 countries are clear, if surprising: Life-expectancy emerges as a stable positive predictor both for standardized cases of CoV2 infections, as well as for Covid-19 related deaths. cache = ./cache/cord-320666-cmqj8get.txt txt = ./txt/cord-320666-cmqj8get.txt === reduce.pl bib === id = cord-324254-qikr9ryf author = Lyócsa, Štefan title = FX Market Volatility Modelling: Can we use low-frequency data? date = 2020-09-30 pages = extension = .txt mime = text/plain words = 5763 sentences = 342 flesch = 56 summary = With an increased forecast horizon, low-frequency volatility models become competitive, suggesting that if high-frequency data are not available, low-frequency data can be used to estimate and predict long-term volatility in FX markets. Despite the wide interest of academia, the existing literature provides evidence only that i) volatility estimators based on high-frequency data are theoretically preferred (Andersen et al., 1 The basic specification of the HAR model has also been enhanced, e.g., by the inclusion of semivariances (Patton and Sheppard, 2015) , the disentanglement of the realized volatility into continuous and jump components (e.g., Andersen et al., 2012) , the introduction of the measurement error of the realized volatility into the HAR model as in (Bollerslev et al., 2016) , the inclusion of nontrading volatility components (Lyócsa and Molnár, 2017, Lyócsa and Todorova, 2020) , and the use of hidden Markov chains (Luo et al., 2019) . cache = ./cache/cord-324254-qikr9ryf.txt txt = ./txt/cord-324254-qikr9ryf.txt === reduce.pl bib === id = cord-326314-9ycht8gi author = Armstrong, Eve title = Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation date = 2020-11-02 pages = extension = .txt mime = text/plain words = 5087 sentences = 274 flesch = 48 summary = We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Second, given noiseless measurements, a temporal baseline of 101 days is sufficient for the SDA procedure to capture the general trends in the evolution of the model populations, the detection probabilities, and the time-varying transmission rate following the implementation of social distancing. Other avenues for expansion are as follows: 1) define additional model parameters as unknowns to be estimated, including the fraction of patients hospitalized, the fraction who enter critical care, and the various timescales governing the reaction equations; 2) impose various constraints regarding the unknown time-varying quantities, particularly transmission rate K i (t), and identifying which forms permit a solution consistent with measurements; 3) examine model sensitivity to the initial numbers within each population; 4) examine model sensitivity to the temporal frequency of data sampling. cache = ./cache/cord-326314-9ycht8gi.txt txt = ./txt/cord-326314-9ycht8gi.txt === reduce.pl bib === id = cord-326409-m3rgspxc author = Lai, Alvin C.K. title = Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors date = 2007-03-24 pages = extension = .txt mime = text/plain words = 3568 sentences = 196 flesch = 51 summary = authors: Lai, Alvin C.K.; Chen, F.Z. title: Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors Results reveal that the standard k–ε Lagrangian model over-predicts particle deposition compared to the present turbulence-corrected Lagrangian approach. In the present work, we compared particle distribution and deposition rates for a small model chamber by the two approaches. (1), while within the concentration boundary layer, the particle wall flux is determined with a one-dimensional semi-empirical particle deposition model (Lai and Nazaroff, 2000) and the results are substituted into Eq. Overall speaking, the results modeled by the two approaches agree well with each other; as the particle size increases, the deposition fraction increases. For submicron particles, the deposition fractions predicted by Lagrangian (without near-wall turbulent correction) is higher than those predicted with correction and Eulerian drift flux prediction follows. Modeling indoor particle deposition from turbulent flow onto smooth surfaces cache = ./cache/cord-326409-m3rgspxc.txt txt = ./txt/cord-326409-m3rgspxc.txt === reduce.pl bib === id = cord-326280-kjjljbl5 author = Abdo, Mohammed S. title = Existence theory and numerical analysis of three species prey–predator model under Mittag-Leffler power law date = 2020-05-27 pages = extension = .txt mime = text/plain words = 3516 sentences = 306 flesch = 62 summary = Newly, to overcome Caputo-Fabrizio's problem, Atangana and Baleanu (AB) in [13] have proposed a new modified version of a fractional derivative with the aid of a generalized Mittag-Leffler function (MLF) as a nonsingular kernel and being nonlocal. However, recently, there has been great interest in studying the behavior of the solution for some biological systems using fractional differential equations involving the Atangana-Baleanu operator by several authors for the purpose of investigating several real-world systems and modeling infectious diseases; see [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] . Also, the existence results and analytic solutions of fractional-order dynamics of COVID-19 with ABC derivative has been obtained in [34] . Due to the success of this operator in modeling the biological systems and infectious diseases, we have studied the dynamical behavior of the mathematical model which describes three prey-predator species by a nonlocal Atangana-Baleanu-Caputo (ABC) derivative operator with 0 < α ≤ 1 as cache = ./cache/cord-326280-kjjljbl5.txt txt = ./txt/cord-326280-kjjljbl5.txt === reduce.pl bib === id = cord-325738-c800ynvc author = Shi, Pengpeng title = SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration date = 2020-02-20 pages = extension = .txt mime = text/plain words = 2669 sentences = 158 flesch = 53 summary = We established a new SEIR propagation dynamics model, that considered the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and isolate comprehensively. Through the Euler integration algorithm to solve the model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted. In this paper, we established a new SEIR propagation dynamics model, considering the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and quarantine comprehensively. Based on this new SEIR propagation dynamics model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted. cache = ./cache/cord-325738-c800ynvc.txt txt = ./txt/cord-325738-c800ynvc.txt === reduce.pl bib === id = cord-324230-nu0pn2q8 author = Ardabili, S. F. title = COVID-19 Outbreak Prediction with Machine Learning date = 2020-04-22 pages = extension = .txt mime = text/plain words = 7335 sentences = 451 flesch = 53 summary = This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). In the present study, the frequently used algorithms, (i.e., genetic algorithm (GA), particle swarm optimizer (PSO) and grey wolf optimizer (GWO)) are employed to estimate the parameters by solving a cost function. In the present research, two frequently used ML methods, the multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS) are employed for the prediction of the outbreak in the five countries. According to Tables 5 to 12 , GWO provided the highest accuracy (smallest RMSE and largest correlation coefficient) and smallest processing time compared to PSO and GA for fitting the logistic, linear, logarithmic, quadratic, cubic, power, compound, and exponential-based equations for all five countries. cache = ./cache/cord-324230-nu0pn2q8.txt txt = ./txt/cord-324230-nu0pn2q8.txt === reduce.pl bib === id = cord-325321-37kyd8ak author = Iftikhar, H. title = Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study date = 2020-09-22 pages = extension = .txt mime = text/plain words = 2618 sentences = 144 flesch = 57 summary = title: Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study In this work, we used five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple Exponential Smoothing (SES) models for forecasting confirmed, death and recovered cases. The findings show that the time series models are useful in predicting COVID-19 confirmed, deaths and recovered cases. In this work, the COVID-19 confirmed, deaths and recovered counts times series are plotted in Figure 1 (left-column) daily and Figure 1 (right-column) cumulative cases. The main purpose of this work was to forecast confirmed, deaths and recovered cases of COVID-19 for Pakistan using five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple exponential smoothing (SES) models. cache = ./cache/cord-325321-37kyd8ak.txt txt = ./txt/cord-325321-37kyd8ak.txt === reduce.pl bib === id = cord-325862-rohhvq4h author = Zhang, Yong title = Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 date = 2020-06-04 pages = extension = .txt mime = text/plain words = 5899 sentences = 259 flesch = 47 summary = The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The main contributions of this work, therefore, include 1) the first application of FDEs in modeling the evolution of the COVID-19 death toll, 2) an updated SEIR model with a transient recovery rate to better capture the dynamics of COVID-19 pandemic within China and for other countries, and 3) a particle-tracking approach based on stochastic bimolecular reaction theory to evaluate the mitigation of the spread of the COVID-19 outbreak. cache = ./cache/cord-325862-rohhvq4h.txt txt = ./txt/cord-325862-rohhvq4h.txt === reduce.pl bib === id = cord-329256-7njgmdd1 author = Leecaster, Molly title = Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics date = 2011-04-21 pages = extension = .txt mime = text/plain words = 4534 sentences = 228 flesch = 48 summary = METHODS: The goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves. Regression analysis was used to explore the relationship between the initial exponential growth rate and the epidemic season characteristics of size, days to peak, and length using the seven epidemic seasons of RSV data from PCMC. The fit statistics for the models with either transmission parameter or Table 1 Observed RSV epidemic size, start date, days to peak, duration, and 4-week exponential growth detection fraction estimated as a constant across epidemic year did not differ substantially from those from the saturated model (Table 4) . cache = ./cache/cord-329256-7njgmdd1.txt txt = ./txt/cord-329256-7njgmdd1.txt === reduce.pl bib === id = cord-326540-1r4gm2d4 author = Liu, Yuliang title = Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically date = 2020-03-11 pages = extension = .txt mime = text/plain words = 7093 sentences = 367 flesch = 44 summary = [28] [29] [30] [31] In this paper, we sought to propose an auxiliary diagnosis algorithm that can not only diagnose hyperlipidemia rapidly and accurately according to human hematological parameters but also provide diagnostic markers automatically, which improves the objectivity of traditional methods and the interpretability of deep learning model algorithm. The research method of diagnostic markers based on deep learning technology proposed in this paper can not only automatically synthesize large quantities of data but also effectively simplify the research process, thus reducing the research cost, as shown in Figure 2 . In this paper, an algorithm of attention deep learning is proposed which has the potential to automatically diagnose hyperlipidemia with human hematological parameters and provide the diagnostic markers and the importance of different markers for the diagnosis results at the same time. cache = ./cache/cord-326540-1r4gm2d4.txt txt = ./txt/cord-326540-1r4gm2d4.txt === reduce.pl bib === id = cord-329276-tfrjw743 author = Ledzewicz, Urszula title = On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies date = 2020-09-30 pages = extension = .txt mime = text/plain words = 12517 sentences = 624 flesch = 51 summary = We discuss various aspects of the modeling of the dynamics (such as growth and interaction terms), modeling of treatment (including pharmacometrics of the drugs), and give special attention to the choice of the objective functional to be minimized. , m, represent the administration of the therapies (dose rates) and as variables are separated from the effects of the actions (which, for example, depend on the concentrations), then a model which is linear in the controls is not only adequate, but is the correct one. Choosing the objective functional in the form (17) with N = 0 (as we do not consider an immune boost), optimal chemotherapy protocols follow the concatenation structure 1s01 with 1 representing a full dose segment, s denoting administration following a singular control and 0 standing for a rest-period of the treatment. cache = ./cache/cord-329276-tfrjw743.txt txt = ./txt/cord-329276-tfrjw743.txt === reduce.pl bib === id = cord-326908-l9wrrapv author = Duchêne, David A. title = Evaluating the Adequacy of Molecular Clock Models Using Posterior Predictive Simulations date = 2015-07-10 pages = extension = .txt mime = text/plain words = 7596 sentences = 370 flesch = 47 summary = We test the power of this approach using simulated data and find that the method is sensitive to bias in the estimates of branch lengths, which tends to occur when using underparameterized clock models. 2001) ; uncorrelated beta-distributed rate variation among lineages; misleading node-age priors (i.e., node calibrations that differ considerably from the true node ages); and when data were generated under a strict clock but analyzed with an underparameterized substitution model ( fig. The substitution model was identified as inadequate for the coronavirus data set by the multinomial test statistic estimated using posterior predictive data sets from a clock analysis (P < 0.05); however, it was identified as adequate when using a clock-free method (P = 0.20). In addition, our metric of uncertainty in posterior predictive branch lengths is sensitive to some cases of misspecification of clock models and node-age priors, but not to substitution model misspecification, as shown for our analyses of the coronavirus data set. cache = ./cache/cord-326908-l9wrrapv.txt txt = ./txt/cord-326908-l9wrrapv.txt === reduce.pl bib === id = cord-324924-5f7b02yq author = Agarwal, A. title = A TRANSPARENT, OPEN-SOURCE SIRD MODEL FOR COVID19DEATH PROJECTIONS IN INDIA date = 2020-06-04 pages = extension = .txt mime = text/plain words = 4425 sentences = 267 flesch = 64 summary = . https://doi.org/10.1101/2020.06.02.20119917 doi: medRxiv preprint Transparency: Since our model is based parameters well-documented in epidemiological theory, we can do a sanity check on the inferred values to see if they agree with what is known at this point of time. To estimate R we leverage open-source, real-time social distancing data published by Google [5] , which allows us to model various mitigation measures by just two parameters as described below. . https://doi.org/10.1101/2020.06.02.20119917 doi: medRxiv preprint Table 1 : Raw and smooth social distancing data for three different regions from 15 Feb baseline day is the median value from the 5-week period Jan 3 -Feb 6, 2020. Across a row, we vary the number data points we fit the model on, and obtain projections for the remaining times and compare them to the actual death counts. cache = ./cache/cord-324924-5f7b02yq.txt txt = ./txt/cord-324924-5f7b02yq.txt === reduce.pl bib === id = cord-326831-dvg0isgt author = Muhammad, L. J. title = Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery date = 2020-06-21 pages = extension = .txt mime = text/plain words = 2707 sentences = 145 flesch = 52 summary = The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor. Data mining algorithm which includes decision tree, support vector machine, naive Bayes, logistic regression random forest, and K-nearest neighbor were applied directly on the dataset using python programming language to develop the models. cache = ./cache/cord-326831-dvg0isgt.txt txt = ./txt/cord-326831-dvg0isgt.txt === reduce.pl bib === id = cord-330148-yltc6wpv author = Lessler, Justin title = Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases date = 2016-07-02 pages = extension = .txt mime = text/plain words = 5911 sentences = 247 flesch = 34 summary = Uncertainty was largely addressed through scenario-based approaches (e.g., different future epidemic trajectories were presented for different plausible sets of parameters), and for the most part, different aspects of the transmission dynamics were derived from independent studies, with only the growth rate (i.e., doubling time) estimated from incidence data. These recent attempts to quickly characterize the properties of emerging diseases are emblematic of an increasing focus on developing statistical methods, grounded in dynamical models, to estimate key epidemic parameters based on diverse data sources. High-resolution geographic data can gain additional power when paired with mechanistic models that capture changes in disease risk, as in recent analyses that accounted for the effect of birth, natural infection, and vaccine disruptions driving increases in measles susceptibility and epidemic risk in the wake of the Ebola outbreak [63] . The formal statistical integration of population genetic and epidemic models allows us to estimate the critical epidemiological parameters such as the basic reproductive number directly from pathogen sequence data [75] [76] [77] . cache = ./cache/cord-330148-yltc6wpv.txt txt = ./txt/cord-330148-yltc6wpv.txt === reduce.pl bib === id = cord-330668-7aw17jf8 author = Chen, Cheng-Chang title = ORF8a of SARS-CoV forms an ion channel: Experiments and molecular dynamics simulations date = 2011-02-28 pages = extension = .txt mime = text/plain words = 4806 sentences = 274 flesch = 56 summary = The protein is synthesized using solid phase peptide synthesis and reconstituted into artificial lipid bilayers that forms cation-selective ion channels with a main conductance level of 8.9±0.8pS at elevated temperature (38.5°C). Computational modeling studies including multi nanosecond molecular dynamics simulations in a hydrated POPC lipid bilayer are done with a 22 amino acid transmembrane helix to predict a putative homooligomeric helical bundle model. Before embedding low energy models into lipid bilayers two amino acids residues of the protein were added at the N and C termini of each of the helices in each bundle model to account for the consequences of their interaction with the lipid bilayer during the simulation. The idealized monomeric TM helix based on the consensus sequence Leu-3 to Val-20 (Fig. 1A) shows clustering of hydrophilic residues (Thr-8, Ser-11, Ser-14 and Thr-18) on one side suggesting that the four hydrophilic amino acids form the lumen of the pore in a homooligomeric helical bundle channel model. cache = ./cache/cord-330668-7aw17jf8.txt txt = ./txt/cord-330668-7aw17jf8.txt === reduce.pl bib === id = cord-332412-lrn0wpvj author = Ibrahim, Mohamed R. title = Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe date = 2020-04-24 pages = extension = .txt mime = text/plain words = 6819 sentences = 309 flesch = 56 summary = Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe. Relying on deep learning, we introduce a novel variational Long-Short Term Memory (LSTM) autoencoder model to forecast the spread of coronavirus per country across the globe. The main advantages of the proposed method are: 1) It can structure and learns from different data sources, either that belongs to spatial adjacency, urban and population factors, or various historical related data, 2) the model is flexible to apply to different scales, in which currently, it can provide prediction at global and country scales, however, it can be also applied to city level. cache = ./cache/cord-332412-lrn0wpvj.txt txt = ./txt/cord-332412-lrn0wpvj.txt === reduce.pl bib === id = cord-330474-c6eq1djd author = Fox, J title = Rapid translation of clinical guidelines into executable knowledge: a case study of COVID‐19 and on‐line demonstration date = 2020-06-18 pages = extension = .txt mime = text/plain words = 3323 sentences = 156 flesch = 45 summary = The initial goal is to assess whether the platform is adequate for rapidly building executable models of clinical expertise, while the longer term goal is to use the resulting COVID‐19 knowledge model as a reference and resource for medical training, research and, with partners, develop products and services for better patient care. The Polyphony project was initiated on 18 March 2020 with the following mission To create, validate, publish and maintain knowledge of best medical practice regarding the detection, diagnosis and management of COVID-19 infections, in a computer executable form. The purpose is to provide a resource for clinicians and researchers, healthcare provider organisations, technology developers and other users, to (1) develop point of care products and services which (2) embody best clinical practice in decision-making, workflow, data analysis and other "intelligent" services across the COVID patient journey. cache = ./cache/cord-330474-c6eq1djd.txt txt = ./txt/cord-330474-c6eq1djd.txt === reduce.pl bib === id = cord-332729-f1e334g0 author = Shah, Nirav R. title = An Impact-Oriented Approach to Epidemiological Modeling date = 2020-09-21 pages = extension = .txt mime = text/plain words = 1638 sentences = 115 flesch = 52 summary = 5 The Centers for Disease Control and Prevention (CDC) recently added policy development as a sixth item in its list of the major tasks of epidemiology in public health, but there remains no mention of the impact on the general public. For instance, the Covid Act Now (CAN) model is fully open-source, along with its data inputs (available at https://covidactnow.org). Both the New York Times and Georgetown University's Center for Global Health, Science, and Security (available at https://covidamp.org/) have begun to collect data on COVID-19 policies by state and effective dates, including shelter-in-place and reopening orders. These eight considerations may enable COVID-19 data and models to become better harbingers of actionable, behavior-changing, and even life-saving information; to bridge the gap between scientific public health expertise and mainstream, layperson Are the data and model's mechanisms and data sources publicly available for fact-checking and validation? cache = ./cache/cord-332729-f1e334g0.txt txt = ./txt/cord-332729-f1e334g0.txt === reduce.pl bib === id = cord-329534-deoyowto author = McBryde, Emma S. title = Role of modelling in COVID-19 policy development date = 2020-06-18 pages = extension = .txt mime = text/plain words = 3143 sentences = 155 flesch = 42 summary = Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. In this paper, we describe ways in which models have influenced policy, from the early stages of the outbreak to the current date -and anticipate the future value of models in informing suppression efforts, vaccination programs and economic interventions. For COVID-19, strategies may differ between countries depending on the acuity of the epidemic, the age groups driving the infection or at higher risk for severe disease, and the age structure of the population. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. cache = ./cache/cord-329534-deoyowto.txt txt = ./txt/cord-329534-deoyowto.txt === reduce.pl bib === id = cord-332583-5enha3g9 author = Bodine, Erin N. title = Agent-Based Modeling and Simulation in Mathematics and Biology Education date = 2020-07-28 pages = extension = .txt mime = text/plain words = 7586 sentences = 358 flesch = 44 summary = ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms. Whether students are working with ABMs in life science or math modeling classes, it is helpful for them to learn how to read and understand flow diagrams as they are often included in research publications that use agent-based modeling. While not every student necessarily needs to take a course exclusively focused on agent-based modeling, every undergraduate biology student should have the opportunity to utilize an ABM to perform experiments and to collect and analyze data. cache = ./cache/cord-332583-5enha3g9.txt txt = ./txt/cord-332583-5enha3g9.txt === reduce.pl bib === id = cord-327784-xet20fcw author = Rieke, Nicola title = The future of digital health with federated learning date = 2020-09-14 pages = extension = .txt mime = text/plain words = 5658 sentences = 273 flesch = 42 summary = We envision a federated future for digital health and with this perspective paper, we share our consensus view with the aim of providing context and detail for the community regarding the benefits and impact of FL for medical applications (section "Datadriven medicine requires federated efforts"), as well as highlighting key considerations and challenges of implementing FL for digital health (section "Technical considerations"). FL addresses this issue by enabling collaborative learning without centralising data (subsection "The promise of federated efforts") and has already found its way to digital health applications (subsection "Current FL efforts for digital health"). Current FL efforts for digital health Since FL is a general learning paradigm that removes the data pooling requirement for AI model development, the application range of FL spans the whole of AI for healthcare. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation cache = ./cache/cord-327784-xet20fcw.txt txt = ./txt/cord-327784-xet20fcw.txt === reduce.pl bib === id = cord-333088-ygdau2px author = Roy, Manojit title = On representing network heterogeneities in the incidence rate of simple epidemic models date = 2006-03-31 pages = extension = .txt mime = text/plain words = 7645 sentences = 397 flesch = 52 summary = We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We also demonstrate the existence of a complex dynamical behavior in the stochastic system within the narrow small-world region, consisting of persistent cycles with enhanced amplitude and a well-defined period that are not predicted by the equivalent homogeneous mean-field model. cache = ./cache/cord-333088-ygdau2px.txt txt = ./txt/cord-333088-ygdau2px.txt === reduce.pl bib === === reduce.pl bib === id = cord-330596-p4k7jexz author = Hu, Ji title = An integrated classification model for incremental learning date = 2020-10-21 pages = extension = .txt mime = text/plain words = 4518 sentences = 252 flesch = 51 summary = However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. This method consists of two parts: a pre-trained (Pt) model and a novel Truncated Gradient Confidence-weighted online classification model (TGCW). Online learning is a continuous training process in which input values are fed into the model in each round of training, and the model outputs prediction results based on the current parameters [16] . In this section, we propose a new online learning algorithm suitable for binary classification of streamed data, named TGCW, which aims to further improve the prediction accuracy and feature selection capability of the model. In addition, we will also look for improved pre-trained models or use more classifiers for integrated learning to improve the classification accuracy of complex data. cache = ./cache/cord-330596-p4k7jexz.txt txt = ./txt/cord-330596-p4k7jexz.txt === reduce.pl bib === id = cord-332618-8al98ya2 author = Barraza, Néstor Ruben title = A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic date = 2020-09-18 pages = extension = .txt mime = text/plain words = 4603 sentences = 307 flesch = 62 summary = We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Hence, 35 we propose a different model based on a Pure Birth process with an event rate that, like Polya's, depends on both the elapsed time and the number of previous events, but with a different functional form. Our main motivation is to obtain a model that describes an epidemic outbreak at its first stage, before it reaches the inflection point in the case incidence curve, which is useful to monitor how contagion is spreading out. Since the mean value function of the Polya-Lundberg process is a linear function of time (see Appendix B), we introduce a modification in the event rate in order to get a mean value function that grows 85 subexponentially with either positive or negative concavity as we observe in the early epidemic growth curves usually reported. cache = ./cache/cord-332618-8al98ya2.txt txt = ./txt/cord-332618-8al98ya2.txt === reduce.pl bib === id = cord-331646-j5mkparg author = Sze To, G. N. title = Review and comparison between the Wells–Riley and dose‐response approaches to risk assessment of infectious respiratory diseases date = 2009-07-31 pages = extension = .txt mime = text/plain words = 11058 sentences = 551 flesch = 45 summary = Dose‐response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Dose-response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Some newer studies have proposed to use dose-response models for assessing the infection risk of airborne-transmissible pathogens (e.g., Armstrong and Haas, 2007a; Nicas, 1996; Sze To et al., 2008) . Some Review of the Wells-Riley and dose-response models studies also suggested that the deposition loss of infectious particles and the viability loss of pathogens while airborne can also be considered by adding these sink terms in the denominator, similar to Equation 11 (Fisk et al., 2005; Franchimon et al., 2008) : cache = ./cache/cord-331646-j5mkparg.txt txt = ./txt/cord-331646-j5mkparg.txt === reduce.pl bib === === reduce.pl bib === id = cord-331849-o346txxr author = Cardoso, Pedro J.S. title = Computational Science in the Interconnected World: Selected papers from 2019 International Conference on Computational Science date = 2020-09-21 pages = extension = .txt mime = text/plain words = 2569 sentences = 136 flesch = 36 summary = Against this background, the International Conference on Computational Science (ICCS), annually held since 2001, has grown to become a major event in the CS field, with hundreds of experts meeting and discussing their works, along with keynote lectures presented by world's renowned researchers. As matter of fact, the context in which this editorial paper is being written, just a few months after the declaration of the COVID-19 pandemic, highlights the importance of this interconnected world and keeps CS in the forefront of the needs, reflected in the epidemiological research that is supported in computational methods [3] [6] or the proved accuracy of many disease propagation models [7] [12] . Their study pays attention to random data access with data recurrence as major issue to attain performance, proposing a method to avoid these data races for high performance on many-core CPU architectures with wide single instruction, multiple data (SIMD) units, exemplified by finite-element earthquake simulations. cache = ./cache/cord-331849-o346txxr.txt txt = ./txt/cord-331849-o346txxr.txt === reduce.pl bib === id = cord-331374-3gau0vmc author = Giorgi, Gabriele title = Expatriates’ Multiple Fears, from Terrorism to Working Conditions: Development of a Model date = 2016-10-13 pages = extension = .txt mime = text/plain words = 7417 sentences = 362 flesch = 44 summary = Structural equation model analyses showed that fear of expatriation mediates the relationship of mental health with fear of economic crisis and with perceived dangerous working conditions. Then, a structural model was performed to estimate the fit to the data of the hypothesized model in which fear of expatriation mediates the relationship of mental health problems with economic stress and perceived dangerous working conditions (Hypotheses 1 and 2) . A CFA was, therefore, performed with Mplus, version 7.11 (Muthén and Muthén, 1998-2010) , with the four variables measuring mental health problems, fear of expatriation, economic stress, and perceived dangerous working conditions. Next, we compared the hypothesized model with a nonmediation model (Model 3), which only included direct paths from mental health problems and fear of expatriation to economic stress and perceived dangerous working conditions. Furthermore, because mental health problems, fear of expatriation, economic stress, and perceived dangerous working conditions were all measured at the same time, reverse relationships could also be expected between the four variables. cache = ./cache/cord-331374-3gau0vmc.txt txt = ./txt/cord-331374-3gau0vmc.txt === reduce.pl bib === id = cord-332093-iluqwwxs author = Lessler, Justin title = Mechanistic Models of Infectious Disease and Their Impact on Public Health date = 2016-02-17 pages = extension = .txt mime = text/plain words = 5501 sentences = 231 flesch = 38 summary = Though never published by Reed and Frost (versions of the model were eventually published by their students (3, 4) ), their model was one of the first mechanistic models of infectious disease transmission, and at a time long before digital computing, they may have been the first to use simulation methods to understand the epidemic process. Perhaps the first mechanistic model of infectious disease transmission used in assessing intervention strategies was a mathematical model of malaria transmission developed and refined by Ronald Ross in a series of papers published between 1908 and 1921 (9) (10) (11) , pre-dating the work of Reed and Frost by decades. The aforementioned work, particularly that of the World Health Organization Ebola Response Team, also characterized important aspects of Ebola's natural history and epidemiology, including its basic reproductive number (R 0 ), the decline in R over the course of the epidemic, the incubation period, and the serial interval, properties of the disease that will be important to understand should it re-emerge. cache = ./cache/cord-332093-iluqwwxs.txt txt = ./txt/cord-332093-iluqwwxs.txt === reduce.pl bib === === reduce.pl bib === id = cord-333490-8pv5x6tz author = Liao, Yi title = Early box office prediction in China’s film market based on a stacking fusion model date = 2020-10-06 pages = extension = .txt mime = text/plain words = 5979 sentences = 309 flesch = 56 summary = Specifically, combining Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and k-Nearest Neighbor (KNN) algorithms, we establish a stacking model for box office prediction during a film's early stage of production (shooting period). (2015) added MPAA rating, competition, star value, sequels, and the number of screens to the prediction variables and proposed a pre-release box office prediction model based on a dynamic artificial neural network algorithm. Post-release prediction In addition to pre-release features, it also includes a large amount of theatre data, heat index, and audience comment information It contains the most information and the best predictive effectiveness, but the application value of the results is very low Next, we compare the contribute factors and the effectiveness of box office prediction at different stages (Table 1 ). Considering the availability of data and the predictive power of features, five pre-production factors are selected based on the film itself: genre, star value, release date, release area, and sequels. cache = ./cache/cord-333490-8pv5x6tz.txt txt = ./txt/cord-333490-8pv5x6tz.txt === reduce.pl bib === id = cord-332922-2qjae0x7 author = Mbuvha, Rendani title = Bayesian inference of COVID-19 spreading rates in South Africa date = 2020-08-05 pages = extension = .txt mime = text/plain words = 3224 sentences = 169 flesch = 52 summary = In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. In this work we combine Bayesian inference with the compartmental SEIR and SIR models to infer time varying spreading rates that allow for quantification of the impact of government interventions in South Africa. SIR and SEIR model parameter inference was performed using confirmed cases data up to and including 20 April 2020 and MCMC samplers described in the methodology section. cache = ./cache/cord-332922-2qjae0x7.txt txt = ./txt/cord-332922-2qjae0x7.txt === reduce.pl bib === id = cord-335465-sckfkciz author = Gupta, Rishi K. title = Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date = 2020-09-25 pages = extension = .txt mime = text/plain words = 5052 sentences = 246 flesch = 33 summary = We aimed to address this knowledge gap by systematically evaluating the performance of proposed prognostic models, among consecutive patients hospitalised with a final diagnosis of COVID-19 at a single centre, when using predictors measured at the point of hospital admission. We also assessed the discrimination of each candidate model for standardised outcomes of: (a) our composite endpoint of clinical deterioration; and (b) mortality, across a range of pre-specified time horizons from admission (7 days, 14 days, 30 days and any time during hospital admission), by calculating time-dependent AUROCs (with cumulative sensitivity and dynamic specificity) [18] . In order to further benchmark the performance of candidate prognostic models, we then computed AUROCs for a limited number of univariable predictors considered to be of highest importance a priori, based on clinical knowledge and existing data, for prediction of our composite endpoints of clinical deterioration and mortality (7 days, 14 days, 30 days and any time during hospital admission). cache = ./cache/cord-335465-sckfkciz.txt txt = ./txt/cord-335465-sckfkciz.txt === reduce.pl bib === === reduce.pl bib === id = cord-335418-s8ugu8e1 author = Annan, James D title = Model calibration, nowcasting, and operational prediction of the COVID-19 pandemic date = 2020-04-17 pages = extension = .txt mime = text/plain words = 4075 sentences = 205 flesch = 57 summary = We present a simple operational nowcasting/forecasting scheme based on a joint state/parameter estimate of the COVID-19 epidemic at national or regional scale, performed by assimilating the time series of reported daily death numbers into a simple SEIR model. This system generates estimates of the current reproductive rate, Rt, together with predictions of future daily deaths and clearly outperforms a number of alternative forecasting systems that have been presented recently. In this work, we focus on the the current reproductive rate of the epidemic, R t , as the main parameter of interest, and also on the reported number of daily deaths, both as being the most reliable source of data (i.e., our observations O in the application of Bayes' Theorem above) and also the primary forecast variable of interest to the public and policy makers. We have presented a simple data assimilation method that simultaneously calibrates and initialises a SEIR model for nowcasting and forecasting the COVID-19 epidemic at national and regional scale. cache = ./cache/cord-335418-s8ugu8e1.txt txt = ./txt/cord-335418-s8ugu8e1.txt === reduce.pl bib === id = cord-338466-7uvta990 author = Singh, Brijesh P. title = Modeling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlook date = 2020-10-31 pages = extension = .txt mime = text/plain words = 9001 sentences = 478 flesch = 57 summary = For the spread of COVID-19, when disease dynamics are still unclear, mathematical modeling helps us to estimate the cumulative number of positive cases in the present scenarios. There are already various measures such as social distancing, lockdown masking and washing hand regularly has been implemented to prevent the spread of COVID-19, but in absence of particular medicine and vaccine it is very important to predict how the infection is likely to develop among the population that support prevention of the disease and aid in the preparation of healthcare service. The logistic growth regression model is used for the estimation of the final size and its peak time of the COVID-19 pandemic in many countries of the World and found similar result obtained by SIR model (Batista, 2020) . cache = ./cache/cord-338466-7uvta990.txt txt = ./txt/cord-338466-7uvta990.txt === reduce.pl bib === id = cord-333693-z2ni79al author = Wu, Lin title = Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion date = 2020-08-06 pages = extension = .txt mime = text/plain words = 723 sentences = 43 flesch = 40 summary = title: Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. We addressed these issues by presenting an interactive individual-based simulator, which is capable of modeling an epidemic through multi-source information fusion. However, oversimplified models are not capable of incorporating multi-type uncertain information like clinical courses, viral shedding, subclinical transmission, infections, confirmations, deaths, or interventions, so they cannot reduce uncertainty by multi-source information fusion. To tackle the three challenges of modelling epidemic dynamics, we have developed an interactive simulator for individual-based models in this paper. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia Estimates of the severity of coronavirus disease 2019: a model-based analysis cache = ./cache/cord-333693-z2ni79al.txt txt = ./txt/cord-333693-z2ni79al.txt === reduce.pl bib === id = cord-330714-hhvap8ts author = Shah, Kamal title = Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan date = 2020-11-12 pages = extension = .txt mime = text/plain words = 4537 sentences = 296 flesch = 57 summary = This work is the consideration of a fractal fractional mathematical model on the transmission and control of corona virus (COVID-19), in which the total population of an infected area is divided into susceptible, infected and recovered classes. For the last few decades, it is noted that arbitrary-order equations of differentiations (FDEs) and integrations (FIDEs) can be use for modeling real world problems by much better way than integer order ODEs, PDEs and IDEs. In the 1750s when "Reimann and Liouvilli", "Euler and Fourier" give interesting analytical results in integer order of differential and integral calculus. [33, 54, 55, 62] Let us take the continuous and differentiable mapping ℧(t) in (a, b) with 0 < r ≤ 1 order, then the fractal-arbitrary order derivative of ℧(t) in ABC form with fractional order 0 < ω ≤ 1 and the law of power is given as cache = ./cache/cord-330714-hhvap8ts.txt txt = ./txt/cord-330714-hhvap8ts.txt === reduce.pl bib === id = cord-340244-qjf23a7e author = Bernstein, Daniel J. title = Further analysis of the impact of distancing upon the COVID-19 pandemic date = 2020-04-16 pages = extension = .txt mime = text/plain words = 7064 sentences = 439 flesch = 63 summary = The 22 March 2020 paper "Social distancing strategies for curbing the COVID-19 epidemic" [5] reports calculations in a model where distancing reduces R 0 by at most 60%, and claims that 60% is "on par with the reduction in R 0 achieved in China through intense social distancing measures (3)". The paper [5] claims, within its model, that the (37.5, 10.0) distancing strategy explained above achieves the "goal of keeping the number of critical care patients below 0.89 per 10,000 adults" under the following assumptions: wintertime R 0 = 2, and distancing achieves a 60% reduction in R 0 . The paper [5] claims that increasing critical-care capacity "allows population immunity to be accumulated more rapidly, reducing the overall duration of the epidemic and the total length of social distancing measures". The third (more optimistic) plot takes the wintertime R 0 to be 2.0 and uses an extended model where "intense" distancing has more of an effect, reducing R 0 by 99%. cache = ./cache/cord-340244-qjf23a7e.txt txt = ./txt/cord-340244-qjf23a7e.txt === reduce.pl bib === id = cord-340354-j3xsp2po author = Noll, N. B. title = COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2 date = 2020-05-07 pages = extension = .txt mime = text/plain words = 4322 sentences = 252 flesch = 50 summary = Thus, to make such modeling widely available, we have developed an interactive, online tool that allows users to efficiently explore COVID-19 scenarios based upon different epidemiological assumptions and potential mitigation strategies. All source code and the aggregated surveillance data are made freely available through GitHub. We approximate the dynamics of a COVID-19 outbreak using a generalized SEIR model in which the population is partitioned into age-stratified compartments of: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), ICU overflow (O), dead (D) and recovered (R) individuals (Kermack et al., 1927) . The parameters of the model fall into three broad categories: a time-dependent infection rate β a (t); the rate of transition out of the exposed, infectious, hospitalized, and critical/overflow compartments γ e , γ i , γ h , and γ c respectively; and the age-specific fractions m a , c a and f a of mild, critical, and fatal infections respectively. cache = ./cache/cord-340354-j3xsp2po.txt txt = ./txt/cord-340354-j3xsp2po.txt === reduce.pl bib === === reduce.pl bib === id = cord-335689-8a704p38 author = Martin, Gerardo title = Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk date = 2018-03-19 pages = extension = .txt mime = text/plain words = 8557 sentences = 462 flesch = 53 summary = We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. One approach to identify areas at risk from emerging infectious diseases is to model the ecological niche of the causal agent and its reservoir host with spatiality explicit climatic data, and to use the model to predict their geographic distribution (Escobar and Craft 2016) . We took the following steps to build these models: (1) assigned presence points to the most likely reservoir host species present at spillover locations, (2) computed the optimal size of spatial units and determined appropriate explanatory climatic variables, (3) selected the model structure (linear and quadratic terms and interactions with AIC and cross-validation), (4) selected priors for the Bayesian model, (5) fitted the Bayesian model, (6) cross-validated, and (7) transferred models to climate change scenarios (Fig. 1 ). cache = ./cache/cord-335689-8a704p38.txt txt = ./txt/cord-335689-8a704p38.txt === reduce.pl bib === id = cord-340805-qbvgnr4r author = Ioannidis, John P.A. title = Forecasting for COVID-19 has failed date = 2020-08-25 pages = extension = .txt mime = text/plain words = 6084 sentences = 313 flesch = 56 summary = Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence. cache = ./cache/cord-340805-qbvgnr4r.txt txt = ./txt/cord-340805-qbvgnr4r.txt === reduce.pl bib === === reduce.pl bib === === reduce.pl bib === id = cord-333919-nrd9ajj2 author = Albi, G. title = Relaxing lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty date = 2020-05-16 pages = extension = .txt mime = text/plain words = 7707 sentences = 409 flesch = 52 summary = In this work, starting from a compartmental model with a social structure, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. The heterogeneity of the procedures used to carry out the disease positivity tests, the delays in recording and reporting the results, and the large percentage of asymptomatic patients (in varying percentages depending on the studies and the countries but estimated by WHO at an average of around 80% of cases) make the construction of predictive scenarios affected by high uncertainty [28, 33, 44] . We present different simulation scenarios for various countries where the epidemic wave is underway, including Germany, France, Italy, Spain, the United Kingdom and the United States showing the effect of relaxing the lockdown measures in a selective way on the various social activities. cache = ./cache/cord-333919-nrd9ajj2.txt txt = ./txt/cord-333919-nrd9ajj2.txt === reduce.pl bib === id = cord-342591-6joc2ld1 author = Higazy, M. title = Novel Fractional Order SIDARTHE Mathematical Model of The COVID-19 Pandemic date = 2020-06-13 pages = extension = .txt mime = text/plain words = 3689 sentences = 247 flesch = 47 summary = The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. In addition, we study an optimal control plans for the fractional order SIDARTHE model via four control strategies that include the availability of vaccination and existence of treatments for the infected detected three population fraction phases. Applying the fractional order differential equations numerical solver using MATLAB software, we show the dynamics of the state variables of the model and display the effect of changing the fractional derivative order on the system response. We also implement the optimal control strategies numerically for the fractional order SIDARTHE model. Figure 9 displays the phase plane of state variables: total infected ( ) and susceptible cases (S(t)) with different fractional derivative order . cache = ./cache/cord-342591-6joc2ld1.txt txt = ./txt/cord-342591-6joc2ld1.txt === reduce.pl bib === === reduce.pl bib === id = cord-340713-v5sdowb7 author = Bird, Jordan J. title = Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach date = 2020-10-28 pages = extension = .txt mime = text/plain words = 5669 sentences = 260 flesch = 53 summary = The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. The classification problem of risk is therefore formulated based on prior knowledge of the pandemic in terms of class only, but the attributes to attempt to classify them are purely country-level information regardless of number of cases, deaths and other coronavirus specific data. Country-level pandemic risk and preparedness classification based on COVID-19 data Fig 10 shows a comparison of other models that were explored. Country-level pandemic risk and preparedness classification based on COVID-19 data Table 1 shows the predicted class values for the best models applied to each of the respective risk classification problems. cache = ./cache/cord-340713-v5sdowb7.txt txt = ./txt/cord-340713-v5sdowb7.txt === reduce.pl bib === id = cord-337897-hkvll3xh author = Yang, Zheng Rong title = Peptide Bioinformatics- Peptide Classification Using Peptide Machines date = 2009 pages = extension = .txt mime = text/plain words = 7631 sentences = 495 flesch = 54 summary = The earlier work was to investigate a set of experimentally determined (synthesized) functional peptides to find some conserved amino acids, referred In protease cleavage site prediction, we commonly use peptides with a fixed length. The bio-basis function method has been successfully applied to various peptide classification tasks, for instance, the prediction of trypsin cleavage sites [ 9 ] , the prediction of HIV cleavage sites [ 10 ] , the prediction of hepatitis C virus protease cleavage sites [ 16 ] , the prediction of the disorder segments in proteins [ 7 , 17 ] , the prediction of protein phosphorylation sites [ 18 , 19 ] , the prediction of the O-linkage sites in glycoproteins [ 20 ] , the prediction of signal peptides [ 21 ] , the prediction of factor Xa protease cleavage sites [ 22 ] , the analysis of mutation patterns of HIV-1 Fig. 9 . cache = ./cache/cord-337897-hkvll3xh.txt txt = ./txt/cord-337897-hkvll3xh.txt === reduce.pl bib === id = cord-336747-8m7n5r85 author = Grossmann, G. title = Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study date = 2020-05-08 pages = extension = .txt mime = text/plain words = 7033 sentences = 432 flesch = 55 summary = In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We calibrate both, ODE-models and stochastic models with interaction structure to the same basic reproduction number R 0 or to the same infection peak and compare the corresponding results. In the last decade, research focused largely on epidemic spreading, where interactions were constrained by contact networks, i.e. a graph representing the individuals (as nodes) and their connectivity (as edges). SIS-type models require knowledge of the spreading parameters (infection strength, recovery rate, etc.) and the contact network, which can partially be inferred from real-world observations. We are interested in the relationship between the contact network structure, R 0 , the height and time point of the infection-peak, and the number of individuals ultimately affected by the epidemic. cache = ./cache/cord-336747-8m7n5r85.txt txt = ./txt/cord-336747-8m7n5r85.txt === reduce.pl bib === id = cord-336687-iw3bzy0m author = Kraemer, M. U. G. title = Big city, small world: density, contact rates, and transmission of dengue across Pakistan date = 2015-10-06 pages = extension = .txt mime = text/plain words = 4517 sentences = 231 flesch = 41 summary = Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of 'mixing parameters' when disaggregating data across an urban–rural gradient. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. In no application of the TSIR model to date has the potential for variation in these parameters been assessed, leaving the extent to which inhomogeneity of mixing varies across space and time as an open question in the study of infectious disease dynamics. To assess the potential for spatial variation in the inhomogeneity of mixing as it pertains dengue transmission, we performed an analysis of district-level time series of dengue transmission in the Punjab province of Pakistan using a TSIR model with separate mixing parameters for urban and rural districts. cache = ./cache/cord-336687-iw3bzy0m.txt txt = ./txt/cord-336687-iw3bzy0m.txt === reduce.pl bib === id = cord-343701-x5rghsbs author = Zhao, Yu-Feng title = Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models date = 2020-06-25 pages = extension = .txt mime = text/plain words = 5030 sentences = 241 flesch = 53 summary = Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. On this basis, a rolling grey Verhulst model and its derived models were established to predict the change trend of the number of cases of COVID-19 infection in China. Based on a rolling mechanism, the rolling grey Verhulst model and its derived models for predicting the number of patients infected with COVID-19 in China were constructed by adding the latest data and removing the earliest data. The results showed that the rolling grey Verhulst model and its derived models could accurately predict the changes in the number of confirmed patients in China. cache = ./cache/cord-343701-x5rghsbs.txt txt = ./txt/cord-343701-x5rghsbs.txt === reduce.pl bib === id = cord-339374-2hxnez28 author = De Kort, Hanne title = Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors date = 2020-09-22 pages = extension = .txt mime = text/plain words = 9182 sentences = 388 flesch = 31 summary = The overall SDM framework is not just an interesting tool for identifying areas of local conservation concern or areas not yet occupied but potentially suitable; it has the potential to contribute substantially to the global protection of biodiversity and ecosystem services threatened by multiple environmental stressors, including land-use change and habitat fragmentation, climate change, invasive alien species, pollution, and overexploitation (Franklin, 2013; Kok et al., 2017; Wiens, Stralberg, Jongsomjit, Howell, & Snyder, 2009 ). Patches occupied by larger butterflies (representing better dispersers) are predicted to be accessible due to dispersal evolution (after DeKort, Prunier, et al., 2018) boost opens the door for comparing and synthesizing published SDM studies for answering taxon-wide and large-scale research questions, including the role of species' traits and evolutionary potential in driving general species distribution shifts in response to land-use change. cache = ./cache/cord-339374-2hxnez28.txt txt = ./txt/cord-339374-2hxnez28.txt === reduce.pl bib === id = cord-344115-gtbkwuqv author = Grimm, Volker title = Three questions to ask before using model outputs for decision support date = 2020-09-30 pages = extension = .txt mime = text/plain words = 1920 sentences = 116 flesch = 50 summary = Without knowing its purpose, it is impossible to assess whether a model's outputs can be used to support decisions affecting the real world. Decision makers can quickly understand which aspects of the real world are included, and which are excluded, by assessing: what entities are present in the model (e.g., individuals, populations, companies), what state variables characterize these entities (e.g., age, nationality, bank balance), what processes (e.g., movement patterns, meeting rates) link entities and their variables to system dynamics, and what are the temporal and spatial resolution and extent? The three screening questions support decision makers to assess whether a model is suitable for addressing real-world decisions and provide a common language for communication. The three questions do not replace more detailed guidelines on GMP 6,7 , but they provide a simple and effective common language that will allow us to develop models and use their outputs for decision support in a more transparent, robust, and safe way. cache = ./cache/cord-344115-gtbkwuqv.txt txt = ./txt/cord-344115-gtbkwuqv.txt === reduce.pl bib === id = cord-342855-dvgqouk2 author = Anzum, R. title = Mathematical Modeling of Coronavirus Reproduction Rate with Policy and Behavioral Effects date = 2020-06-18 pages = extension = .txt mime = text/plain words = 3086 sentences = 188 flesch = 56 summary = In this paper a modified mathematical model based on the SIR model used which can predict the spreading of the corona virus disease (COVID-19) and its effects on people in the days ahead. Since reducing the face to face contact among people and staying home in lockdown can improve in reducing the further infection rate, ܴ is taken as a time varying constant rather than a fixed one to observe the overall scenario of coronavirus spreading. According to the conventional SIR model, for the N number of constant population, each of whom may be in one of five states with respect to time implies that: While a susceptible person can be affected by the disease when comes in contact with an infectious person. The SIR model is used to predict the vulnerability of any type pandemic which may not be applicable to coronavirus cases since this model assumes the reproduction rate as a constant. cache = ./cache/cord-342855-dvgqouk2.txt txt = ./txt/cord-342855-dvgqouk2.txt === reduce.pl bib === id = cord-340827-vx37vlkf author = Jackson, Matthew O. title = Chapter 14 Diffusion, Strategic Interaction, and Social Structure date = 2011-12-31 pages = extension = .txt mime = text/plain words = 13725 sentences = 754 flesch = 56 summary = Seminal studies by Ryan and Gross (1943) and Griliches (1957) examined the effects of social connections on the adoption of a new behavior, specifically the adoption of hybrid corn in the U.S. Looking at aggregate adoption rates in different states, these authors illustrated that the diffusion of hybrid corn followed an S-shape curve over time: starting out slowly, accelerating, and then ultimately decelerating. The shape of the distribution F determines which equilibria are tipping points: equilibria such that only a slight addition to the fraction of agents choosing the action 1 shifts the population, under the best response dynamics, to the next higher equilibrium level of adoption (we return to a discussion of tipping and stable points when we consider a more general model of strategic interactions on networks below). While the above models provide some ideas about how social structure impacts diffusion, they are limited to settings where, roughly speaking, the probability that a given individual adopts a behavior is simply proportional to the infection rate of neighbors. cache = ./cache/cord-340827-vx37vlkf.txt txt = ./txt/cord-340827-vx37vlkf.txt === reduce.pl bib === id = cord-346309-hveuq2x9 author = Reis, Ben Y title = An Epidemiological Network Model for Disease Outbreak Detection date = 2007-06-26 pages = extension = .txt mime = text/plain words = 8419 sentences = 382 flesch = 46 summary = CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events. In order to both improve overall detection performance and reduce vulnerability to baseline shifts, we introduce a general class of epidemiological network models that explicitly capture the relationships among epidemiological data streams. In order to evaluate the practical utility of this approach for surveillance, we constructed epidemiological network models based on real-world historical health-care data and compared their outbreak-detection performance to that of standard historical models. In this study, the researchers developed a new class of surveillance systems called ''epidemiological network models.'' These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams). cache = ./cache/cord-346309-hveuq2x9.txt txt = ./txt/cord-346309-hveuq2x9.txt === reduce.pl bib === id = cord-346265-jx4kspen author = Tatapudi, Hanisha title = Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region date = 2020-10-19 pages = extension = .txt mime = text/plain words = 5638 sentences = 315 flesch = 52 summary = In this paper, we investigate a few 'what-if' scenarios for social intervention policies including if the stay-at-home order were not lifted, if the Phase II order continues unaltered, what impact will the universal face mask usage have on the infections and deaths, and finally, how do the benefits of contact tracing vary with various target levels for identifying asymptomatic and pre-symptomatic. We conduct our investigation by first developing a comprehensive agent-based simulation model for COVID-19, and then using a major urban outbreak region (Miami-Dade County hospitalization (if infected with acute illness); and 10) recovery or death (if infected). The model also considers: varying levels of compliances for isolation and quarantine, lower on-site staffing levels of essential work and community places during stay-at-home order, restricted daily schedule of people during various social intervention periods, phased lifting of interventions, use of face masks in workplaces, schools and community places with varying compliance levels, and contact tracing with different target levels to identify asymptomatic and presymptomatic cases. cache = ./cache/cord-346265-jx4kspen.txt txt = ./txt/cord-346265-jx4kspen.txt === reduce.pl bib === id = cord-346921-3hfxv6h8 author = Nave, OPhir title = Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition date = 2020-09-21 pages = extension = .txt mime = text/plain words = 3197 sentences = 179 flesch = 58 summary = In this study, we apply the singular perturbed vector field (SPVF) method to the COVID-19 mathematical model of to expose the hierarchy of the model. This decomposition enables us to rewrite the model in new coordinates in the form of fast and slow subsystems and, hence, to investigate only the fast subsystem with different asymptotic methods. We found the stable equilibrium points of the mathematical model and compared the results of the model with those reported by the Chinese authorities and found a fit of approximately 96 percent. After we transformed and presented the model in the new coordinates using the eigenvectors of the SPVF method, the model can be decomposed into the fast and slow subsystems based on the gap of the eigenvalues. As we have shown in the previous section, we obtain the stable equilibrium points of the mathematical model owing to the application of the SPVF method. cache = ./cache/cord-346921-3hfxv6h8.txt txt = ./txt/cord-346921-3hfxv6h8.txt === reduce.pl bib === id = cord-347906-3ehsg8oi author = Zhang, Zizhen title = Dynamics of COVID-19 mathematical model with stochastic perturbation date = 2020-08-28 pages = extension = .txt mime = text/plain words = 1774 sentences = 176 flesch = 58 summary = title: Dynamics of COVID-19 mathematical model with stochastic perturbation Thirdly, we examine the threshold of the proposed stochastic COVID-19 model, when noise is small or large. The same set of parameter values and initial conditions for deterministic models will lead to an ensemble of different outputs. They obtained the condition of the disease extinction and persistence according to noise and threshold of the deterministic system. Similarly, several authors discussed the same conditions for stochastic models; see [32] [33] [34] [35] [36] [37] [38] [39] . To study the effects of the environment on spreading of COVID-19 and make the research more realistic, first we formulate a stochastic mathematical COVID-19 model. In this section, a COVID-19 mathematical model with random perturbation is formulated as follows: The extinction and persistence of the stochastic SIS epidemic model with vaccination A stochastic differential equation SIS epidemic model cache = ./cache/cord-347906-3ehsg8oi.txt txt = ./txt/cord-347906-3ehsg8oi.txt === reduce.pl bib === id = cord-346136-sqc09x9c author = Hamilton, Kyra title = Application of the Health Action Process Approach to Social Distancing Behavior During COVID‐19 date = 2020-10-02 pages = extension = .txt mime = text/plain words = 8263 sentences = 356 flesch = 36 summary = Given that social distancing is a key evidence-based behavior that will minimise transmission of SARS-CoV-2 if performed consistently at the population level, the aim of the present study was to apply the HAPA to identify the social cognition and self-regulatory determinants of this preventive behavior in samples of adults from two countries, Australia and the US. The study adopted a prospective correlational design with self-report measures of HAPA constructs (attitudes, self-efficacy, risk perceptions, intentions, action planning, coping planning, and action control) and past engagement in social distancing behavior administered at an initial time-point (T1) in a survey administered using the Qualtrics TM online survey tool. The present research has a number of strengths including focus on social distancing, a key preventive behavior aimed at reducing transmission of SARS-CoV-2 to prevent COVID-19 infections; adoption of a fit-for-purpose theoretical model, the HAPA, that provides a set of a priori predictions on the motivational and volitional determinants of the target behavior; recruitment of samples from two countries, Australia and the US, with key demographic characteristics that closely match those of the population; and the use of prospective study design and structural equation modelling techniques. cache = ./cache/cord-346136-sqc09x9c.txt txt = ./txt/cord-346136-sqc09x9c.txt === reduce.pl bib === id = cord-350240-bmppif8g author = Girardi, Paolo title = Robust inference for nonlinear regression models from the Tsallis score: application to COVID‐19 contagion in Italy date = 2020-08-12 pages = extension = .txt mime = text/plain words = 3228 sentences = 181 flesch = 57 summary = title: Robust inference for nonlinear regression models from the Tsallis score: application to COVID‐19 contagion in Italy In particular, we focus on deaths and intensive care unit hospitalizations data, that are expected to aid the detection of the time when the peaks and the upper asymptotes of contagion, both in daily new cases and total cases, are reached, so that preventive measures (such as mobility restrictions) can be applied and/or relaxed. In contrast, the asymptotic distribution of the scoring rule ratio statisis a linear combination of independent chi-square random variables with coefficients related to the eigenvalues of the matrix J(θ)K(θ) −1 (Dawid et al., 2016) . The robust fits (Tsallis estimates and 95% confidence intervals) of the parameters e (inflection point) and d (upper asymptote) for the models are summarized in Tables 1 and 2 for DD and ICU, respectively. cache = ./cache/cord-350240-bmppif8g.txt txt = ./txt/cord-350240-bmppif8g.txt === reduce.pl bib === id = cord-350603-ssen3q08 author = Albrecht, Randy A. title = Moving Forward: Recent Developments for the Ferret Biomedical Research Model date = 2018-07-17 pages = extension = .txt mime = text/plain words = 1607 sentences = 80 flesch = 37 summary = While widely recognized for its utility in influenza virus research, ferrets are used for a variety of infectious and noninfectious disease models due to the anatomical, metabolic, and physiological features they share with humans and their susceptibility to many human pathogens. To resolve this, a group of researchers from around the world are working together to develop validated reagents and assays to improve our understanding of the innate and adaptive immune responses in the ferret. Flow Cytometric and cytokine ELISPOT approaches to characterize the cell-mediated immune response in ferrets following influenza virus Infection Screening monoclonal antibodies for cross-reactivity in the ferret model of influenza infection Infection of ferrets with influenza virus elicits a light chain-biased antibody response against hemagglutinin Ferrets as a novel animal model for studying human respiratory syncytial virus infections in immunocompetent and immunocompromised hosts A neutralizing human monoclonal antibody protects against lethal disease in a new ferret model of acute Nipah virus infection cache = ./cache/cord-350603-ssen3q08.txt txt = ./txt/cord-350603-ssen3q08.txt === reduce.pl bib === id = cord-344417-1seb8b09 author = Wang, Yuhang title = SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection date = 2020-10-03 pages = extension = .txt mime = text/plain words = 8230 sentences = 540 flesch = 57 summary = In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. To obtain enhanced text representations for fake news detection, we especially take into account the content structure-both global semantic relationship and local sequential order among sentences in a news document. Finally, we feed the enhanced sentence representations into the LSTM-based network sequentially, and obtain the informative document representation by max-pooling, which is further used for fake news detection. RQ3 What is the effect of LSTM, which is used to model the global sequential order information in the process of learning entire document-level representations for improving the fake news detection performance? cache = ./cache/cord-344417-1seb8b09.txt txt = ./txt/cord-344417-1seb8b09.txt === reduce.pl bib === id = cord-344252-6g3zzj0o author = Farooq, Junaid title = A Novel Adaptive Deep Learning Model of Covid-19 with focus on mortality reduction strategies date = 2020-07-21 pages = extension = .txt mime = text/plain words = 6951 sentences = 361 flesch = 56 summary = We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. In this work, we employ deep learning to propose an Artificial Neural Network (ANN) based real-time online incremental learning technique to estimate parameters of a data stream guided analytical model of Covid-19 to study the transmission dynamics and prevention mechanism for SARS-Cov-2 novel coronavirus in order to aid in optimal policy formulation, efficient decision making, forecasting and simulation. To the best of our knowledge, this paper develops for the first time a deep learning model of epidemic diseases with data science approach in which parameters are intelligently adapted to the new ground realities with fast evolving infection dynamics. cache = ./cache/cord-344252-6g3zzj0o.txt txt = ./txt/cord-344252-6g3zzj0o.txt === reduce.pl bib === id = cord-347791-wofyftrs author = Hao, Tian title = Prediction of Coronavirus Disease (covid-19) Evolution in USA with the Model Based on the Eyring Rate Process Theory and Free Volume Concept date = 2020-04-22 pages = extension = .txt mime = text/plain words = 3603 sentences = 206 flesch = 57 summary = title: Prediction of Coronavirus Disease (covid-19) Evolution in USA with the Model Based on the Eyring Rate Process Theory and Free Volume Concept A modification arguing that the human movement energy may change with time is made on our previous infectious disease model, in which infectious disease transmission is considered as a sequential chemical reaction and reaction rate constants obey the Eyrings rate process theory and free volume concept. For better fitting data, modification is made on our previous model 1 by introducing an idea that the energy for human individuals to transmit diseases is time dependent, which is in line with other systems like granular powder under tapping process where the energy of particles is time dependent, too 18 . Infection Dynamics of Coronavirus Disease 2019 (Covid-19) Modeled with the Integration of the Eyring Rate Process Theory and Free Volume Concept cache = ./cache/cord-347791-wofyftrs.txt txt = ./txt/cord-347791-wofyftrs.txt === reduce.pl bib === id = cord-350510-o4libq5d author = Grinfeld, M. title = On Linear Growth in COVID-19 Cases date = 2020-06-22 pages = extension = .txt mime = text/plain words = 2154 sentences = 121 flesch = 65 summary = We present an elementary model of COVID-19 propagation that makes explicit the connection between testing strategies and rates of transmission and the linear growth in new cases observed in many parts of the world. MODELS We derive, in its simplest and most illuminating form, a system of two difference equations for the rate of growth of new positive test results and the number of people that have been exposed to the virus; that is, we neglect the asymptomatics. (A5) We assume that the information stream is dominated by the rate of increase of the numbers of new positive tests. It would be interesting to investigate models in which g is a function of more than the last day's data, or of undominated maxima in the number of new cases, but we assume here for simplicity that R(n) is a reasonable proxy for the information stream. cache = ./cache/cord-350510-o4libq5d.txt txt = ./txt/cord-350510-o4libq5d.txt === reduce.pl bib === id = cord-347199-slq70aou author = Safta, Cosmin title = Characterization of partially observed epidemics through Bayesian inference: application to COVID-19 date = 2020-10-07 pages = extension = .txt mime = text/plain words = 8406 sentences = 455 flesch = 54 summary = The method is cast as one of Bayesian inference of the latent infection rate (number of people infected per day), conditioned on a time-series of Developing a forecasting method that is applicable in the early epoch of a partially-observed outbreak poses some peculiar difficulties. This infection rate curve is convolved with the Probability Density Function (PDF) of the incubation period of the disease to produce an expression for the time-series of newly symptomatic cases, an observable that is widely reported as "daily new cases" by various data sources [2, 5, 6] . 2, with postulated forms for the infection rate curve and the derivation of the prediction for daily new cases; we also discuss a filtering approach that is applied to the data before using it to infer model parameters. cache = ./cache/cord-347199-slq70aou.txt txt = ./txt/cord-347199-slq70aou.txt === reduce.pl bib === id = cord-347952-k95wrory author = Prieto, Diana M title = A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels date = 2012-03-30 pages = extension = .txt mime = text/plain words = 9202 sentences = 433 flesch = 38 summary = Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Of the existing computer simulation models addressing PHP, those focused on disease spread and mitigation of pandemic influenza (PI) have been recognized by the public health officials as useful decision support tools for preparedness planning [1] . cache = ./cache/cord-347952-k95wrory.txt txt = ./txt/cord-347952-k95wrory.txt === reduce.pl bib === id = cord-348010-m3a3utvz author = Wolff, Michael title = On build‐up of epidemiologic models—Development of a SEI(3)RSD model for the spread of SARS‐CoV‐2 date = 2020-10-13 pages = extension = .txt mime = text/plain words = 13018 sentences = 991 flesch = 61 summary = (Adequate contacts, reproduction and contact numbers) (i) A contact is called adequate (also effective), if it leads to a transmission of the pathogen from an infectious person to another one, and, if the affected individual is susceptible, then an infection is provoked. In the case of concrete models one uses generally contact and replacement numbers, and , which reflect the current infection behaviour. (i) (Closed-population model) An assumed constant number of community members (see Remark 2.2) seems to be justified, if the infection spreads quickly, approximately within a year, and/or, if there is a balance between births, migration and non-disease-related deaths. (Using , there arise difficulties with the dot indicating the time derivation.) If the model is to be to take a latent period into account, the class of infected is divided into subclasses in the following way. cache = ./cache/cord-348010-m3a3utvz.txt txt = ./txt/cord-348010-m3a3utvz.txt === reduce.pl bib === id = cord-352348-2wtyk3r5 author = Sabroe, Ian title = Identifying and hurdling obstacles to translational research date = 2007 pages = extension = .txt mime = text/plain words = 5307 sentences = 229 flesch = 39 summary = The quality of our scientific output (perceived as a change in disease incidence and/or the development of a therapy) is largely dependent on the quality of the input data and the methods for their processing and interpretation, although the process of generating effective translational science is not as linear (that is, from molecules to models to humans) as is often thought. These revolve around our understanding of the nature of the translational process, the integration of the outputs of different technological approaches to disease, the use of models, access to tissues and appropriate materials, and the need for support in increasingly complex areas such as ethics and bioinformatics. Such debates might facilitate the comparison of data between laboratories and between species, and might highlight the components of specific diseases that are ripe for the development of new in vivo models and protocols (for example, there remains a great need to more effectively model the role of the innate immune system in acute and chronic asthma), broadening the number of disease processes or phenotypes that are modelled in pathology. cache = ./cache/cord-352348-2wtyk3r5.txt txt = ./txt/cord-352348-2wtyk3r5.txt === reduce.pl bib === id = cord-350870-a89zj5mh author = Ikeda, Hiroki title = Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model date = 2014-05-21 pages = extension = .txt mime = text/plain words = 5059 sentences = 261 flesch = 54 summary = Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. To estimate the accuracy of the parameters estimated by our two novel models, we created simulated time course data of target cell densities and viral load during the acute phase of viral infection (lasting approximately 21 days [14] [15] [16] [17] [18] [19] [20] [26] [27] [28] ) assuming biologically plausible parameter values. We created artificial data with target cell densities and virus loads during acute infection using the reduced standard model for viral infection (i.e., Eqs. cache = ./cache/cord-350870-a89zj5mh.txt txt = ./txt/cord-350870-a89zj5mh.txt === reduce.pl bib === id = cord-350001-pd2bnqbp author = Liu, L. title = Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility date = 2020-08-07 pages = extension = .txt mime = text/plain words = 5516 sentences = 276 flesch = 53 summary = We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. On the other hand, compartmental models [e.g., 1, 6, 7] assume a flexible, causal story for the spread of a disease and can also incorporate mobility data as a covariate for predicting the time-varying infection rate of a disease. However, most often though we don't know the parameters of the model beforehand, but we do have some data that can provide a learning signal to fit the parameters, One such signal is the daily number of new cases of a disease, which can be predicted by a compartmental model as the change in I + R between each day. cache = ./cache/cord-350001-pd2bnqbp.txt txt = ./txt/cord-350001-pd2bnqbp.txt === reduce.pl bib === id = cord-351411-q9kqjvvf author = Moghadas, Seyed M title = Improving public health policy through infection transmission modelling: Guidelines for creating a Community of Practice date = 2015 pages = extension = .txt mime = text/plain words = 3882 sentences = 189 flesch = 37 summary = Its objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. The workshop objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. In the final session, "Developing our network and communities of practice", participants reflected on earlier presentations and discussions to clarify what is needed to continue collaboration and knowledge exchange that can increase the value of research modelling in public health. cache = ./cache/cord-351411-q9kqjvvf.txt txt = ./txt/cord-351411-q9kqjvvf.txt === reduce.pl bib === id = cord-352431-yu7kxnab author = Langbeheim, Elon title = Science Teachers’ Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module date = 2020-08-25 pages = extension = .txt mime = text/plain words = 7931 sentences = 432 flesch = 45 summary = It examines the factors shaping the teachers' self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. Surprisingly, the short interaction with computational modeling increased the group's self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Therefore, the goal of this study is to examine science teachers' attitudes towards introducing computational model construction in the context of inquiry-based learning in physics. The first research question asked how do teachers' prior experiences in teaching physics influence their self-efficacy and attitudes towards inquiry-based learning practices in a PD workshop. 2. In order to investigate the 2nd research question regarding the influence of teachers' prior involvement with programming on their self-efficacy in, and experience of computational modeling that involves coding in a PD workshop, we used the following data sources: cache = ./cache/cord-352431-yu7kxnab.txt txt = ./txt/cord-352431-yu7kxnab.txt === reduce.pl bib === id = cord-354627-y07w2f43 author = pinter, g. title = COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach date = 2020-05-06 pages = extension = .txt mime = text/plain words = 5478 sentences = 337 flesch = 50 summary = As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. Due to the complex nature of the COVID-19 outbreak and its irregularity in different countries, the standard epidemiological models, i.e., susceptible-infected-resistant (SIR)-based models, had been challenged for delivering higher performance in individual nations. In this study the hybrid machine learning model of MLP-ICA and ANFIS are used to predict the COVID-19 outbreak in Hungary. Both machine learning models, as an alternative to epidemiological models, showed potential in predicting COVID-19 outbreak as well as estimating total mortality. cache = ./cache/cord-354627-y07w2f43.txt txt = ./txt/cord-354627-y07w2f43.txt === reduce.pl bib === id = cord-352543-8il0dh58 author = Kuzdeuov, A. title = A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies date = 2020-05-06 pages = extension = .txt mime = text/plain words = 6262 sentences = 311 flesch = 47 summary = In this scenario, epidemiological models can be used to project the future course of the disease, and to estimate the impact of non-pharmaceutical interventions (NPIs) and related control measures that might be used to slow the contagion, and thereby provide time to enhance health care resources and develop effective immunological defenses such as new vaccines. We have developed and implemented a network-based stochastic epidemic simulator (leveraging our prior work [8] ) which models cities and regions as nodes in a graph, and the edges between nodes representing transit links of roads, railways, and air travel routes to model the mobility of inhabitants amongst cities. In each node, the simulator runs a compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) model, such that individuals can cycle through the four stages based on state transition probabilities. cache = ./cache/cord-352543-8il0dh58.txt txt = ./txt/cord-352543-8il0dh58.txt === reduce.pl bib === id = cord-353200-5csewb1k author = Jehi, Lara title = Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19 date = 2020-08-11 pages = extension = .txt mime = text/plain words = 4344 sentences = 226 flesch = 40 summary = OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. Hospitalization risk prediction and outcomes in COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0237419 August 11, 2020 2 / 15 ethical restrictions by the Cleveland clinic regulatory bodies including the institutional review Board and legal counsel. We also develop and validate a statistical model that can assist with individualized prediction of hospitalization risk for a patient with COVID-19. cache = ./cache/cord-353200-5csewb1k.txt txt = ./txt/cord-353200-5csewb1k.txt === reduce.pl bib === id = cord-354254-89vjfkfd author = Peng, Shanbi title = The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control date = 2020-08-31 pages = extension = .txt mime = text/plain words = 7520 sentences = 420 flesch = 48 summary = Inspired by the impact of COVID-19, this review summarizes research works of pathogen transmission based on CFD methods with different models and algorithms. Defining the pathogen as the particle or gaseous in CFD simulation is a common method and epidemic models are used in some investigations to rise the authenticity of calculation. The Re-Normalization Group (RNG) k-ε was used in simulation in order to solve the turbulence with the good performance of accuracy, efficiency and robustness; In Gao and Niu [45] study, RNG k-ε model including the effect of low-Reynolds-number is used to solve the airflow and the diffusion of tracer gas which can represent the contaminant transmission are calculated by the equation below: Gao, et.al [102] combined the use of experiment and CFD method to study airborne transmission in different flats of a high-rise building and to verify their simulation, the data of tracer gas experiment from Denmark Aalborg University [103] is used. cache = ./cache/cord-354254-89vjfkfd.txt txt = ./txt/cord-354254-89vjfkfd.txt === reduce.pl bib === id = cord-355102-jcyq8qve author = Avila, Eduardo title = Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios date = 2020-06-29 pages = extension = .txt mime = text/plain words = 4768 sentences = 242 flesch = 39 summary = PURPOSE: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. METHODS: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. In order to evaluate the adequacy and generalization power of the proposed model, as well as its tolerance to handle samples containing missing data (i.e., at least one variable with no informed values), an additional set of 92 samples (10 positives for COVID-19 and 82 negatives) was obtained from the patient database. When no clinical or medical data is available, or when decisions regarding resource management involving multiple symptomatic patients are necessary, the model can be used in multiple individuals simultaneously, aiming to identify those with higher probabilities of presenting positive qRT-PCR results. cache = ./cache/cord-355102-jcyq8qve.txt txt = ./txt/cord-355102-jcyq8qve.txt ===== Reducing email addresses cord-015147-h0o0yqv8 cord-004584-bcw90f5b cord-160382-8n3s5j8w cord-259534-hpyf0uj6 cord-266593-hmx2wy1p cord-263987-ff6kor0c cord-280064-rz8cglyt cord-289325-jhokn5bu cord-283907-ev1ghlwl cord-299932-c079r94n cord-300570-xes201g7 cord-316393-ozl28ztz cord-319378-li77za5e cord-351411-q9kqjvvf cord-353200-5csewb1k Creating transaction Updating adr table ===== Reducing keywords cord-000332-u3f89kvg cord-001687-paax8pqh cord-003377-9vkhptas cord-007129-qjdg46o9 cord-003243-u744apzw cord-011400-zyjd9rmp cord-005321-b3pyg5b3 cord-007147-0v8ltunv cord-004332-99lxmq4u cord-018791-h3bfdr14 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cord-340827-vx37vlkf cord-344417-1seb8b09 cord-352348-2wtyk3r5 cord-351411-q9kqjvvf cord-347199-slq70aou cord-350870-a89zj5mh cord-350001-pd2bnqbp cord-354627-y07w2f43 cord-347952-k95wrory cord-353200-5csewb1k cord-355102-jcyq8qve cord-352543-8il0dh58 cord-352431-yu7kxnab cord-354254-89vjfkfd cord-348010-m3a3utvz Creating transaction Updating pos table Building ./etc/reader.txt cord-264408-vk4lt83x cord-319933-yp9ofhi8 cord-225429-pz9lsaw6 cord-133273-kvyzuayp cord-140624-lphr5prl cord-030681-4brd2efp number of items: 434 sum of words: 2,266,627 average size in words: 7,606 average readability score: 50 nouns: model; models; data; time; disease; number; infection; results; population; rate; cases; parameters; epidemic; system; study; analysis; cells; virus; individuals; transmission; case; dynamics; methods; approach; risk; cell; network; information; function; studies; structure; health; prediction; control; process; level; value; values; order; protein; method; effects; pandemic; example; patients; distribution; state; research; effect; days verbs: uses; based; shown; included; given; considered; provides; follow; made; predict; developed; increase; seen; modelling; found; compare; taking; described; obtained; proposed; represents; estimating; infected; allows; reduced; presents; applied; assuming; generated; require; reported; identifying; leading; determined; observing; performed; studying; define; becomes; needs; learned; induced; results; suggest; known; indicate; related; causing; improve; contained adjectives: different; human; new; social; infectious; high; first; available; many; specific; important; large; infected; non; susceptible; possible; clinical; similar; small; several; real; various; initial; total; covid-19; higher; significant; low; public; single; current; multiple; global; mathematical; effective; key; early; novel; molecular; general; second; complex; random; positive; respiratory; potential; best; particular; experimental; spatial adverbs: also; however; well; therefore; respectively; even; often; first; still; significantly; highly; finally; moreover; furthermore; now; hence; usually; directly; rather; recently; much; relatively; especially; generally; currently; less; together; particularly; instead; widely; typically; specifically; far; previously; already; approximately; fully; easily; additionally; just; yet; almost; similarly; clearly; indeed; mainly; always; commonly; strongly; later pronouns: we; it; our; their; its; they; i; them; one; us; itself; his; he; you; themselves; her; my; your; she; me; u; ourselves; him; s; 's; ours; em; himself; ashcs; mir-3906; herself; theirs; oneself; myself; legionnairesÕ; l(t; π(q; β; yourself; thee; t; r; imagej; https://laurayuliu.com/covid19-panel-forecast/.; adrb1; a129; ; ; −εt; →⊆ proper nouns: COVID-19; Fig; SARS; SIR; China; Table; S; Model; T; CoV-2; C; •; Health; Eq; N; M; Figure; K; A; D; SEIR; May; Italy; US; March; University; F; HIV; Wuhan; CC; April; R; Disease; RNA; II; sha; CT; J; New; India; LSTM; United; ICU; DOI; Coronavirus; −; QSAR; E; Germany; Analysis keywords: model; covid-19; disease; datum; sir; network; cell; sars; human; system; parameter; image; agent; study; structure; risk; time; sequence; seir; result; protein; mouse; lstm; health; transmission; social; rna; patient; individual; icu; fractional; china; case; base; rabbit; qsar; process; prediction; population; infection; hiv; epidemic; drug; cnn; bayesian; animal; user; simulation; rate; number one topic; one dimension: model file(s): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3048541/ titles(s): The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale three topics; one dimension: model; cells; model file(s): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122373/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7095932/, https://doi.org/10.1186/s12889-020-09519-2 titles(s): Compartmental Models in Epidemiology | Oral Communications and Posters | Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews five topics; three dimensions: model models time; model data models; model models structure; human model disease; cells cell human file(s): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431977/, https://arxiv.org/pdf/2007.02847v1.pdf, https://www.ncbi.nlm.nih.gov/pubmed/16444740/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149682/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100641/ titles(s): Dynamic causal modelling of COVID-19 | Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media | Two types of transmembrane homomeric interactions in the integrin receptor family are evolutionarily conserved | Models in Ophthalmology and Vision Research | Abstracts of the 82(nd) Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) and the 18(th) Annual Meeting of the Network Clinical Pharmacology Germany (VKliPha) in cooperation with the Arbeitsgemeinschaft für Angewandte Humanpharmakologie e.V. (AGAH) Type: cord title: keyword-model-cord date: 2021-05-25 time: 15:36 username: emorgan patron: Eric Morgan email: emorgan@nd.edu input: keywords:model ==== make-pages.sh htm files ==== make-pages.sh complex files ==== make-pages.sh named enities ==== making bibliographics id: cord-326280-kjjljbl5 author: Abdo, Mohammed S. title: Existence theory and numerical analysis of three species prey–predator model under Mittag-Leffler power law date: 2020-05-27 words: 3516.0 sentences: 306.0 pages: flesch: 62.0 cache: ./cache/cord-326280-kjjljbl5.txt txt: ./txt/cord-326280-kjjljbl5.txt summary: Newly, to overcome Caputo-Fabrizio''s problem, Atangana and Baleanu (AB) in [13] have proposed a new modified version of a fractional derivative with the aid of a generalized Mittag-Leffler function (MLF) as a nonsingular kernel and being nonlocal. However, recently, there has been great interest in studying the behavior of the solution for some biological systems using fractional differential equations involving the Atangana-Baleanu operator by several authors for the purpose of investigating several real-world systems and modeling infectious diseases; see [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] . Also, the existence results and analytic solutions of fractional-order dynamics of COVID-19 with ABC derivative has been obtained in [34] . Due to the success of this operator in modeling the biological systems and infectious diseases, we have studied the dynamical behavior of the mathematical model which describes three prey-predator species by a nonlocal Atangana-Baleanu-Caputo (ABC) derivative operator with 0 < α ≤ 1 as abstract: In this manuscript, the fractional Atangana–Baleanu–Caputo model of prey and predator is studied theoretically and numerically. The existence and Ulam–Hyers stability results are obtained by applying fixed point theory and nonlinear analysis. The approximation solutions for the considered model are discussed via the fractional Adams Bashforth method. Moreover, the behavior of the solution to the given model is explained by graphical representations through the numerical simulations. The obtained results play an important role in developing the theory of fractional analytical dynamic of many biological systems. url: https://doi.org/10.1186/s13662-020-02709-7 doi: 10.1186/s13662-020-02709-7 id: cord-252903-pg0l92zb author: Abueg, M. title: Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state date: 2020-09-02 words: 7326.0 sentences: 333.0 pages: flesch: 42.0 cache: ./cache/cord-252903-pg0l92zb.txt txt: ./txt/cord-252903-pg0l92zb.txt summary: In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. We use data at the county level to match the population, demographic, and occupational structure of the region, and calibrate the model with epidemiological data from Washington state and Google''s Community Mobility Reports for a time-varying infection rate ( 21 ) . Estimated total infected percentage, total deaths, and peak hospitalized under a 50% reopening scenario (an increase of 50% of the difference between pre-lockdown and post-lockdown network interactions) at various exposure notification adoption rates for King, Pierce, and Snohomish Counties, assuming no change to social distancing after the (t) β baseline and 15 manual contact tracers per 100k people. abstract: Contact tracing is increasingly being used to combat COVID-19, and digital implementations are now being deployed, many of them based on Apple and Google's Exposure Notification System. These systems are new and are based on smartphone technology that has not traditionally been used for this purpose, presenting challenges in understanding possible outcomes. In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. Specifically, we use a representative model of the household and occupational structure of three counties in the state of Washington together with a proposed digital exposure notifications deployment to quantify impacts under a range of scenarios of adoption, compliance, and mobility. In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing. We believe this can serve as guidance to health authorities in Washington state and beyond on how exposure notification systems can complement traditional public health interventions to suppress the spread of COVID-19. url: http://medrxiv.org/cgi/content/short/2020.08.29.20184135v1?rss=1 doi: 10.1101/2020.08.29.20184135 id: cord-311086-i4e0rdxp author: Adekola, Hafeez Aderinsayo title: Mathematical modeling for infectious viral disease: The COVID‐19 perspective date: 2020-08-17 words: 3277.0 sentences: 169.0 pages: flesch: 41.0 cache: ./cache/cord-311086-i4e0rdxp.txt txt: ./txt/cord-311086-i4e0rdxp.txt summary: The SEIR model with suitable adaptations has been widely applied for various disease epidemics such as chickenpox and SARS, and its relevance has been advanced for the analysis of the dynamic transmission of COVID-19 in this context. This sixchambered model was used to study the transmission mechanism of COVID-19 and the implemented prevention and control measures, with the aid of time series and kinetic modal analysis, a basic reproductive number value of 4.01 was obtained (Li, Geng, et al., 2020) . Although the mathematical models for the COVID-19 have majorly forecast few areas relating to pathogen spread such as the basic reproductive number of the SARS-CoV-2, population control measures, percentage of asymptomatic people (Nandal, 2020) . Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan abstract: In this study, we examined various forms of mathematical models that are relevant for the containment, risk analysis, and features of COVID‐19. Greater emphasis was laid on the extension of the Susceptible–Infectious–Recovered (SIR) models for policy relevance in the time of COVID‐19. These mathematical models play a significant role in the understanding of COVID‐19 transmission mechanisms, structures, and features. Considering that the disease has spread sporadically around the world, causing large scale socioeconomic disruption unwitnessed in contemporary ages since World War II, researchers, stakeholders, government, and the society at large are actively engaged in finding ways to reduce the rate of infection until a cure or vaccination procedure is established. We advanced argument for the various forms of the mathematical model of epidemics and highlighted their relevance in the containment of COVID‐19 at the present time. Mathematical models address the need for understanding the transmission dynamics and other significant factors of the disease that would aid policymakers to make accurate decisions and reduce the rate of transmission of the disease. url: https://doi.org/10.1002/pa.2306 doi: 10.1002/pa.2306 id: cord-104486-syirijql author: Adiga, Aniruddha title: Data-driven modeling for different stages of pandemic response date: 2020-09-21 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who is at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision making. As different countries and regions go through phases of the pandemic, the questions and data availability also changes. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real-time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523119/ doi: nan id: cord-312366-8qg1fn8f author: Adiga, Aniruddha title: Mathematical Models for COVID-19 Pandemic: A Comparative Analysis date: 2020-10-30 words: 8797.0 sentences: 472.0 pages: flesch: 49.0 cache: ./cache/cord-312366-8qg1fn8f.txt txt: ./txt/cord-312366-8qg1fn8f.txt summary: As the pandemic takes hold, researchers begin investigating: (i) various intervention and control strategies; usually pharmaceutical interventions do not work in the event of a pandemic and thus nonpharmaceutical interventions are most appropriate, (ii) forecasting the epidemic incidence rate, hospitalization rate and mortality rate, (iii) efficiently allocating scarce medical resources to treat the patients and (iv) understanding the change in individual and collective behavior and adherence to public policies. Like projection approaches, models for epidemic forecasting can be broadly classified into two broad groups: (i) statistical and machine learning-based data-driven models, (ii) causal or mechanistic models-see 29, 30, 2, 31, 32, 6, 33 and the references therein for the current state of the art in this rapidly evolving field. In the context of COVID-19 case count modeling and forecasting, a multitude of models have been developed based on different assumptions that capture specific aspects of the disease dynamics (reproduction number evolution, contact network construction, etc.). abstract: COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article, we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope. url: https://doi.org/10.1007/s41745-020-00200-6 doi: 10.1007/s41745-020-00200-6 id: cord-128991-mb91j2zs author: Agapiou, Sergios title: Modeling of Covid-19 Pandemic in Cyprus date: 2020-10-05 words: 7453.0 sentences: 419.0 pages: flesch: 58.0 cache: ./cache/cord-128991-mb91j2zs.txt txt: ./txt/cord-128991-mb91j2zs.txt summary: Here we report our work including results from statistical and mathematical models used to understand the epidemiology of COVID-19 in Cyprus, during the time period starting from the beginning of March till the end of May 2020. We use change-point detection, count time series methods and compartmental models for short and long term projections, respectively. Testing approaches in the Republic of Cyprus included: a) targeted testing of suspect cases and their contacts; of repatriates at the airport and during their 14-day quarantine; of teachers and students when schools re-opened in mid-May; of employees in essential services that continued their operation throughout the first pandemic wave (e.g., customer services, public domain); and of health-care workers in public hospitals, and b) population screenings following random sampling in the general population of most districts and in two municipalities with increased disease burden. abstract: The Republic of Cyprus is a small island in the southeast of Europe and member of the European Union. The first wave of COVID-19 in Cyprus started in early March, 2020 (imported cases) and peaked in late March-early April. The health authorities responded rapidly and rigorously to the COVID-19 pandemic by scaling-up testing, increasing efforts to trace and isolate contacts of cases, and implementing measures such as closures of educational institutions, and travel and movement restrictions. The pandemic was also a unique opportunity that brought together experts from various disciplines including epidemiologists, clinicians, mathematicians, and statisticians. The aim of this paper is to present the efforts of this new, multidisciplinary research team in modelling the COVID-19 pandemic in the Republic of Cyprus. url: https://arxiv.org/pdf/2010.01927v1.pdf doi: nan id: cord-324924-5f7b02yq author: Agarwal, A. title: A TRANSPARENT, OPEN-SOURCE SIRD MODEL FOR COVID19DEATH PROJECTIONS IN INDIA date: 2020-06-04 words: 4425.0 sentences: 267.0 pages: flesch: 64.0 cache: ./cache/cord-324924-5f7b02yq.txt txt: ./txt/cord-324924-5f7b02yq.txt summary: . https://doi.org/10.1101/2020.06.02.20119917 doi: medRxiv preprint Transparency: Since our model is based parameters well-documented in epidemiological theory, we can do a sanity check on the inferred values to see if they agree with what is known at this point of time. To estimate R we leverage open-source, real-time social distancing data published by Google [5] , which allows us to model various mitigation measures by just two parameters as described below. . https://doi.org/10.1101/2020.06.02.20119917 doi: medRxiv preprint Table 1 : Raw and smooth social distancing data for three different regions from 15 Feb baseline day is the median value from the 5-week period Jan 3 -Feb 6, 2020. Across a row, we vary the number data points we fit the model on, and obtain projections for the remaining times and compare them to the actual death counts. abstract: As India emerges from the lockdown with ever higher COVID19 case counts and a mounting death toll, reliable projections of case numbers and deaths counts are critical in informing policy decisions. We examine various existing models and their shortcomings. Given the amount of uncertainty surrounding the disease we choose a simple SIRD model with minimal assumptions enabling us to make robust predictions. We employ publicly available mobility data from Google to estimate social distancing covariates which influence how fast the disease spreads. We further present a novel method for estimating the uncertainty in our predictions based on first principles. To demonstrate, we fit our model to three regions (Spain, Italy, NYC) where the peak has passed and obtain predictions for the Indian states of Delhi and Maharashtra where the peak is desperately awaited. url: https://doi.org/10.1101/2020.06.02.20119917 doi: 10.1101/2020.06.02.20119917 id: cord-024341-sw2pdnh6 author: Aksyonov, Konstantin title: Development of Cloud-Based Microservices to Decision Support System date: 2020-05-05 words: 3009.0 sentences: 236.0 pages: flesch: 60.0 cache: ./cache/cord-024341-sw2pdnh6.txt txt: ./txt/cord-024341-sw2pdnh6.txt summary: Thus, the urgent task is to choose a dynamic model of a business process and build on its basis a web-service of simulation. 1) accounting for various types of resources [9, 10] ; 2) accounting for the status of operations and resources at specific times; 3) accounting for the conflicts on common resources and means [11, 12] ; 4) modeling of discrete processes; 5) accounting for complex resources (resource instances with properties, in the terminology of queuing systems -application (transaction)); 6) application of a situational approach (the presence of a language for describing situations (a language for representing knowledge) and mechanisms for diagnosing situations and finding solutions (a logical inference mechanism according to the terminology of expert systems); 7) implementation of intelligent agents (DM models); 8) description of hierarchical processes. A service should take one from the model domain with a specific identifier and refer to its many tasks for simulation. abstract: Intelligent systems of simulation become a key stage of the scheduling of companies and industries work. Most of the existing decision support systems are desktop software. Today there is a need to use durability, flexibility, availability and crossplatforming information technologies. The paper proposes the idea of working cloud based decision support system BPsim.Web and this one consists of some set of services and tools. The model of the multiagent resources conversion process is considered. The process of the simulation model developing via BPsim.Web is described. An example of the real process model is given. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198253/ doi: 10.1007/978-3-030-47240-5_9 id: cord-130928-ozxvtvjt author: Albi, G. title: Control with uncertain data of socially structured compartmental epidemic models date: 2020-04-27 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The adoption of containment measures to reduce the amplitude of the epidemic peak is a key aspect in tackling the rapid spread of an epidemic. Classical compartmental models must be modified and studied to correctly describe the effects of forced external actions to reduce the impact of the disease. In addition, data are often incomplete and heterogeneous, so a high degree of uncertainty must naturally be incorporated into the models. In this work we address both these aspects, through an optimal control formulation of the epidemiological model in presence of uncertain data. After the introduction of the optimal control problem, we formulate an instantaneous approximation of the control that allows us to derive new feedback controlled compartmental models capable of describing the epidemic peak reduction. The need for long-term interventions shows that alternative actions based on the social structure of the system can be as effective as the more expensive global strategy. The importance of the timing and intensity of interventions is particularly relevant in the case of uncertain parameters on the actual number of infected people. Simulations related to data from the recent COVID-19 outbreak in Italy are presented and discussed. url: https://arxiv.org/pdf/2004.13067v1.pdf doi: nan id: cord-333919-nrd9ajj2 author: Albi, G. title: Relaxing lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty date: 2020-05-16 words: 7707.0 sentences: 409.0 pages: flesch: 52.0 cache: ./cache/cord-333919-nrd9ajj2.txt txt: ./txt/cord-333919-nrd9ajj2.txt summary: In this work, starting from a compartmental model with a social structure, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. The heterogeneity of the procedures used to carry out the disease positivity tests, the delays in recording and reporting the results, and the large percentage of asymptomatic patients (in varying percentages depending on the studies and the countries but estimated by WHO at an average of around 80% of cases) make the construction of predictive scenarios affected by high uncertainty [28, 33, 44] . We present different simulation scenarios for various countries where the epidemic wave is underway, including Germany, France, Italy, Spain, the United Kingdom and the United States showing the effect of relaxing the lockdown measures in a selective way on the various social activities. abstract: After an initial phase characterized by the introduction of timely and drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments are preparing to relax such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a compartmental model with a social structure, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. Specific contact patterns in the home, work, school and other locations for all countries considered have been used. Results from different scenarios in some of the major countries where the epidemic is ongoing, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed. url: http://medrxiv.org/cgi/content/short/2020.05.12.20099721v1?rss=1 doi: 10.1101/2020.05.12.20099721 id: cord-350603-ssen3q08 author: Albrecht, Randy A. title: Moving Forward: Recent Developments for the Ferret Biomedical Research Model date: 2018-07-17 words: 1607.0 sentences: 80.0 pages: flesch: 37.0 cache: ./cache/cord-350603-ssen3q08.txt txt: ./txt/cord-350603-ssen3q08.txt summary: While widely recognized for its utility in influenza virus research, ferrets are used for a variety of infectious and noninfectious disease models due to the anatomical, metabolic, and physiological features they share with humans and their susceptibility to many human pathogens. To resolve this, a group of researchers from around the world are working together to develop validated reagents and assays to improve our understanding of the innate and adaptive immune responses in the ferret. Flow Cytometric and cytokine ELISPOT approaches to characterize the cell-mediated immune response in ferrets following influenza virus Infection Screening monoclonal antibodies for cross-reactivity in the ferret model of influenza infection Infection of ferrets with influenza virus elicits a light chain-biased antibody response against hemagglutinin Ferrets as a novel animal model for studying human respiratory syncytial virus infections in immunocompetent and immunocompromised hosts A neutralizing human monoclonal antibody protects against lethal disease in a new ferret model of acute Nipah virus infection abstract: Since the initial report in 1911, the domestic ferret has become an invaluable biomedical research model. While widely recognized for its utility in influenza virus research, ferrets are used for a variety of infectious and noninfectious disease models due to the anatomical, metabolic, and physiological features they share with humans and their susceptibility to many human pathogens. However, there are limitations to the model that must be overcome for maximal utility for the scientific community. Here, we describe important recent advances that will accelerate biomedical research with this animal model. url: https://doi.org/10.1128/mbio.01113-18 doi: 10.1128/mbio.01113-18 id: cord-260797-tc3pueow author: Aleta, Alberto title: Data-driven contact structures: From homogeneous mixing to multilayer networks date: 2020-07-16 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population. url: https://doi.org/10.1371/journal.pcbi.1008035 doi: 10.1371/journal.pcbi.1008035 id: cord-267180-56wqok4c author: Aliee, M. title: Predicting the impact of COVID-19 interruptions on transmission of gambiense human African trypanosomiasis in two health zones of the Democratic Republic of Congo date: 2020-10-27 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Many control programmes against neglected tropical diseases have been interrupted due to COVID-19 pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting, and the achievement of 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with slowed reduction - but no increase - if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation such as increased screening coverage is put in place. However, we emphasise that the biggest challenge will remain in the higher prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered. url: http://medrxiv.org/cgi/content/short/2020.10.26.20219485v1?rss=1 doi: 10.1101/2020.10.26.20219485 id: cord-024061-gxv8y146 author: Alkhamis, Moh A. title: Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks date: 2020-04-21 words: 12855.0 sentences: 540.0 pages: flesch: 36.0 cache: ./cache/cord-024061-gxv8y146.txt txt: ./txt/cord-024061-gxv8y146.txt summary: Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Our phylodynamic analyses included comparisons between commonly inferred evolutionary posterior parameters (e.g., substitution rate/site/year, divergence times, phylogeographic root state posterior probabilities, significant dispersal route between states) under different combinations of node-age and branch rate prior models. Epidemiology of Swine Influenza in the U.S. Based on the results of the best fitting phylodynamic models for both HA and PB2 segments, evolutionary rates of currently circulating human-like H3 viruses in the United States remain high with no apparent signs of substantial declines (Figures 2B,D) and were similar to what was inferred elsewhere (117). abstract: Emerging and endemic animal viral diseases continue to impose substantial impacts on animal and human health. Most current and past molecular surveillance studies of animal diseases investigated spatio-temporal and evolutionary dynamics of the viruses in a disjointed analytical framework, ignoring many uncertainties and made joint conclusions from both analytical approaches. Phylodynamic methods offer a uniquely integrated platform capable of inferring complex epidemiological and evolutionary processes from the phylogeny of viruses in populations using a single Bayesian statistical framework. In this study, we reviewed and outlined basic concepts and aspects of phylodynamic methods and attempted to summarize essential components of the methodology in one analytical pipeline to facilitate the proper use of the methods by animal health researchers. Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Subsequently, we compared similarities and differences between the posterior parameters inferred from sequence data using multiple phylodynamic models. Our suggested phylodynamic approach attempts to reduce the impact of its inherent limitations to offer less biased and biologically plausible inferences about the pathogen evolutionary characteristics to properly guide intervention activities. We also pinpointed requirements and challenges for integrating phylodynamic methods in routine animal disease surveillance activities. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186338/ doi: 10.3389/fvets.2020.00176 id: cord-268779-qbn3i2nq author: Alrasheed, Hend title: COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis date: 2020-10-23 words: 10876.0 sentences: 628.0 pages: flesch: 53.0 cache: ./cache/cord-268779-qbn3i2nq.txt txt: ./txt/cord-268779-qbn3i2nq.txt summary: In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. We aimed to match the model simulations with empirical data and then used the model to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to predict the percentage of individuals that must be vaccinated to stop the outbreak (when a vaccine becomes available). Volz [35] modeled SIR dynamics on a static random network, which represents the population structure of susceptible and infected individuals and their contact patterns with an arbitrary degree distribution. abstract: The novel coronavirus Severe Acute Respiratory Syndrome (SARS)-Coronavirus-2 (CoV-2) has resulted in an ongoing pandemic and has affected over 200 countries around the world. Mathematical epidemic models can be used to predict the course of an epidemic and develop methods for controlling it. As social contact is a key factor in disease spreading, modeling epidemics on contact networks has been increasingly used. In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. We generated a contact network that captures realistic social behaviors and dynamics of individuals in Saudi Arabia. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. Our results suggest that Saudi Arabia would have faced a nationwide peak of the outbreak on 21 April 2020 with a total of approximately 26 million infections had it not imposed strict control measures. The results also indicate that social distancing plays a crucial role in determining the future local dynamics of the epidemic. Our results also show that the closure of schools and mosques had the maximum impact on delaying the epidemic peak and slowing down the infection rate. If a vaccine does not become available and no social distancing is practiced from 10 June 2020, our predictions suggest that the epidemic will end in Saudi Arabia at the beginning of November with over 13 million infected individuals, and it may take only 15 days to end the epidemic after 70% of the population receive a vaccine. url: https://www.ncbi.nlm.nih.gov/pubmed/33113936/ doi: 10.3390/ijerph17217744 id: cord-229937-fy90oebs author: Amaro, J. E. title: Global analysis of the COVID-19 pandemic using simple epidemiological models date: 2020-05-14 words: 4902.0 sentences: 278.0 pages: flesch: 59.0 cache: ./cache/cord-229937-fy90oebs.txt txt: ./txt/cord-229937-fy90oebs.txt summary: The Death or ''D'' model is a simplified version of the SIR (susceptible-infected-recovered) model, which assumes no recovery over time, and allows for the transmission-dynamics equations to be solved analytically. The evolution of the COVID-19 pandemic in several countries (China, Spain, Italy, France, UK, Iran, USA and Germany) shows a similar behavior in concord with the D-model trend, characterized by a rapid increase of death cases followed by a slow decline, which are affected by the earliness and efficiency of the lockdown effect. These results are in agreement with more accurate calculations using the extended SIR model with a parametrized solution and more sophisticated Monte Carlo grid simulations, which predict similar trends and indicate a common evolution of the pandemic with universal parameters. Additionally, D-model calculations are benchmarked with more sophisticated and reliable calculations using the extended SIR (ESIR) and Monte Carlo Planck (MCP) models -also developed in this work -which provide similar results, but allow for a more coherent spatial-time disentanglement of the various effects present during a pandemic. abstract: Several analytical models have been used in this work to describe the evolution of death cases arising from coronavirus (COVID-19). The Death or `D' model is a simplified version of the SIR (susceptible-infected-recovered) model, which assumes no recovery over time, and allows for the transmission-dynamics equations to be solved analytically. The D-model can be extended to describe various focuses of infection, which may account for the original pandemic (D1), the lockdown (D2) and other effects (Dn). The evolution of the COVID-19 pandemic in several countries (China, Spain, Italy, France, UK, Iran, USA and Germany) shows a similar behavior in concord with the D-model trend, characterized by a rapid increase of death cases followed by a slow decline, which are affected by the earliness and efficiency of the lockdown effect. These results are in agreement with more accurate calculations using the extended SIR model with a parametrized solution and more sophisticated Monte Carlo grid simulations, which predict similar trends and indicate a common evolution of the pandemic with universal parameters. url: https://arxiv.org/pdf/2005.06742v1.pdf doi: nan id: cord-335418-s8ugu8e1 author: Annan, James D title: Model calibration, nowcasting, and operational prediction of the COVID-19 pandemic date: 2020-04-17 words: 4075.0 sentences: 205.0 pages: flesch: 57.0 cache: ./cache/cord-335418-s8ugu8e1.txt txt: ./txt/cord-335418-s8ugu8e1.txt summary: We present a simple operational nowcasting/forecasting scheme based on a joint state/parameter estimate of the COVID-19 epidemic at national or regional scale, performed by assimilating the time series of reported daily death numbers into a simple SEIR model. This system generates estimates of the current reproductive rate, Rt, together with predictions of future daily deaths and clearly outperforms a number of alternative forecasting systems that have been presented recently. In this work, we focus on the the current reproductive rate of the epidemic, R t , as the main parameter of interest, and also on the reported number of daily deaths, both as being the most reliable source of data (i.e., our observations O in the application of Bayes'' Theorem above) and also the primary forecast variable of interest to the public and policy makers. We have presented a simple data assimilation method that simultaneously calibrates and initialises a SEIR model for nowcasting and forecasting the COVID-19 epidemic at national and regional scale. abstract: We present a simple operational nowcasting/forecasting scheme based on a joint state/parameter estimate of the COVID-19 epidemic at national or regional scale, performed by assimilating the time series of reported daily death numbers into a simple SEIR model. This system generates estimates of the current reproductive rate, Rt, together with predictions of future daily deaths and clearly outperforms a number of alternative forecasting systems that have been presented recently. Our current (14th April 2020) estimates for Rt are, respectively, UK 0.49 (0.0 – 1.02), Spain 0.55 (0.33 – 0.77), Italy 0.90 (0.74 – 1.06) and France 0.67 (0.40 – 0.94) (mean and 95% credible intervals). Thus, we believe that the epidemics have been successfully suppressed in each of these countries, with high probability. Our approach is trivial to set up for any region and generates results in a few minutes on a laptop. We believe it would be straightforward to set up equivalent frameworks using more complex and realistic models, and hope that some experts in the field of epidemiological modelling will consider investigating this approach further. url: https://doi.org/10.1101/2020.04.14.20065227 doi: 10.1101/2020.04.14.20065227 id: cord-342855-dvgqouk2 author: Anzum, R. title: Mathematical Modeling of Coronavirus Reproduction Rate with Policy and Behavioral Effects date: 2020-06-18 words: 3086.0 sentences: 188.0 pages: flesch: 56.0 cache: ./cache/cord-342855-dvgqouk2.txt txt: ./txt/cord-342855-dvgqouk2.txt summary: In this paper a modified mathematical model based on the SIR model used which can predict the spreading of the corona virus disease (COVID-19) and its effects on people in the days ahead. Since reducing the face to face contact among people and staying home in lockdown can improve in reducing the further infection rate, ܴ is taken as a time varying constant rather than a fixed one to observe the overall scenario of coronavirus spreading. According to the conventional SIR model, for the N number of constant population, each of whom may be in one of five states with respect to time implies that: While a susceptible person can be affected by the disease when comes in contact with an infectious person. The SIR model is used to predict the vulnerability of any type pandemic which may not be applicable to coronavirus cases since this model assumes the reproduction rate as a constant. abstract: In this paper a modified mathematical model based on the SIR model used which can predict the spreading of the corona virus disease (COVID-19) and its effects on people in the days ahead. This model takes into account all the death, infected and recovered characteristics of this disease. To determine the extent of the risk posed by this novel coronavirus; the transmission rate (R0) is utilized for a time period from the beginning of spreading virus. In particular, it includes a novel policy to capture the R0 response in the virus spreading over time. The model estimates the vulnerability of the pandemic with a prediction of new cases by estimating a time-varying R0 to capture changes in the behavior of SIR model implies to new policy taken at different times and different locations of the world. This modified SIR model with the different values of R0 can be applied to different country scenario using the real time data report provided by the authorities during this pandemic. The effective evaluation of R0 can forecast the necessity of lockdown as well as reopening the economy. url: https://doi.org/10.1101/2020.06.16.20133330 doi: 10.1101/2020.06.16.20133330 id: cord-324230-nu0pn2q8 author: Ardabili, S. F. title: COVID-19 Outbreak Prediction with Machine Learning date: 2020-04-22 words: 7335.0 sentences: 451.0 pages: flesch: 53.0 cache: ./cache/cord-324230-nu0pn2q8.txt txt: ./txt/cord-324230-nu0pn2q8.txt summary: This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). In the present study, the frequently used algorithms, (i.e., genetic algorithm (GA), particle swarm optimizer (PSO) and grey wolf optimizer (GWO)) are employed to estimate the parameters by solving a cost function. In the present research, two frequently used ML methods, the multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS) are employed for the prediction of the outbreak in the five countries. According to Tables 5 to 12 , GWO provided the highest accuracy (smallest RMSE and largest correlation coefficient) and smallest processing time compared to PSO and GA for fitting the logistic, linear, logarithmic, quadratic, cubic, power, compound, and exponential-based equations for all five countries. abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. url: http://medrxiv.org/cgi/content/short/2020.04.17.20070094v1?rss=1 doi: 10.1101/2020.04.17.20070094 id: cord-284617-uwby8r3y author: Area, Iván title: Determination in Galicia of the required beds at Intensive Care Units date: 2020-10-06 words: 2595.0 sentences: 168.0 pages: flesch: 61.0 cache: ./cache/cord-284617-uwby8r3y.txt txt: ./txt/cord-284617-uwby8r3y.txt summary: By using a recent mathematical compartmental model that includes the super-spreader class and developed by Ndaïrou, Area, Nieto, and Torres, a procedure to estimate in advance the number of required beds at intensive care units is presented. We have employed a compartmental mathematical model for COVID19 to estimate in advance the number of required beds at intensive care units. Following previous works [8] [9] [10] , in [11] a model including the super-spreader class [14, 15] has been presented, and applied to give an estimation of the infected and death individuals in Wuhan. The usefulness of our model is then illustrated in Section 3 of numerical simulations, where by using the real data from Galicia we estimate the number of required beds at ICUs and compare the predictions with the real data. abstract: By using a recent mathematical compartmental model that includes the super-spreader class and developed by Ndaïrou, Area, Nieto, and Torres, a procedure to estimate in advance the number of required beds at intensive care units is presented. Numerical simulations are performed to show the accuracy of the predictions as compared with the real data in Galicia. url: https://api.elsevier.com/content/article/pii/S1110016820304890 doi: 10.1016/j.aej.2020.09.034 id: cord-267890-j64x6f5r author: Armstrong, E. title: Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation date: 2020-05-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We demonstrate the ability of statistical data assimilation to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain accurate estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. These state estimates require a measurement of the recovered population, and are tolerant to low errors in that measurement. Further, excellent estimates of all quantities are obtained using a temporal baseline of 112 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. The estimation of this transmission rate is sensitive to contamination in the data, highlighting the need for accurate and uniform methods of reporting. Finally, we employ the procedure using real data from Italy reported by Johns Hopkins. The aim of this paper is not to assign extreme significance to the results of these specific experiments textit{per se}. Rather, we intend to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown model parameters to a desired precision - all set within the complex context of the COVID-19 pandemic. url: https://doi.org/10.1101/2020.05.27.20112987 doi: 10.1101/2020.05.27.20112987 id: cord-326314-9ycht8gi author: Armstrong, Eve title: Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation date: 2020-11-02 words: 5087.0 sentences: 274.0 pages: flesch: 48.0 cache: ./cache/cord-326314-9ycht8gi.txt txt: ./txt/cord-326314-9ycht8gi.txt summary: We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Second, given noiseless measurements, a temporal baseline of 101 days is sufficient for the SDA procedure to capture the general trends in the evolution of the model populations, the detection probabilities, and the time-varying transmission rate following the implementation of social distancing. Other avenues for expansion are as follows: 1) define additional model parameters as unknowns to be estimated, including the fraction of patients hospitalized, the fraction who enter critical care, and the various timescales governing the reaction equations; 2) impose various constraints regarding the unknown time-varying quantities, particularly transmission rate K i (t), and identifying which forms permit a solution consistent with measurements; 3) examine model sensitivity to the initial numbers within each population; 4) examine model sensitivity to the temporal frequency of data sampling. abstract: We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. Given a noiseless measurement of the recovered population, excellent estimates of all quantities are obtained using a temporal baseline of 101 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. The aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision, in a model with the complexity required to capture important features of the COVID-19 pandemic. url: https://www.sciencedirect.com/science/article/pii/S2468042720300646?v=s5 doi: 10.1016/j.idm.2020.10.010 id: cord-104133-d01joq23 author: Arthur, Ronan F. title: Adaptive social contact rates induce complex dynamics during epidemics date: 2020-07-14 words: 5240.0 sentences: 300.0 pages: flesch: 55.0 cache: ./cache/cord-104133-d01joq23.txt txt: ./txt/cord-104133-d01joq23.txt summary: We develop a model for adaptive optimal control of the effective social contact rate within a Susceptible-Infectious-Susceptible (SIS) epidemic model using a dynamic utility function with delayed information. To represent endogenous behavior-change, we start with the classical discrete-time 112 susceptible-infected-susceptible (SIS) model [28] , which, when incidence is relatively 113 small compared to the total population [29, 30] , can be written in terms of the recursions 114 In order to introduce human behavior, we 121 substitute for b a time-dependent b t , which is a function of both b 0 , the probability that 122 disease transmission takes place on contact, and a dynamic social rate of contact c t 123 whose optimal value, c * t , is determined at each time t as in economic epidemiological 124 models [31] , namely abstract: The COVID-19 pandemic has posed a significant dilemma for governments across the globe. The public health consequences of inaction are catastrophic; but the economic consequences of drastic action are likewise catastrophic. Governments must therefore strike a balance in the face of these trade-offs. But with critical uncertainty about how to find such a balance, they are forced to experiment with their interventions and await the results of their experimentation. Models have proved inaccurate because behavioral response patterns are either not factored in or are hard to predict. One crucial behavioral response in a pandemic is adaptive social contact: potentially infectious contact between people is deliberately reduced either individually or by fiat; and this must be balanced against the economic cost of having fewer people in contact and therefore active in the labor force. We develop a model for adaptive optimal control of the effective social contact rate within a Susceptible-Infectious-Susceptible (SIS) epidemic model using a dynamic utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic model reveals the existence of a non-zero equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior-change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for COVID-19 include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections. Author summary Epidemic response in the form of social contact reduction, such as has been utilized during the ongoing COVID-19 pandemic, presents inherent tradeoffs between the economic costs of reducing social contacts and the public health costs of neglecting to do so. Such tradeoffs introduce an interactive, iterative mechanism which adds complexity to an infectious disease system. Consequently, infectious disease modeling typically has not included dynamic behavior change that must address such a tradeoff. Here, we develop a theoretical model that introduces lost or gained economic and public health utility through the adjustment of social contact rates with delayed information. We find this model produces an equilibrium, a point of indifference where the tradeoff is neutral, and at which a disease will be endemic for a long period of time. Under small perturbations, this model exhibits complex dynamic regimes, including oscillatory behavior, runaway exponential growth, and eradication. These dynamics suggest that for epidemic response that relies on social contact reduction, secondary waves and surges with accompanied business re-closures and shutdowns may be expected, and that accurate projection under such circumstances is unlikely. url: https://doi.org/10.1101/2020.04.14.028407 doi: 10.1101/2020.04.14.028407 id: cord-310406-5pvln91x author: Asbury, Thomas M title: Genome3D: A viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome date: 2010-09-02 words: 3014.0 sentences: 189.0 pages: flesch: 44.0 cache: ./cache/cord-310406-5pvln91x.txt txt: ./txt/cord-310406-5pvln91x.txt summary: RESULTS: We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In addition, in spite of the many recent efforts to measure and model the genome structure at various resolutions and detail [3] [4] [5] [6] [7] [8] [9] [10] , little work has focused on combining these models into a plausible aggregate, or has taken advantage of the large amount of genomic and epigenomic data available from new high-throughput approaches. The viewer is designed to display data from multiple scales and uses a hierarchical model of the relative positions of all nucleotide atoms in the cell nucleus, i.e., the complete physical genome. An integrated physical genome model can show the interplay between histone modifications and other genomic data, such as SNPs, DNA methylation, the structure of gene, promoter and transcription machinery, etc. In addition to epigenomic data, the physical genome model also provides a platform to visualize highthroughput gene expression data and its interplay with global binding information of transcription factors. abstract: BACKGROUND: New technologies are enabling the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is increasingly structural in nature, and is often difficult to coordinate with other data sets. There is a legitimate need for integrating and visualizing these disparate data sets to reveal structural relationships not apparent when looking at these data in isolation. RESULTS: We have applied object-oriented technology to develop a downloadable visualization tool, Genome3D, for integrating and displaying epigenomic data within a prescribed three-dimensional physical model of the human genome. In order to integrate and visualize large volume of data, novel statistical and mathematical approaches have been developed to reduce the size of the data. To our knowledge, this is the first such tool developed that can visualize human genome in three-dimension. We describe here the major features of Genome3D and discuss our multi-scale data framework using a representative basic physical model. We then demonstrate many of the issues and benefits of multi-resolution data integration. CONCLUSIONS: Genome3D is a software visualization tool that explores a wide range of structural genomic and epigenetic data. Data from various sources of differing scales can be integrated within a hierarchical framework that is easily adapted to new developments concerning the structure of the physical genome. In addition, our tool has a simple annotation mechanism to incorporate non-structural information. Genome3D is unique is its ability to manipulate large amounts of multi-resolution data from diverse sources to uncover complex and new structural relationships within the genome. url: https://www.ncbi.nlm.nih.gov/pubmed/20813045/ doi: 10.1186/1471-2105-11-444 id: cord-355102-jcyq8qve author: Avila, Eduardo title: Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios date: 2020-06-29 words: 4768.0 sentences: 242.0 pages: flesch: 39.0 cache: ./cache/cord-355102-jcyq8qve.txt txt: ./txt/cord-355102-jcyq8qve.txt summary: PURPOSE: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. METHODS: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. In order to evaluate the adequacy and generalization power of the proposed model, as well as its tolerance to handle samples containing missing data (i.e., at least one variable with no informed values), an additional set of 92 samples (10 positives for COVID-19 and 82 negatives) was obtained from the patient database. When no clinical or medical data is available, or when decisions regarding resource management involving multiple symptomatic patients are necessary, the model can be used in multiple individuals simultaneously, aiming to identify those with higher probabilities of presenting positive qRT-PCR results. abstract: BACKGROUND: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. PURPOSE: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. METHODS: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. RESULTS: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. CONCLUSIONS: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency. url: https://doi.org/10.7717/peerj.9482 doi: 10.7717/peerj.9482 id: cord-026827-6vjg386e author: Awan, Ammar Ahmad title: HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow date: 2020-05-22 words: 6142.0 sentences: 347.0 pages: flesch: 56.0 cache: ./cache/cord-026827-6vjg386e.txt txt: ./txt/cord-026827-6vjg386e.txt summary: To address these problems, we create HyPar-Flow—a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF''s Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6[Formula: see text] speedup over Horovod-based data-parallel training, 2) 110[Formula: see text] speedup over single-node on 128 Stampede2 nodes, and 3) 481[Formula: see text] speedup over single-node on 512 Frontera nodes. To achieve performance, we need to investigate if applying widely-used and important HPC techniques like 1) efficient placement of processes on CPU cores, 2) pipelining via batch splitting, and 3) overlap of computation and communication can be exploited for improving performance of model-parallel and hybrid-parallel training. abstract: To reduce the training time of large-scale Deep Neural Networks (DNNs), Deep Learning (DL) scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model’s accuracy. To address these problems, we create HyPar-Flow—a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF’s Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6[Formula: see text] speedup over Horovod-based data-parallel training, 2) 110[Formula: see text] speedup over single-node on 128 Stampede2 nodes, and 3) 481[Formula: see text] speedup over single-node on 512 Frontera nodes. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295349/ doi: 10.1007/978-3-030-50743-5_5 id: cord-163946-a4vtc7rp author: Awasthi, Raghav title: VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning date: 2020-09-14 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and a temporal separation of hot-spots might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Actor-Critic using Kronecker-Factored Trust Region (ACKTR) model into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine. Whereas the ACKTR model suggests better actions and rewards, Contextual Bandits allow online modifications that may need to be implemented on a day-to-day basis in the real world scenario. We evaluate this framework against a naive allocation approach of distributing vaccine proportional to the incidence of COVID-19 cases in five different States across India and demonstrate up to 100,000 additional lives potentially saved and a five-fold increase in the efficacy of limiting the spread over a period of 30 days through the VacSIM approach. We also propose novel evaluation strategies including a standard compartmental model based projections and a causality preserving evaluation of our model. Finally, we contribute a new Open-AI environment meant for the vaccine distribution scenario, and open-source VacSIM for wide testing and applications across the globe. url: https://arxiv.org/pdf/2009.06602v1.pdf doi: nan id: cord-308115-bjyr6ehq author: Baba, Isa Abdullah title: Fractional Order Model for the Role of Mild Cases in the Transmission of COVID-19 date: 2020-10-20 words: 2394.0 sentences: 134.0 pages: flesch: 42.0 cache: ./cache/cord-308115-bjyr6ehq.txt txt: ./txt/cord-308115-bjyr6ehq.txt summary: To execute these measures effectively, there is need to have an in depth study about the number of persons that each infected individual can infect, meanwhile a mathematical model describing the transmission dynamics of the disease should be established. [6] developed a mathematical model (for MERS) inform of nonlinear system of differential equations, in which he considered a camel to be the source of infection that spread the virus to infective human population, then human to human transmission, then to clinic center then to care center. However, they constructed the Lyapunov candidate function to investigate the local and global stability analysis of the equilibriums solution and subsequently obtained the basic reproduction number or roughly, a key parameter describing transmission of the infection. A mathematical model for COVID-19 transmission by using the Caputo fractional derivative A fractional differential equation model for the COVID-19 transmission by using the Caputo-Fabrizio derivative abstract: Most of the nations with deplorable health conditions lack rapid COVID-19diagnostic test due to limited testing kits and laboratories. The un-diagnosticmild cases (who show no critical sign and symptoms) play the role as a route that spread the infection unknowingly to healthy individuals. In this paper, we present a fractional order SIR model incorporating individual with mild cases as a compartment to become SMIR model. The existence of the solutions of the model is investigated by solving the fractional Gronwall's inequality using the Laplace transform approach. The equilibrium solutions (DFE & Endemic) are found to be locally asymptotically stable, and subsequently the basic reproduction number is obtained. Also the global stability analysis is carried out by constructing Lyapunov function. Lastly, numerical simulations that support analytic solution follow. It was also shown that when the rate of infection of the mild cases increases, there is equivalent increase in the overall population of infected individuals. Hence to curtail the spread of the disease there is need to take care of the Mild cases as well. url: https://www.sciencedirect.com/science/article/pii/S0960077920307682?v=s5 doi: 10.1016/j.chaos.2020.110374 id: cord-196353-p05a8zjy author: Backhausz, 'Agnes title: Virus spread and voter model on random graphs with multiple type nodes date: 2020-02-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: When modelling epidemics or spread of information on online social networks, it is crucial to include not just the density of the connections through which infections can be transmitted, but also the variability of susceptibility. Different people have different chance to be infected by a disease (due to age or general health conditions), or, in case of opinions, ones are easier to be convinced by others, or stronger at sharing their opinions. The goal of this work is to examine the effect of multiple types of nodes on various random graphs such as ErdH{o}s--R'enyi random graphs, preferential attachment random graphs and geometric random graphs. We used two models for the dynamics: SEIR model with vaccination and a version of voter model for exchanging opinions. In the first case, among others, various vaccination strategies are compared to each other, while in the second case we studied sevaral initial configurations to find the key positions where the most effective nodes should be placed to disseminate opinions. url: https://arxiv.org/pdf/2002.06926v1.pdf doi: nan id: cord-034834-zap82dta author: Bai, Xiao title: A Review of Micro-Based Systemic Risk Research from Multiple Perspectives date: 2020-06-27 words: 14932.0 sentences: 691.0 pages: flesch: 41.0 cache: ./cache/cord-034834-zap82dta.txt txt: ./txt/cord-034834-zap82dta.txt summary: Meanwhile, cross-disciplinary research methods from other disciplines have been introduced, such as the introduction of complex network models when studying the structural stability of the system, linking the contagious effects of financial systemic risks to the transmission pathways of infectious diseases or bio-food chains [1] [2] [3] [4] [5] [6] , establishing new measures to measure systemic risk [7] [8] [9] [10] . Therefore, although the academic community still has differences in the definition of systemic risks, by comparing the concepts of systemic risk and financial crisis, and summarizing the definition of systemic risk in the academic world, the concept of systemic risk can be defined from an economic perspective: triggered by macro or micro-events, the institutions in the system are subjected to negative impacts, and more organizations are involved in risk diffusion and the existence of internal correlations strengthens the feedback mechanism, causing the system as a whole to face the risk of collapse. abstract: The Covid-19 pandemic has brought about a heavy impact on the world economy, which arouses growing concerns about potential systemic risk, taking place in countries and regions. At this critical moment, it makes sense to interpret the systemic risk from the perspective of the financial crisis framework. By combing the latest research on systemic risks, we may arrive at some precautions relating to the current events. This literature review verifies the origin of systemic risk research. By comparing the retrieved and screened systemic literature with the relevant research on the financial crisis, more focus on the micro-foundations of systemic risk has been discovered. Besides, the measurement methods of systemic risks and the introduction of interdisciplinary methods have made the research in this field particularly active. This paper synthesizes the previous research conclusions to find the appropriate definition of systemic risk and combs the research literature of systemic risk from two lines: Firstly, conducting the division according to the sub-branch fields within the financial discipline and the relevant interdisciplinary research methods, which is helpful for scholars within and outside the discipline to have a more systematic understanding of the research in this field. Secondly predicting the research direction that can be expanded in this field. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517248/ doi: 10.3390/e22070711 id: cord-302277-c66xm2n4 author: Bakaletz, Lauren O. title: Developing animal models for polymicrobial diseases date: 2004 words: 10910.0 sentences: 537.0 pages: flesch: 33.0 cache: ./cache/cord-302277-c66xm2n4.txt txt: ./txt/cord-302277-c66xm2n4.txt summary: Briefly, viral infection compromises the protective functions of the Eustachian tube, alters respiratory-tract secretions, damages the mucosal epithelial lining, interferes with antibiotic efficacy, modulates the immune response and enhances bacterial adherence 77 and colonization 78 to predispose the host to bacterial OM. In otitis media, which is a middle ear infection, a synergistic interaction that results in disease owing to co-infection with an upper respiratory tract virus and three bacterial species -Streptococcus pneumoniae, nontypeable Haemophilus influenzae (NTHI) and Moraxella catarrhalis -is well documented. It seems likely that the transient suppression of RDC migration and the delayed development of an effective adaptive immune response to a second infection might be another mechanism by which influenza virus predisposes the host to bacterial co-infection. Using this criterion, a mouse model of polymicrobial-induced osteoclastogenesis, bacterial penetration, leukocyte recruitment and softtissue necrosis has been developed to clarify the role of cytokines in periodontal disease. abstract: Polymicrobial diseases involve two or more microorganisms that act synergistically, or in succession, to mediate complex disease processes. Although polymicrobial diseases in animals and humans can be caused by similar organisms, these diseases are often also caused by organisms from different kingdoms, genera, species, strains, substrains and even by phenotypic variants of a single species. Animal models are often required to understand the mechanisms of pathogenesis, and to develop therapies and prevention regimes. However, reproducing polymicrobial diseases of humans in animal hosts presents significant challenges. url: https://www.ncbi.nlm.nih.gov/pubmed/15197391/ doi: 10.1038/nrmicro928 id: cord-185125-be11h9wn author: Baldea, Ioan title: What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia? date: 2020-05-25 words: 2402.0 sentences: 146.0 pages: flesch: 51.0 cache: ./cache/cord-185125-be11h9wn.txt txt: ./txt/cord-185125-be11h9wn.txt summary: In the unprecedented difficulty created by the COVID-19 pandemic outbreak, 1 mathematical modeling developed by epidemiologists over many decades 2-7 may make an important contribution in helping politics to adopt adequate regulations to efficiently fight against the spread of SARS-CoV-2 virus while mitigating negative economical and social consequences. As an aggravating circumstance, one should also add the difficulty not encountered in the vast majority of previous studies: how do the input parameters needed in model simulations change in time under so many restrictive measures (wearing face masks, social distancing, movement restrictions, isolation and quarantine policies, etc) unknown in the pre-COVID-19 era? Rather, we use raw epidemiological data to validate the logistic growth and straightforwardly extract the time dependent infection rate, which is the relevant model parameter for the specific case considered and makes it possible to compare how efficient different restrictive measures act to mitigate the COVID-19 pandemic, and even to get insight significant for behavioral and social science. abstract: A recent work (DOI 10.1101/2020.05.06.20093310) indicated that temporarily splitting larger populations into smaller groups can efficiently mitigate the spread of SARS-CoV-2 virus. The fact that, soon afterwards, on May 15, 2020, the two million people Slovenia was the first European country proclaiming the end of COVID-19 epidemic within national borders may be relevant from this perspective. Motivated by this evolution, in this paper we investigate the time dynamics of coronavirus cases in Slovenia with emphasis on how efficient various containment measures act to diminish the number of COVID-19 infections. Noteworthily, the present analysis does not rely on any speculative theoretical assumption; it is solely based on raw epidemiological data. Out of the results presented here, the most important one is perhaps the finding that, while imposing drastic curfews and travel restrictions reduce the infection rate kappa by a factor of four with respect to the unrestricted state, they only improve the k{appa}-value by ~15 % as compared to the much bearable state of social and economical life wherein (justifiable) wearing face masks and social distancing rules are enforced/followed. Significantly for behavioral and social science, our analysis of the time dependence k{appa} = k{appa}(t) may reveal an interesting self-protection instinct of the population, which became manifest even before the official lockdown enforcement. url: https://arxiv.org/pdf/2005.12367v1.pdf doi: nan id: cord-262524-ununcin0 author: Bankhead, Armand title: A Simulation Framework to Investigate in vitro Viral Infection Dynamics date: 2011-12-31 words: 4245.0 sentences: 206.0 pages: flesch: 49.0 cache: ./cache/cord-262524-ununcin0.txt txt: ./txt/cord-262524-ununcin0.txt summary: In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles. We also show that the model can explain the experimentally observed virus titer data and allows a deeper understanding of the infection dynamics in the in vitro experiments. Infectious: Assembled virion is being released from the host cell according to the release function (Section 2.4) By examining the experimental viral titer data shown in Figure 1 we derived temporal delay of the state transition between Containing and Infectious. p BP represents the probability of a virus-receptor binding event leading to a cell''s infection by a single viral particle during a given model time step. abstract: Abstract Virus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24hours post infection. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles. url: https://www.ncbi.nlm.nih.gov/pubmed/32288900/ doi: 10.1016/j.procs.2011.04.195 id: cord-176131-0vrb3law author: Bao, Richard title: PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic date: 2020-06-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The Covid-19 pandemic has made clear the need to improve modern multivariate time-series forecasting models. Current state of the art predictions of future daily deaths and, especially, hospital resource usage have confidence intervals that are unacceptably wide. Policy makers and hospitals require accurate forecasts to make informed decisions on passing legislation and allocating resources. We used US county-level data on daily deaths and population statistics to forecast future deaths. We extended the SIR epidemiological model to a novel model we call the PECAIQR model. It adds several new variables and parameters to the naive SIR model by taking into account the ramifications of the partial quarantining implemented in the US. We fitted data to the model parameters with numerical integration. Because of the fit degeneracy in parameter space and non-constant nature of the parameters, we developed several methods to optimize our fit, such as training on the data tail and training on specific policy regimes. We use cross-validation to tune our hyper parameters at the county level and generate a CDF for future daily deaths. For predictions made from training data up to May 25th, we consistently obtained an averaged pinball loss score of 0.096 on a 14 day forecast. We finally present examples of possible avenues for utility from our model. We generate longer-time horizon predictions over various 1-month windows in the past, forecast how many medical resources such as ventilators and ICU beds will be needed in counties, and evaluate the efficacy of our model in other countries. url: https://arxiv.org/pdf/2006.13693v1.pdf doi: nan id: cord-301117-egd1gxby author: Barh, Debmalya title: In Silico Models: From Simple Networks to Complex Diseases date: 2013-11-15 words: 13765.0 sentences: 670.0 pages: flesch: 37.0 cache: ./cache/cord-301117-egd1gxby.txt txt: ./txt/cord-301117-egd1gxby.txt summary: Bioinformatics deals with methods for storing, retrieving, and analyzing biological data and protein sequences, structures, functions, pathways, and networks, and recently, in silico disease modeling and simulation using systems biology. Bioinformatics is the computational data management discipline that helps us gather, analyze, and represent this information in order to educate ourselves, understand biological processes in healthy and diseased states, and to facilitate discovery of better animal products. The development of such computational modeling techniques to include diverse types of molecular biological information clearly supports the gene regulatory network inference process and enables the modeling of the dynamics of gene regulatory systems. Understanding the complexity of the disease and its biological significance in health can be achieved by integrating data from the different functional genomics experiments with medical, physiological, and environmental factor information, and computing mathematically. abstract: In this chapter, we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given. url: https://api.elsevier.com/content/article/pii/B9780124160026000213 doi: 10.1016/b978-0-12-416002-6.00021-3 id: cord-332618-8al98ya2 author: Barraza, Néstor Ruben title: A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic date: 2020-09-18 words: 4603.0 sentences: 307.0 pages: flesch: 62.0 cache: ./cache/cord-332618-8al98ya2.txt txt: ./txt/cord-332618-8al98ya2.txt summary: We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Hence, 35 we propose a different model based on a Pure Birth process with an event rate that, like Polya''s, depends on both the elapsed time and the number of previous events, but with a different functional form. Our main motivation is to obtain a model that describes an epidemic outbreak at its first stage, before it reaches the inflection point in the case incidence curve, which is useful to monitor how contagion is spreading out. Since the mean value function of the Polya-Lundberg process is a linear function of time (see Appendix B), we introduce a modification in the event rate in order to get a mean value function that grows 85 subexponentially with either positive or negative concavity as we observe in the early epidemic growth curves usually reported. abstract: This work introduces a new markovian stochastic model that can be described as a non-homogeneous Pure Birth process. We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Thus, we model the early stages of an epidemic. The number of individuals then becomes the infectious cases and the birth rate becomes the incidence rate. We obtain this way a process that depends on two competitive phenomena, infection and immunization. Variations in those rates allow us to monitor how effective the actions taken by government and health organizations are. From our model, three useful indicators for the epidemic evolution over time are obtained: the immunization rate, the infection/immunization ratio and the mean time between infections (MTBI). The proposed model allows either positive or negative concavities for the mean value curve, provided the infection/immunization ratio is either greater or less than one. We apply this model to the present SARS-CoV-2 pandemic still in its early growth stage in Latin American countries. As it is shown, the model accomplishes a good fit for the real number of both positive cases and deaths. We analyze the evolution of the three indicators for several countries and perform a comparative study between them. Important conclusions are obtained from this analysis. url: https://www.sciencedirect.com/science/article/pii/S0960077920306937?v=s5 doi: 10.1016/j.chaos.2020.110297 id: cord-026503-yomnqr78 author: Basile, Davide title: Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego date: 2020-05-13 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Moving block railway systems are the next generation signalling systems currently under development as part of the Shift2Rail European initiative, including autonomous driving technologies. In this paper, we model a suitable abstraction of a moving block signalling system with autonomous driving as a stochastic priced timed game. We then synthesise safe and optimal driving strategies for the model by applying advanced techniques that combine statistical model checking with reinforcement learning as provided by Uppaal Stratego. Hence, we show the applicability of Uppaal Stratego in this concrete case study. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281857/ doi: 10.1007/978-3-030-50086-3_1 id: cord-318187-c59c9vi3 author: Basu, Saikat title: Numerical evaluation of spray position for improved nasal drug delivery date: 2020-06-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Topical intra-nasal sprays are amongst the most commonly prescribed therapeutic options for sinonasal diseases in humans. However, inconsistency and ambiguity in instructions show a lack of definitive knowledge on best spray use techniques. In this study, we have identified a new usage strategy for nasal sprays available over-the-counter, that registers an average 8-fold improvement in topical delivery of drugs at diseased sites, when compared to prevalent spray techniques. The protocol involves re-orienting the spray axis to harness inertial motion of particulates and has been developed using computational fluid dynamics simulations of respiratory airflow and droplet transport in medical imaging-based digital models. Simulated dose in representative models is validated through in vitro spray measurements in 3D-printed anatomic replicas using the gamma scintigraphy technique. This work breaks new ground in proposing an alternative user-friendly strategy that can significantly enhance topical delivery inside human nose. While these findings can eventually translate into personalized spray usage instructions and hence merit a change in nasal standard-of-care, this study also demonstrates how relatively simple engineering analysis tools can revolutionize everyday healthcare. Finally, with respiratory mucosa as the initial coronavirus infection site, our findings are relevant to intra-nasal vaccines that are in-development, to mitigate the COVID-19 pandemic. url: https://www.ncbi.nlm.nih.gov/pubmed/32601278/ doi: 10.1038/s41598-020-66716-0 id: cord-274513-0biyfhab author: Baumgartner, M. T. title: Assessing the relative contributions of healthcare protocols for epidemic control: an example with network transmission model for COVID-19 date: 2020-07-22 words: 5076.0 sentences: 249.0 pages: flesch: 46.0 cache: ./cache/cord-274513-0biyfhab.txt txt: ./txt/cord-274513-0biyfhab.txt summary: In this study, we used an individual-based age-structured network model to assess the effective roles of different healthcare protocols such as the use of personal protection equipment and social distancing at neighborand city-level scales. Our results revealed that the model was more sensitive to changes in the parameter representing the rate of contact among people from different neighborhoods, which defends the social distancing at the city-level as the most effective protocol for the control of the disease outbreak. By varying model parameters related to these protocols, we were able to discuss better scenarios considering the delay in the infection peak and lower numbers of cases, as well as activities with a low potential to boost the outbreak. Given the specified model structure, those results forecasting early wave peaks emerged under moderate to high probabilities of the individual-level exposure to SARS-CoV-2 virus (high β), in combination with higher encountering rates among people (v and k) ( Figure 1 ; Table S1 ). abstract: The increasing number of COVID-19 cases threatens human life and requires retainment actions that control the spread of the virus in the absence of effective medical therapy or a reliable vaccine. There is a general consensus that the most efficient health protocol in the actual state is to disrupt the infection chain through social distancing, although economic interests stand against closing non-essential activities and poses a debatable tradeoff. In this study, we used an individual-based age-structured network model to assess the effective roles of different healthcare protocols such as the use of personal protection equipment and social distancing at neighbor- and city-level scales. Using as much as empirical data available in the literature, we calibrated a city model and simulated low, medium, and high parameters representing these protocols. Our results revealed that the model was more sensitive to changes in the parameter representing the rate of contact among people from different neighborhoods, which defends the social distancing at the city-level as the most effective protocol for the control of the disease outbreak. Another important identified parameter represented the use of individual equipment such as masks, face shields, and hand sanitizers like alcohol-based solutions and antiseptic products. Interestingly, our simulations suggest that some periodical activities such as going to the supermarket, gas station, and pharmacy would have little contribution to the SARS-CoV-2 spread once performed within the same neighborhood. As we can see nowadays, there is an inevitable context-dependency and economic pressure on the level of social distancing recommendations, and we reinforce that every decision must be a welfare-oriented science-based decision. url: https://doi.org/10.1101/2020.07.20.20158576 doi: 10.1101/2020.07.20.20158576 id: cord-303187-ny4qr2a2 author: Belo, Vinícius Silva title: Abundance, survival, recruitment and effectiveness of sterilization of free-roaming dogs: A capture and recapture study in Brazil date: 2017-11-01 words: 7691.0 sentences: 410.0 pages: flesch: 44.0 cache: ./cache/cord-303187-ny4qr2a2.txt txt: ./txt/cord-303187-ny4qr2a2.txt summary: Despite the perceived need and usefulness of such parameter estimates and recommendations for the most appropriate approaches applicable under such study designs [30] , survival and recruitment estimates of free-ranging dogs had not been obtained using methods of capture and recapture. In this study, we present estimates of abundance, survival and recruitment rates, and the probabilities of capture of two free-roaming dog populations by means of analytical models for open populations, so far unexplored in previous studies. We estimated critical parameters (survival, recruitment and abundance) that describe the population dynamics of free-roaming dogs based on a capture and recapture study design and on models suitable for open populations. Our study demonstrated the increase in population size in both areas, the predominance and greater recruitment of males, the temporal variability in recruitment and in survival probabilities, the lack of effect of sterilization on population dynamics, the influence of abandon and of density-independent factors and a high demographic turnover. abstract: The existence of free-roaming dogs raises important issues in animal welfare and in public health. A proper understanding of these animals’ ecology is useful as a necessary input to plan strategies to control these populations. The present study addresses the population dynamics and the effectiveness of the sterilization of unrestricted dogs using capture and recapture procedures suitable for open animal populations. Every two months, over a period of 14 months, we captured, tagged, released and recaptured dogs in two regions in a city in the southeast region of Brazil. In one of these regions the animals were also sterilized. Both regions had similar social, environmental and demographic features. We estimated the presence of 148 females and 227 males during the period of study. The average dog:man ratio was 1 dog for each 42 and 51 human beings, in the areas without and with sterilization, respectively. The animal population size increased in both regions, due mainly to the abandonment of domestic dogs. Mortality rate decreased throughout the study period. Survival probabilities did not differ between genders, but males entered the population in higher numbers. There were no differences in abundance, survival and recruitment between the regions, indicating that sterilization did not affect the population dynamics. Our findings indicate that the observed animal dynamics were influenced by density-independent factors, and that sterilization might not be a viable and effective strategy in regions where availability of resources is low and animal abandonment rates are high. Furthermore, the high demographic turnover rates observed render the canine free-roaming population younger, thus more susceptible to diseases, especially to rabies and leishmaniasis. We conclude by stressing the importance of implementing educational programs to promote responsible animal ownership and effective strategies against abandonment practices. url: https://www.ncbi.nlm.nih.gov/pubmed/29091961/ doi: 10.1371/journal.pone.0187233 id: cord-296560-ehrww6uu author: Bender, Andreas title: Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR date: 2006-11-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This chapter discusses recent developments in some of the areas that exploit the molecular similarity principle, novel approaches to capture molecular properties by the use of novel descriptors, focuses on a crucial aspect of computational models—their validity, and discusses additional ways to examine data available, such as those from high-throughput screening (HTS) campaigns and to gain more knowledge from this data. The chapter also presents some of the recent applications of methods discussed focusing on the successes of virtual screening applications, database clustering and comparisons (such as drug- and in-house-likeness), and the recent large-scale validations of docking and scoring programs. While a great number of descriptors and modeling methods has been proposed until today, the recent trend toward proper model validation is very much appreciated. Although some of their limitations are surely because of underlying principles and limitations of fundamental concepts, others will certainly be eliminated in the future. url: https://www.ncbi.nlm.nih.gov/pubmed/32362803/ doi: 10.1016/s1574-1400(06)02009-3 id: cord-103280-kf6mqv4e author: Bergs, Thomas title: Determination of Johnson-Cook material model parameters for AISI 1045 from orthogonal cutting tests using the Downhill-Simplex algorithm date: 2020-12-31 words: 7779.0 sentences: 455.0 pages: flesch: 45.0 cache: ./cache/cord-103280-kf6mqv4e.txt txt: ./txt/cord-103280-kf6mqv4e.txt summary: title: Determination of Johnson-Cook material model parameters for AISI 1045 from orthogonal cutting tests using the Downhill-Simplex algorithm Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. These results motivated for the development of a methodology capable to determine material model parameters robust and inversely from the machining process, which can be used with lower computational effort. By using the Downhill-Simplex-Algorithm, it was possible to determine material model parameters within 17 iterations and achieving an average deviation between the experiment and the simulations below 10 %. Therefore, a sequential approach, starting with an initial set of machining simulation based on a design of computer experiments (DOCE) and analysis of the numerical results in terms of cutting forces and temperatures was used. abstract: Abstract Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today’s manufacturing technology. Compared to the conventional process design, which is influenced by an empirical "trial-and-error" principle, the simulative process design offers the possibility of reducing development time and costs while at the same time improving the process understanding. A possible simulation technique to achieve these goals is the Finite Element Method (FEM). The FEM enables the calculation of the thermo-mechanical load spectrum underlying the machining process. Therefore, different input models are required. One of the most critical input models is the material model, which describes the constitutive material behavior. To determine the material model parameters, either (conventional) material tests, which require an extrapolation into the regime of metal cutting, or inverse techniques are used, where the process itself is used as a material test. Using the inverse technique, the model parameters are modified iteratively until a predefined agreement between simulations and experiments is achieved. The evaluation of the agreement bases on integral process variables, such as the cutting force, and their simulative counterparts. However, the procedure of the inverse determination requires high computational efforts and is not robust. This paper presents a novel approach to enhance the robustness of the inverse material model parameter determination from the cutting process. Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. Thereby, the workpiece material was investigated in the two different heat treatments: normalized and coarse-grain annealed. The machining experiments showed differences in terms of the integral process results when comparing the two heat treatments. These results motivated for the development of a methodology capable to determine material model parameters robust and inversely from the machining process, which can be used with lower computational effort. To simulate the machining process, a Coupled-Eulerian-Lagrangian (CEL) model of the orthogonal cutting process has been set up. The material model parameters have been inversely determined using the Downhill-Simplex-Algorithm, which has been modified for this case. By using the Downhill-Simplex-Algorithm, it was possible to determine material model parameters within 17 iterations and achieving an average deviation between the experiment and the simulations below 10 %. Thereby, different process observables such as temperature, forces, and chip form have been used for the evaluation. Through this method, it is possible to determine material model parameters, which enable a good match between experiments and simulations with a low computational effort. url: https://api.elsevier.com/content/article/pii/S235197892031533X doi: 10.1016/j.promfg.2020.05.081 id: cord-340244-qjf23a7e author: Bernstein, Daniel J. title: Further analysis of the impact of distancing upon the COVID-19 pandemic date: 2020-04-16 words: 7064.0 sentences: 439.0 pages: flesch: 63.0 cache: ./cache/cord-340244-qjf23a7e.txt txt: ./txt/cord-340244-qjf23a7e.txt summary: The 22 March 2020 paper "Social distancing strategies for curbing the COVID-19 epidemic" [5] reports calculations in a model where distancing reduces R 0 by at most 60%, and claims that 60% is "on par with the reduction in R 0 achieved in China through intense social distancing measures (3)". The paper [5] claims, within its model, that the (37.5, 10.0) distancing strategy explained above achieves the "goal of keeping the number of critical care patients below 0.89 per 10,000 adults" under the following assumptions: wintertime R 0 = 2, and distancing achieves a 60% reduction in R 0 . The paper [5] claims that increasing critical-care capacity "allows population immunity to be accumulated more rapidly, reducing the overall duration of the epidemic and the total length of social distancing measures". The third (more optimistic) plot takes the wintertime R 0 to be 2.0 and uses an extended model where "intense" distancing has more of an effect, reducing R 0 by 99%. abstract: This paper questions various claims from the paper "Social distancing strategies for curbing the COVID-19 epidemic" by Kissler, Tedijanto, Lipsitch, and Grad: most importantly, the claim that China's "intense" distancing measures achieved only a 60% reduction in R0. url: https://doi.org/10.1101/2020.04.14.20048025 doi: 10.1101/2020.04.14.20048025 id: cord-027315-1i94ye79 author: Bielecki, Andrzej title: Simulation of Neurotransmitter Flow in Three Dimensional Model of Presynaptic Bouton date: 2020-05-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper a geometrical model for simulation of the nerve impulses inside the presynaptic bouton is designed. The neurotransmitter flow is described by using partial differential equation with nonlinear term. The bouton is modeled as a distorted geosphere and the mitochondrion inside it as a highly modified cuboid. The quality of the mesh elements is examined. The changes of the amount of neurotransmitter during exocytosis are simulated. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304010/ doi: 10.1007/978-3-030-50420-5_10 id: cord-034839-6xctzwng author: Bień-Barkowska, Katarzyna title: Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets date: 2020-07-20 words: 9893.0 sentences: 480.0 pages: flesch: 58.0 cache: ./cache/cord-034839-6xctzwng.txt txt: ./txt/cord-034839-6xctzwng.txt summary: We aim to contribute to this strand of research by proposing a new self-exciting probability peaks-over-threshold (SEP-POT) model with the prerogative of being adequately tailored to the dynamics of real-world extreme events in financial markets. The point-process POT model approximates the time-varying conditional probability of an extreme loss over a given day with the help of a conditional intensity function that characterizes the arrival rate of such extreme events. According to such a point process approach to POT models, the first factor on the left-hand side of Equation (3) (i.e., the conditional probability of a threshold exceedance over day t + 1) can be derived based on the (time varying) conditional intensity function as follows: The dynamic versions of the POT models benefit from both (1) the point process theory, which allows for the time-varying intensity rate of threshold exceedances, and hence, the clustering of extreme losses, and (2) the EVT, which allows us to account for the tail risk of financial instruments. abstract: Forecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is built under a discrete-time framework. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices. The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial (NegBin) distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter. In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk (VaR) forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517354/ doi: 10.3390/e22070789 id: cord-340713-v5sdowb7 author: Bird, Jordan J. title: Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach date: 2020-10-28 words: 5669.0 sentences: 260.0 pages: flesch: 53.0 cache: ./cache/cord-340713-v5sdowb7.txt txt: ./txt/cord-340713-v5sdowb7.txt summary: The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. The classification problem of risk is therefore formulated based on prior knowledge of the pandemic in terms of class only, but the attributes to attempt to classify them are purely country-level information regardless of number of cases, deaths and other coronavirus specific data. Country-level pandemic risk and preparedness classification based on COVID-19 data Fig 10 shows a comparison of other models that were explored. Country-level pandemic risk and preparedness classification based on COVID-19 data Table 1 shows the predicted class values for the best models applied to each of the respective risk classification problems. abstract: In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding. url: https://doi.org/10.1371/journal.pone.0241332 doi: 10.1371/journal.pone.0241332 id: cord-285435-fu90vb2z author: Björklund, Tua A. title: Expanding entrepreneurial solution spaces in times of crisis: Business model experimentation amongst packaged food and beverage ventures date: 2020-11-30 words: 6817.0 sentences: 288.0 pages: flesch: 37.0 cache: ./cache/cord-285435-fu90vb2z.txt txt: ./txt/cord-285435-fu90vb2z.txt summary: Examining 844 social media posts of 66 ventures between March and May 2020 and interviewing 17 of these ventures, we found ventures to experiment with new business model variations, which not only expanded their set of solutions directly, but resulted in action-based learning leading to longer-term changes and increased capabilities for subsequent value creation. The current study sheds light into how entrepreneurs can experiment with new opportunities and business models to expand entrepreneurial solution spaces in such times of wide-spread collective crisis, examining the activities of packaged food and beverage ventures during the Covid-19 pandemic in Finland. Although further research into the post-crisis effects of such solution space expansions, as well as if, when and how new capabilities are subsequently put to use for business model innovation is still needed, at its best, entrepreneurial experimentation can create new value, capabilities and lasting resilience for both ventures and those in their ecosystem. abstract: Research summary Times of crisis require entrepreneurial responses to mitigate adverse effects and address new opportunities. This study focuses on how packaged food and drink entrepreneurs in Finland took action to create and capture new value during the Covid-19 crisis. Examining 844 social media posts of 66 ventures between March and May 2020 and interviewing 17 of these ventures, we found ventures to experiment with new business model variations, which not only expanded their set of solutions directly, but resulted in action-based learning leading to longer-term changes and increased capabilities for subsequent value creation. Furthermore, collaborative experiments and prosocial support increased the solution space through developing the capabilities of the ecosystem. Managerial summary The global lockdown measures in response to the coronavirus pandemic have disrupted supply, production, sales and consumption. Facing these constraints, entrepreneurs can respond quickly and experiment to create new liquidity and opportunities. Our analysis of packaged food and beverage entrepreneurs in Finland during the crisis shows how entrepreneurs leverage existing resources and acquire new ones to create new offerings, operations and partnerships. These initial actions serve as experiments to learn from in creating and revising business models, promoting a virtuous cycle of further action and expanding potential future solutions accessible to entrepreneurs. Importantly, opportunities available to the venture expand through both venture specific learning and through supporting other actors in the ecosystem. url: https://www.sciencedirect.com/science/article/pii/S2352673420300536 doi: 10.1016/j.jbvi.2020.e00197 id: cord-132307-bkkzg6h1 author: Blanco, Natalia title: Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness date: 2020-07-06 words: 3759.0 sentences: 164.0 pages: flesch: 51.0 cache: ./cache/cord-132307-bkkzg6h1.txt txt: ./txt/cord-132307-bkkzg6h1.txt summary: The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The early part of the curve was fit and statistical parameters were generated using Prism 8 (GraphPad) using the non-linear regression module using the program standard centered second order polynomial (quadratic), exponential growth, and the Gompertz growth model as defined by Prism 8, and a simple user-defined simple square model (N = At 2 + C) where N is the total number of cases, A and C are the fitting constants, and t is the number of days from the beginning of the epidemic curve. The total number of cases for each of 28 countries was plotted with time and several model equations were fit to the early part of the data before mitigating effects from public health policies began to change the rate of disease spread. abstract: Predicting an accurate expected number of future COVID-19 cases is essential to properly evaluate the effectiveness of any treatment or preventive measure. This study aimed to identify the most appropriate mathematical model to prospectively predict the expected number of cases without any intervention. The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The resulting model parameters were used to extrapolate predictions for more recent data. While the Gompertz growth models (mean R2 = 0.998) best fitted the current data, uncertainties in the eventual case limit made future predictions with logistic models prone to errors. Of the other models, the quadratic rate model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as determined by R2 values. The simple square and quadratic models accurately predicted the number of future total cases 37 and 36 days in advance respectively, compared to only 15 days for the simple exponential model. The simple exponential model significantly overpredicted the total number of future cases while the quadratic and simple square models did not. These results demonstrated that accurate future predictions of the case load in a given country can be made significantly in advance without the need for complicated models of population behavior and generate a reliable assessment of the efficacy of current prescriptive measures against disease spread. url: https://arxiv.org/pdf/2007.02712v1.pdf doi: nan id: cord-020193-3oqkdbq0 author: Bley, Katja title: Overcoming the Ivory Tower: A Meta Model for Staged Maturity Models date: 2020-03-06 words: 4734.0 sentences: 243.0 pages: flesch: 47.0 cache: ./cache/cord-020193-3oqkdbq0.txt txt: ./txt/cord-020193-3oqkdbq0.txt summary: We introduce this meta model regarding the different MM concepts, where each MM can be an instance of it as it provides a conceptual template for the rigorous development of new and the evaluation of existing maturity models. Based on a Summarizing, many approaches can support researchers in creating MMs. However, these guidelines are limited in their interpretability and validity, as they do not provide concrete terminology specifications or structural concept models. The development of the Meta Model for Maturity Models (4M) was based on a study of the most common and representative staged MMs. In order to elaborate sufficient meta model elements that are valid for a broad class of staged MMs, an analysis of different staged MMs, their development and their structure was conducted to summarize and analyze existing concepts, their relationships as well as their multiplicities and instantiations. abstract: When it comes to the economic and strategic development of companies, maturity models are regarded as silver bullets. However, the existing discrepancy between the large amount of existing, differently developed models and their rare application remains astonishing. We focus on this phenomenon by analyzing the models’ interpretability and possible structural and conceptual inconsistencies. By analyzing existing, staged maturity models, we develop a meta model for staged maturity models so different maturity models may share common semantics and syntax. Our meta model can therefore contribute to the conceptual rigor of existing and future maturity models in all domains and can be decisive for the success or failure of a maturity measurement in a company. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134302/ doi: 10.1007/978-3-030-44999-5_28 id: cord-340375-lhv83zac author: Bliznashki, Svetoslav title: A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York date: 2020-04-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We use Bayesian Estimation for the logistic growth model in order to estimate the spread of the coronavirus epidemic in the state of New York. Models weighting all data points equally as well as models with normal error structure prove inadequate to model the process accurately. On the other hand, a model with larger weights for more recent data points and with t-distributed errors seems reasonably capable of making at least short term predictions. url: https://doi.org/10.1101/2020.04.05.20054577 doi: 10.1101/2020.04.05.20054577 id: cord-332583-5enha3g9 author: Bodine, Erin N. title: Agent-Based Modeling and Simulation in Mathematics and Biology Education date: 2020-07-28 words: 7586.0 sentences: 358.0 pages: flesch: 44.0 cache: ./cache/cord-332583-5enha3g9.txt txt: ./txt/cord-332583-5enha3g9.txt summary: ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms. Whether students are working with ABMs in life science or math modeling classes, it is helpful for them to learn how to read and understand flow diagrams as they are often included in research publications that use agent-based modeling. While not every student necessarily needs to take a course exclusively focused on agent-based modeling, every undergraduate biology student should have the opportunity to utilize an ABM to perform experiments and to collect and analyze data. abstract: With advances in computing, agent-based models (ABMs) have become a feasible and appealing tool to study biological systems. ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms. url: https://doi.org/10.1007/s11538-020-00778-z doi: 10.1007/s11538-020-00778-z id: cord-263606-aiey8nvq author: Bonate, Peter L. title: Musings on the current state of COVID-19 modeling and reporting date: 2020-05-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: nan url: https://doi.org/10.1007/s10928-020-09692-2 doi: 10.1007/s10928-020-09692-2 id: cord-320141-892v3b7m author: Boshra, Mina title: 3D printing in critical care: a narrative review date: 2020-09-30 words: 4532.0 sentences: 240.0 pages: flesch: 46.0 cache: ./cache/cord-320141-892v3b7m.txt txt: ./txt/cord-320141-892v3b7m.txt summary: Our search produced 31 papers that described possible uses of 3DP in critical care which can be divided into three main themes: Medical education (Med-Ed), patient care, and clinical equipment modification (CEM) ( Table 1) . This review shows that 3DP can have a variety of utilities in the field of critical care including medical education, patient care, and development of clinical equipment; however, Med-Ed takes the lead as the most common utility of 3DP with over 70% of the papers found discussing the use of 3DP models to train medical students and/or residents. This narrative review has summarized the major uses of 3DP in the field of critical care which were found to be mainly within the realms of medical education (e.g. simulation models and training modules), patient care (e.g. wound care and personalized splints), and clinical equipment modification (e.g. 3DP laryngoscope handle). abstract: BACKGROUND: 3D printing (3DP) has gained interest in many fields of medicine including cardiology, plastic surgery, and urology due to its versatility, convenience, and low cost. However, critical care medicine, which is abundant with high acuity yet infrequent procedures, has not embraced 3DP as much as others. The discrepancy between the possible training or therapeutic uses of 3DP in critical care and what is currently utilized in other fields needs to be addressed. OBJECTIVE: This narrative literature review describes the uses of 3DP in critical care that have been documented. It also discusses possible future directions based on recent technological advances. METHODS: A literature search on PubMed was performed using keywords and Mesh terms for 3DP, critical care, and critical care skills. RESULTS: Our search found that 3DP use in critical care fell under the major categories of medical education (23 papers), patient care (4 papers) and clinical equipment modification (4 papers). Medical education showed the use of 3DP in bronchoscopy, congenital heart disease, cricothyroidotomy, and medical imaging. On the other hand, patient care papers discussed 3DP use in wound care, personalized splints, and patient monitoring. Clinical equipment modification papers reported the use of 3DP to modify stethoscopes and laryngoscopes to improve their performance. Notably, we found that only 13 of the 31 papers were directly produced or studied by critical care physicians. CONCLUSION: The papers discussed provide examples of the possible utilities of 3DP in critical care. The relative scarcity of papers produced by critical care physicians may indicate barriers to 3DP implementation. However, technological advances such as point-of-care 3DP tools and the increased demand for 3DP during the recent COVID-19 pandemic may change 3DP implementation across the critical care field. url: https://www.ncbi.nlm.nih.gov/pubmed/32997313/ doi: 10.1186/s41205-020-00081-6 id: cord-216208-kn0njkqg author: Botha, Andr'e E. title: A simple iterative map forecast of the COVID-19 pandemic date: 2020-03-23 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We develop a simple 3-dimensional iterative map model to forecast the global spread of the coronavirus disease. Our model contains at most two fitting parameters, which we determine from the data supplied by the world health organisation for the total number of cases and new cases each day. We find that our model provides a surprisingly good fit to the currently-available data, which exhibits a cross-over from exponential to power-law growth, as lock-down measures begin to take effect. Before these measures, our model predicts exponential growth from day 30 to 69, starting from the date on which the world health organisation provided the first `Situation report' (21 January 2020 $-$ day 1). Based on this initial data the disease may be expected to infect approximately 23% of the global population, i.e. about 1.76 billion people, taking approximately 83 million lives. Under this scenario, the global number of new cases is predicted to peak on day 133 (about the middle of May 2020), with an estimated 60 million new cases per day. If current lock-down measures can be maintained, our model predicts power law growth from day 69 onward. Such growth is comparatively slow and would have to continue for several decades before a sufficient number of people (at least 23% of the global population) have developed immunity to the disease through being infected. Lock-down measures appear to be very effective in postponing the unimaginably large peak in the daily number of new cases that would occur in the absence of any interventions. However, should these measure be relaxed, the spread of the disease will most likely revert back to its original exponential growth pattern. As such, the duration and severity of the lock-down measures should be carefully timed against their potentially devastating impact on the world economy. url: https://arxiv.org/pdf/2003.10532v3.pdf doi: nan id: cord-017728-yazo0lga author: Brauer, Fred title: Compartmental Models in Epidemiology date: 2008 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We describe and analyze compartmental models for disease transmission. We begin with models for epidemics, showing how to calculate the basic reproduction number and the final size of the epidemic. We also study models with multiple compartments, including treatment or isolation of infectives. We then consider models including births and deaths in which there may be an endemic equilibrium and study the asymptotic stability of equilibria. We conclude by studying age of infection models which give a unifying framework for more complicated compartmental models. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122373/ doi: 10.1007/978-3-540-78911-6_2 id: cord-018899-tbfg0vmd author: Brauer, Fred title: Epidemic Models date: 2011-10-03 words: 19642.0 sentences: 1293.0 pages: flesch: 65.0 cache: ./cache/cord-018899-tbfg0vmd.txt txt: ./txt/cord-018899-tbfg0vmd.txt summary: For example, one of the fundamental results in mathematical epidemiology is that most mathematical epidemic models, including those that include a high degree of heterogeneity, usually exhibit "threshold" behavior, which in epidemiological terms can be stated as follows: If the average number of secondary infections caused by an average infective is less than one, a disease will die out, while if it exceeds one there will be an epidemic. [Technically, the attack rate should be called an attack ratio, since it is dimensionless and is not a rate.] The final size relation (9.3) can be generalized to epidemic models with more complicated compartmental structure than the simple SIR model (9.2), including models with exposed periods, treatment models, and models including quarantine of suspected individuals and isolation of diagnosed infectives. Compartmental models for epidemics are not suitable for describing the beginning of a disease outbreak because they assume that all members of a population are equally likely to make contact with a very small number of infectives. abstract: Communicable diseases such as measles, influenza, and tuberculosis are a fact of life. We will be concerned with both epidemics, which are sudden outbreaks of a disease, and endemic situations, in which a disease is always present. The AIDS epidemic, the recent SARS epidemic, recurring influenza pandemics, and outbursts of diseases such as the Ebola virus are events of concern and interest to many people. The prevalence and effects of many diseases in less-developed countries are probably not as well known but may be of even more importance. Every year millions, of people die of measles, respiratory infections, diarrhea, and other diseases that are easily treated and not considered dangerous in the Western world. Diseases such as malaria, typhus, cholera, schistosomiasis, and sleeping sickness are endemic in many parts of the world. The effects of high disease mortality on mean life span and of disease debilitation and mortality on the economy in afflicted countries are considerable. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123900/ doi: 10.1007/978-1-4614-1686-9_9 id: cord-319436-mlitd45q author: Brinati, D. title: Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study date: 2020-04-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Background - The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. Material and methods - We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. Results - We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. Discussion - This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation. This tool is available at https://covid19-blood-ml.herokuapp.com. url: http://medrxiv.org/cgi/content/short/2020.04.22.20075143v1?rss=1 doi: 10.1101/2020.04.22.20075143 id: cord-016965-z7a6eoyo author: Brockmann, Dirk title: Human Mobility, Networks and Disease Dynamics on a Global Scale date: 2017-10-23 words: 6792.0 sentences: 396.0 pages: flesch: 55.0 cache: ./cache/cord-016965-z7a6eoyo.txt txt: ./txt/cord-016965-z7a6eoyo.txt summary: In addition for infected sites to transmit the disease to neighboring susceptible lattice sites, every now and then (with a probability of 1%) they can also Fig. 19 .1) geographic distance to the initial outbreak location is no longer a good predictor of arrival time, unlike in systems with local or spatially limited host mobility infect randomly chosen lattice sites anywhere in the system. A visual inspection of the air-transportation system depicted in Fig. 19 .1 is sufficiently convincing that the significant fraction of long-range connections in global mobility will not only increase the speed at which infectious diseases spread but, more importantly, also cause the patterns of spread to exhibit high spatial incoherence and complexity caused by the intricate connectivity of the air-transportation network. Figure 19 .7 shows that also the model epidemic depicts only a weak correlation between geographic distance to the outbreak location and arrival time. abstract: Disease dynamics is a complex phenomenon and in order to address these questions expertises from many disciplines need to be integrated. One method that has become particularly important during the past few years is the development of computational models and computer simulations that help addressing these questions. In the focus of this chapter are emergent infectious diseases that bear the potential of spreading across the globe, exemplifying how connectivity in a globalized world has changed the way human-mediated processes evolve in the 21st century. The examples of most successful predictions of disease dynamics given in the chapter illustrate that just feeding better and faster computers with more and more data may not necessarily help understanding the relevant phenomena. It might rather be much more useful to change the conventional way of looking at the patterns and to assume a correspondingly modified viewpoint—as most impressively shown with the examples given in this chapter. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121407/ doi: 10.1007/978-3-319-67798-9_19 id: cord-000332-u3f89kvg author: Broeck, Wouter Van den title: The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale date: 2011-02-02 words: 7455.0 sentences: 337.0 pages: flesch: 41.0 cache: ./cache/cord-000332-u3f89kvg.txt txt: ./txt/cord-000332-u3f89kvg.txt summary: The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. GLEaMviz is a client-server software system that can model the world-wide spread of epidemics for human transmissible diseases like influenzalike illnesses (ILI), offering extensive flexibility in the design of the compartmental model and scenario setup, including computationally-optimized numerical simulations based on high-resolution global demographic and mobility data. GLEaMviz makes use of a stochastic and discrete computational scheme to model epidemic spread called "GLEaM" -GLobal Epidemic and Mobility model, presented in previously published work [6, 3, 14] which is based on a geo-referenced metapopulation approach that considers 3,362 subpopulations in 220 countries of the world, as well as air travel flow connections and short-range commuting data. abstract: BACKGROUND: Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions. RESULTS: We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side. CONCLUSIONS: The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3048541/ doi: 10.1186/1471-2334-11-37 id: cord-129272-p1jeiljo author: Broniec, William title: Using VERA to explain the impact of social distancing on the spread of COVID-19 date: 2020-03-30 words: 1932.0 sentences: 113.0 pages: flesch: 45.0 cache: ./cache/cord-129272-p1jeiljo.txt txt: ./txt/cord-129272-p1jeiljo.txt summary: We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the spread of COVID-19. In this article, we describe VERA_Epidemiology (or just VERA for short), an interactive AI tool that enables users to build conceptual models of the impact of social distancing on COVID-19. We describe the use of VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity. We describe the use of VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity. The conceptual models in Figure 2 illustrate an interaction between social distancing and COVID-19 cases. Now that we have illustrated the core techniques in VERA, we describe the use of VERA develop the SIR model for understanding the relationship between social distancing and the spread of COVID-19. abstract: COVID-19 continues to spread across the country and around the world. Current strategies for managing the spread of COVID-19 include social distancing. We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the spread of COVID-19. Then, VERA automatically spawns agent-based simulations from the conceptual models, and, given a data set, automatically fills in the values of the simulation parameters from the data. Next, the user can view the simulation results, and, if needed, revise the simulation parameters and run another experimental trial, or build an alternative conceptual model. We describe the use VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity. url: https://arxiv.org/pdf/2003.13762v1.pdf doi: nan id: cord-030686-wv77zwsc author: Budde, Carlos E. title: FIG: The Finite Improbability Generator date: 2020-03-13 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This paper introduces the statistical model checker FIGV, that estimates transient and steady-state reachability properties in stochastic automata. This software tool specialises in Rare Event Simulation via importance splitting, and implements the algorithms RESTART and Fixed Effort. FIG is push-button automatic since the user need not define an importance function: this function is derived from the model specification plus the property query. The tool operates with Input/Output Stochastic Automata with Urgency, aka IOSA models, described either in the native syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific models. Written in C++, FIG can outperform other state-of-the-art tools for Rare Event Simulation. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439731/ doi: 10.1007/978-3-030-45190-5_27 id: cord-020683-5s3lghj6 author: Buonomo, Bruno title: Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model date: 2020-04-09 words: 4675.0 sentences: 267.0 pages: flesch: 54.0 cache: ./cache/cord-020683-5s3lghj6.txt txt: ./txt/cord-020683-5s3lghj6.txt summary: The model has the basic structure of SIRI compartments (susceptible–infectious–recovered–infectious) and is implemented by taking into account of the behavioral changes of individuals in response to the available information on the status of the disease in the community. Therefore, it becomes an intriguing problem to qualitatively assess how the administration of a vaccine could affect the outbreak, taking into account of the behavioral changes of individuals in response to the information available on the status of the disease in the community. Since the disease of our interest has both reinfection and partial immunity after infection, we first consider the SIRI model, which is given by the following nonlinear ordinary differential equations (the upper dot denotes the time derivative) [18] : In the next section we will modify the SIRI model (4) to assess how an hypothetical vaccine could control the outbreak, taking into account of the behavioral changes of individuals produced by the information available on the status of the disease in the community. abstract: A mathematical model is proposed to assess the effects of a vaccine on the time evolution of a coronavirus outbreak. The model has the basic structure of SIRI compartments (susceptible–infectious–recovered–infectious) and is implemented by taking into account of the behavioral changes of individuals in response to the available information on the status of the disease in the community. We found that the cumulative incidence may be significantly reduced when the information coverage is high enough and/or the information delay is short, especially when the reinfection rate is high enough to sustain the presence of the disease in the community. This analysis is inspired by the ongoing outbreak of a respiratory illness caused by the novel coronavirus COVID-19. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144546/ doi: 10.1007/s11587-020-00506-8 id: cord-015255-1qhgeirb author: Busby, J S title: Managing the social amplification of risk: a simulation of interacting actors date: 2012-07-11 words: 9934.0 sentences: 404.0 pages: flesch: 45.0 cache: ./cache/cord-015255-1qhgeirb.txt txt: ./txt/cord-015255-1qhgeirb.txt summary: Such cases are therefore an important and promising setting for exploring the idea that amplification is only in the heads of social actors, and for exploring the notion that this might nonetheless produce observable, and potentially highly consequential, outcomes in a way that risk managers need to understand. In the remainder of this article we therefore explore the consequences of the idea that social risk amplification is nothing more than an attribution, or judgment that one social actor makes of another, and try to see what implications this might have for risk managers based on a systems dynamics model. Therefore in the second model, shown in Figure 2 , we now have a subsystem in which a risk manager (a government agency or an industrial undertaking in the case of zoonotic disease outbreaks) observes the public risk perception in relation to the expert risk assessment, and communicates a risk level that is designed to compensate for any discrepancy between the two. abstract: A central problem in managing risk is dealing with social processes that either exaggerate or understate it. A longstanding approach to understanding such processes has been the social amplification of risk framework. But this implies that some true level of risk becomes distorted in social actors’ perceptions. Many risk events are characterised by such uncertainties, disagreements and changes in scientific knowledge that it becomes unreasonable to speak of a true level of risk. The most we can often say in such cases is that different groups believe each other to be either amplifying or attenuating a risk. This inherent subjectivity raises the question as to whether risk managers can expect any particular kinds of outcome to emerge. This question is the basis for a case study of zoonotic disease outbreaks using systems dynamics as a modelling medium. The model shows that processes suggested in the social amplification of risk framework produce polarised risk responses among different actors, but that the subjectivity magnifies this polarisation considerably. As this subjectivity takes more complex forms it leaves problematic residues at the end of a disease outbreak, such as an indefinite drop in economic activity and an indefinite increase in anxiety. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099211/ doi: 10.1057/jors.2012.80 id: cord-005321-b3pyg5b3 author: Cai, Li-Ming title: Global analysis of an epidemic model with vaccination date: 2017-07-21 words: 5605.0 sentences: 412.0 pages: flesch: 61.0 cache: ./cache/cord-005321-b3pyg5b3.txt txt: ./txt/cord-005321-b3pyg5b3.txt summary: In some of these studies (e.g., papers [16, 31, 43] ), authors have shown that the dynamics of the model are determined by the disease''s basic reproduction number 0 . In order to derive the equations of the mathematical model, we divide the total population N in a community into five compartments: susceptible, exposed (not yet infectious), infective, recovered, and vaccinated; the numbers in these states are denoted by S(t), If ψ = 0 and limit γ 1 → ∞, system (2.1) will be reduced into an SIV epidemic model in [36] , where authors investigate the effect of imperfect vaccines on the disease''s transmission dynamics. This is also in line with results in paper [26] , where the vaccination-free model (2.3) has a globally asymptotically stable equilibrium if the basic reproduction number R 0 is less than one. Global results for an epidemic model with vaccination that exhibits backward bifurcation abstract: In this paper, an epidemic dynamical model with vaccination is proposed. Vaccination of both newborn and susceptible is included in the present model. The impact of the vaccination strategy with the vaccine efficacy is explored. In particular, the model exhibits backward bifurcations under the vaccination level, and bistability occurrence can be observed. Mathematically, a bifurcation analysis is performed, and the conditions ensuring that the system exhibits backward bifurcation are provided. The global dynamics of the equilibrium in the model are also investigated. Numerical simulations are also conducted to confirm and extend the analytic results. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090535/ doi: 10.1007/s12190-017-1124-1 id: cord-117688-20gfpbyf author: Cakmakli, Cem title: Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model date: 2020-07-03 words: 7551.0 sentences: 423.0 pages: flesch: 58.0 cache: ./cache/cord-117688-20gfpbyf.txt txt: ./txt/cord-117688-20gfpbyf.txt summary: This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. A real-time estimation and forecasting exercise starting from April show that the proposed model with time varying parameters indeed provide timely information on the current stance of the pandemic ahead of the competing models. (2020) use a least squares based approach on a rolling window of daily observations and document the time variation of parameters in the SIRD based model using Chinese data. Independent of the analysis of COVID-19 pandemic, observation-driven models for count data are considered in many different cases. Finally, we explore whether this capability of the TVP-SIRD model in reflecting the stance of the pandemic in a timely manner indeed proved to be useful in forecasting the number of active cases. abstract: This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing. url: https://arxiv.org/pdf/2007.02726v1.pdf doi: nan id: cord-208252-e0vlaoii author: Calvetti, Daniela title: Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model date: 2020-05-09 words: 7814.0 sentences: 351.0 pages: flesch: 49.0 cache: ./cache/cord-208252-e0vlaoii.txt txt: ./txt/cord-208252-e0vlaoii.txt summary: A Bayesian particle filtering algorithm is used to update dynamically the relevant cohort and simultaneously estimate the transmission rate as the new data on the number of new infections and disease related death become available. When we apply the model and particle filter algorithm to COVID-19 infection data from several counties in Northeastern Ohio and Southeastern Michigan we found the proposed reproduction number $R_0$ to have a consistent dynamic behavior within both states, thus proving to be a reliable summary of the success of the mitigation measures. The equilibrium value, which can be analytically calculated from the model parameters, corresponds well to the model-based estimated ratio and can be used to define a dynamically changing effective basic reproduction number R 0 for the epidemic, facilitating the comparison of model predictions with other models. abstract: In this article, we consider a dynamic epidemiology model for the spread of the COVID-19 infection. Starting from the classical SEIR model, the model is modified so as to better describe characteristic features of the underlying pathogen and its infectious modes. In line with the large number of secondary infections not related to contact with documented infectious individuals, the model includes a cohort of asymptomatic or oligosymptomatic infectious individuals, not accounted for in the data of new daily counts of infections. A Bayesian particle filtering algorithm is used to update dynamically the relevant cohort and simultaneously estimate the transmission rate as the new data on the number of new infections and disease related death become available. The underlying assumption of the model is that the infectivity rate is dynamically changing during the epidemics, either because of a mutation of the pathogen or in response to mitigation and containment measures. The sequential Bayesian framework naturally provides a quantification of the uncertainty in the estimate of the model parameters, including the reproduction number, and of the size of the different cohorts. Moreover, we introduce a dimensionless quantity, which is the equilibrium ratio between asymptomatic and symptomatic cohort sizes, and propose a simple formula to estimate the quantity. This ratio leads naturally to another dimensionless quantity that plays the role of the basic reproduction number $R_0$ of the model. When we apply the model and particle filter algorithm to COVID-19 infection data from several counties in Northeastern Ohio and Southeastern Michigan we found the proposed reproduction number $R_0$ to have a consistent dynamic behavior within both states, thus proving to be a reliable summary of the success of the mitigation measures. url: https://arxiv.org/pdf/2005.04365v2.pdf doi: nan id: cord-283907-ev1ghlwl author: Cao, Lingyan title: Electrical load prediction of healthcare buildings through single and ensemble learning date: 2020-11-30 words: 8756.0 sentences: 418.0 pages: flesch: 44.0 cache: ./cache/cord-283907-ev1ghlwl.txt txt: ./txt/cord-283907-ev1ghlwl.txt summary: Therefore, in this paper, the authors propose a one day-ahead electrical load forecasting model based on single and ensemble machine learning algorithms. In the present study, electrical load forecasting models of healthcare buildings are developed based on single and ensemble machine learning algorithms by taking account multi-factors simultaneously. To address this gap, this study takes into account the occupancy of outpatients, emergency patients, and inpatients and employs single and ensemble machine learning algorithms to predict the electric load demand of healthcare buildings. It can be seen that the electric load prediction for the healthcare buildings includes three steps: (1) Identify the relevant features and gather data, (2) Train single and ensemble learning models with prepared dataset, and (3) Compare the prediction performance of different models. Electrical load forecasting is naturally considered to be a regression problem in machine learning, aiming to accurately predict the energy demand of buildings based on its relationship with a given set of independent input variables. abstract: Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models. url: https://www.sciencedirect.com/science/article/pii/S2352484720313585 doi: 10.1016/j.egyr.2020.10.005 id: cord-198449-cru40qp4 author: Carballosa, Alejandro title: Incorporating social opinion in the evolution of an epidemic spread date: 2020-07-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Attempts to control the epidemic spread of COVID19 in the different countries often involve imposing restrictions to the mobility of citizens. Recent examples demonstrate that the effectiveness of these policies strongly depends on the willingness of the population to adhere them. And this is a parameter that it is difficult to measure and control. We demonstrate in this manuscript a systematic way to check the mood of a society and a way to incorporate it into dynamical models of epidemic propagation. We exemplify the process considering the case of Spain although the results and methodology can be directly extrapolated to other countries. url: https://arxiv.org/pdf/2007.04619v1.pdf doi: nan id: cord-331849-o346txxr author: Cardoso, Pedro J.S. title: Computational Science in the Interconnected World: Selected papers from 2019 International Conference on Computational Science date: 2020-09-21 words: 2569.0 sentences: 136.0 pages: flesch: 36.0 cache: ./cache/cord-331849-o346txxr.txt txt: ./txt/cord-331849-o346txxr.txt summary: Against this background, the International Conference on Computational Science (ICCS), annually held since 2001, has grown to become a major event in the CS field, with hundreds of experts meeting and discussing their works, along with keynote lectures presented by world''s renowned researchers. As matter of fact, the context in which this editorial paper is being written, just a few months after the declaration of the COVID-19 pandemic, highlights the importance of this interconnected world and keeps CS in the forefront of the needs, reflected in the epidemiological research that is supported in computational methods [3] [6] or the proved accuracy of many disease propagation models [7] [12] . Their study pays attention to random data access with data recurrence as major issue to attain performance, proposing a method to avoid these data races for high performance on many-core CPU architectures with wide single instruction, multiple data (SIMD) units, exemplified by finite-element earthquake simulations. abstract: nan url: https://api.elsevier.com/content/article/pii/S1877750320305214 doi: 10.1016/j.jocs.2020.101222 id: cord-295786-cpuz08vl author: Castillo-Sánchez, Gema title: Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review date: 2020-11-09 words: 7120.0 sentences: 509.0 pages: flesch: 53.0 cache: ./cache/cord-295786-cpuz08vl.txt txt: ./txt/cord-295786-cpuz08vl.txt summary: This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. This scoping review aims to identify the current ML techniques used to predict suicide risk based on information posted on social networks. The authors have performed a systematic review to identify relevant papers that use suicide risk assessment models in social networks. To select the relevant studies on this topic, the authors defined the following inclusion criteria: & The studies include algorithms or models to estimate suicide risk using the social network. The research papers were excluded if they were not written in the English language, do not include a specific suicide intervention or do not report information regarding technical aspects of the model/algorithm used to detect suicide risk on social networks. The results of the application of artificial intelligence algorithms or models for suicide risk identification using data collected from social networks have been analyzed in this study. abstract: According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-020-01669-5. url: https://doi.org/10.1007/s10916-020-01669-5 doi: 10.1007/s10916-020-01669-5 id: cord-162105-u0w56xrp author: Centeno, Raffy S. title: How much did the Tourism Industry Lost? Estimating Earning Loss of Tourism in the Philippines date: 2020-04-21 words: 3562.0 sentences: 219.0 pages: flesch: 59.0 cache: ./cache/cord-162105-u0w56xrp.txt txt: ./txt/cord-162105-u0w56xrp.txt summary: Based on the Akaike''s Information Criterion (AIC) and Root Mean Squared Error, ARIMA(1,1,1)$times$(1,0,1)$_{12}$ was identified to be the better model among the others with an AIC value of $-414.51$ and RMSE of $47884.85$. The objective of this research is to forecast the monthly earnings loss of the tourism industry during the COVID-19 pandemic by forecasting the monthly foreign visitor arrivals using Seasonal Autoregressive Integrated Moving Average. These patterns suggest a seasonal autoregressive integrated moving average (SARIMA) approach in modeling and forecasting the monthly foreign visitor arrivals in the Philippines. Akaike Information Criterion and Root Mean Squared Error were used to identify which model was used to model and forecast the monthly foreign visitor arrivals in the Philippines. 1. The order of SARIMA model used to forecast the monthly foreign visitor arrival is ARIMA (1,1,1)×(1,0,1) 12 since it produced a relatively low AIC of −414.51 and the lowest RMSE of 47884.85 using an out-of-sample data. abstract: The study aimed to forecast the total earnings lost of the tourism industry of the Philippines during the COVID-19 pandemic using seasonal autoregressive integrated moving average. Several models were considered based on the autocorrelation and partial autocorrelation graphs. Based on the Akaike's Information Criterion (AIC) and Root Mean Squared Error, ARIMA(1,1,1)$times$(1,0,1)$_{12}$ was identified to be the better model among the others with an AIC value of $-414.51$ and RMSE of $47884.85$. Moreover, it is expected that the industry will have an estimated earning loss of around 170.5 billion pesos if the COVID-19 crisis will continue up to July. Possible recommendations to mitigate the problem includes stopping foreign tourism but allowing regions for domestic travels if the regions are confirmed to have no cases of COVID-19, assuming that every regions will follow the stringent guidelines to eliminate or prevent transmissions; or extending this to countries with no COVID-19 cases. url: https://arxiv.org/pdf/2004.09952v1.pdf doi: nan id: cord-252894-c02v47jz author: Chae, Sangwon title: Predicting Infectious Disease Using Deep Learning and Big Data date: 2018-07-27 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society. url: https://doi.org/10.3390/ijerph15081596 doi: 10.3390/ijerph15081596 id: cord-225347-lnzz2chk author: Chakraborty, Tanujit title: Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges date: 2020-10-10 words: 10203.0 sentences: 585.0 pages: flesch: 53.0 cache: ./cache/cord-225347-lnzz2chk.txt txt: ./txt/cord-225347-lnzz2chk.txt summary: Several statistical and machine learning methods for real-time forecasting of the new and cumulative confirmed cases of COVID-19 are developed to overcome limitations of the epidemiological model approaches and assist public health planning and policy-making [25, 91, 6, 26, 23] . As such, we aim to perform a meaningful data analysis, including the study of time series characteristics, to provide a suitable and comprehensive knowledge foundation for the future step of selecting an apt forecasting method. Five time series COVID-19 datasets for the USA, India, Russia, Brazil, and Peru UK are considered for assessing twenty forecasting models (individual, ensemble, and hybrid). Results for USA COVID-19 data: Among the single models, ARIMA (2, 1, 4) performs best in terms of accuracy metrics for 15-days ahead forecasts. Results for India COVID-19 data: Among the single models, ANN performs best in terms of accuracy metrics for 15-days ahead forecasts. abstract: The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously tried to forecast COVID-19 confirmed cases for profoundly affected countries. Due to extreme uncertainty and nonstationarity in the time series data, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. But, epidemic forecasting has a dubious track record. Its failures became more prominent due to insufficient data input, flaws in modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, inadequate past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, and lack of expertise in crucial disciplines. This chapter focuses on assessing different short-term forecasting models that can forecast the daily COVID-19 cases for various countries. In the form of an empirical study on forecasting accuracy, this chapter provides evidence to show that there is no universal method available that can accurately forecast pandemic data. Still, forecasters' predictions are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. url: https://arxiv.org/pdf/2010.05079v1.pdf doi: nan id: cord-319885-8qyavs7m author: Chan, Stephen title: Count regression models for COVID-19 date: 2021-02-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: At the end of 2019, the current novel coronavirus emerged as a severe acute respiratory disease that has now become a worldwide pandemic. Future generations will look back on this difficult period and see how our society as a whole united and rose to this challenge. Many reports have suggested that this new virus is becoming comparable to the Spanish flu pandemic of 1918. We provide a statistical study on the modelling and analysis of the daily incidence of COVID-19 in eighteen countries around the world. In particular, we investigate whether it is possible to fit count regression models to the number of daily new cases of COVID-19 in various countries and make short term predictions of these numbers. The results suggest that the biggest advantage of these methods is that they are simplistic and straightforward allowing us to obtain preliminary results and an overall picture of the trends in the daily confirmed cases of COVID-19 around the world. The best fitting count regression model for modelling the number of new daily COVID-19 cases of all countries analysed was shown to be a negative binomial distribution with log link function. Whilst the results cannot solely be used to determine and influence policy decisions, they provide an alternative to more specialised epidemiological models and can help to support or contradict results obtained from other analysis. url: https://www.sciencedirect.com/science/article/pii/S0378437120307743 doi: 10.1016/j.physa.2020.125460 id: cord-330668-7aw17jf8 author: Chen, Cheng-Chang title: ORF8a of SARS-CoV forms an ion channel: Experiments and molecular dynamics simulations date: 2011-02-28 words: 4806.0 sentences: 274.0 pages: flesch: 56.0 cache: ./cache/cord-330668-7aw17jf8.txt txt: ./txt/cord-330668-7aw17jf8.txt summary: The protein is synthesized using solid phase peptide synthesis and reconstituted into artificial lipid bilayers that forms cation-selective ion channels with a main conductance level of 8.9±0.8pS at elevated temperature (38.5°C). Computational modeling studies including multi nanosecond molecular dynamics simulations in a hydrated POPC lipid bilayer are done with a 22 amino acid transmembrane helix to predict a putative homooligomeric helical bundle model. Before embedding low energy models into lipid bilayers two amino acids residues of the protein were added at the N and C termini of each of the helices in each bundle model to account for the consequences of their interaction with the lipid bilayer during the simulation. The idealized monomeric TM helix based on the consensus sequence Leu-3 to Val-20 (Fig. 1A) shows clustering of hydrophilic residues (Thr-8, Ser-11, Ser-14 and Thr-18) on one side suggesting that the four hydrophilic amino acids form the lumen of the pore in a homooligomeric helical bundle channel model. abstract: Abstract ORF8a protein is 39 residues long and contains a single transmembrane domain. The protein is synthesized using solid phase peptide synthesis and reconstituted into artificial lipid bilayers that forms cation-selective ion channels with a main conductance level of 8.9±0.8pS at elevated temperature (38.5°C). Computational modeling studies including multi nanosecond molecular dynamics simulations in a hydrated POPC lipid bilayer are done with a 22 amino acid transmembrane helix to predict a putative homooligomeric helical bundle model. A structural model of a pentameric bundle is proposed with cysteines, serines and threonines facing the pore. url: https://www.ncbi.nlm.nih.gov/pubmed/20708597/ doi: 10.1016/j.bbamem.2010.08.004 id: cord-167889-um3djluz author: Chen, Jianguo title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date: 2020-07-04 words: 12248.0 sentences: 768.0 pages: flesch: 50.0 cache: ./cache/cord-167889-um3djluz.txt txt: ./txt/cord-167889-um3djluz.txt summary: The progress of CT image inspection based on AI usually includes the following steps: Region Of Interest (ROI) segmentation, lung tissue feature extraction, candidate infection region detection, and COVID-19 classification. Data sources Methods Country/region Huang [82] Yang [231] , WHO [216] CNN, LSTM, MLP, GRU China Hu [80, 81] The Paper [148] , WHO [216] MAE, clustering China Yang [233] Baidu [16] SEIR, LSTM China Fong [51, 52] NHC [139] SVM, PNN China Ai [3] WHO [54, 216] ANFIS, FPA China, USA Rizk [168] WHO [216] ISACL-MFNN USA, Italy, Spain Giuliani [62] Italy [144] EMTMGL Italy Ayyoubzadeh [14] Worldometer [218] , Google [201] LR, LSTM Iran Marini [129, 130] Swiss population Enerpol Switzerland Lai [110] IATA [126] , Worldpop [219] ML Global Punn [155] JHU CSSE [49] SVR, PR, DNN, LSTM, RNN Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model abstract: The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researches to continue to maximize the advantages of AI and big data to fight COVID-19. url: https://arxiv.org/pdf/2007.02202v1.pdf doi: nan id: cord-024283-ydnxotsq author: Chen, Jiarui title: BESTox: A Convolutional Neural Network Regression Model Based on Binary-Encoded SMILES for Acute Oral Toxicity Prediction of Chemical Compounds date: 2020-02-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Compound toxicity prediction is a very challenging and critical task in the drug discovery and design field. Traditionally, cell or animal-based experiments are required to confirm the acute oral toxicity of chemical compounds. However, these methods are often restricted by availability of experimental facilities, long experimentation time, and high cost. In this paper, we propose a novel convolutional neural network regression model, named BESTox, to predict the acute oral toxicity ([Formula: see text]) of chemical compounds. This model learns the compositional and chemical properties of compounds from their two-dimensional binary matrices. Each matrix encodes the occurrences of certain atom types, number of bonded hydrogens, atom charge, valence, ring, degree, aromaticity, chirality, and hybridization along the SMILES string of a given compound. In a benchmark experiment using a dataset of 7413 observations (train/test 5931/1482), BESTox achieved a squared correlation coefficient ([Formula: see text]) of 0.619, root-mean-squared error (RMSE) of 0.603, and mean absolute error (MAE) of 0.433. Despite of the use of a shallow model architecture and simple molecular descriptors, our method performs comparably against two recently published models. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197065/ doi: 10.1007/978-3-030-42266-0_12 id: cord-289496-d8ac6l6o author: Chen, Min title: The introduction of population migration to SEIAR for COVID-19 epidemic modelling with an efficient intervention strategy date: 2020-08-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper, we present a mathematical model of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed enhanced model, which will be referred to as the SEIR (Susceptible-Exposed-Infectious-Recovered) model with population migration, is inspired by the role that asymptomatic infected individuals, as well as population movements can play a crucial role in spreading the virus. In the model, the infected and the basic reproduction numbers are compared under the influence of intervention policies. The experimental simulation results show the impact of social distancing and migration-in rates on reducing the total number of infections and the basic reproductions. And then, the importance of controlling the number of migration-in people and the policy of restricting residents’ movements in preventing the spread of COVID-19 pandemic is verfied. url: https://www.sciencedirect.com/science/article/pii/S1566253520303304?v=s5 doi: 10.1016/j.inffus.2020.08.002 id: cord-323743-hr23ux58 author: Chen, Xiaofeng title: A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study date: 2020-04-16 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student’s t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1–3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. url: https://doi.org/10.1007/s00330-020-06829-2 doi: 10.1007/s00330-020-06829-2 id: cord-295116-eo887olu author: Chimmula, Vinay Kumar Reddy title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() date: 2020-05-08 words: 4708.0 sentences: 252.0 pages: flesch: 50.0 cache: ./cache/cord-295116-eo887olu.txt txt: ./txt/cord-295116-eo887olu.txt summary: title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Recurrent LSTM networks has capability to address the limitations of traditional time series forecasting techniques by adapting nonlinearities of given COVID-19 dataset and can result state of the art results on temporal data. Accord-COVID-19 forecasting using LSTM Networks ing to this second model within 10 days, Canada is expected to see exponential growth of confirmed cases. abstract: On March 11(th) 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14(th) day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases. url: https://api.elsevier.com/content/article/pii/S0960077920302642 doi: 10.1016/j.chaos.2020.109864 id: cord-048353-hqc7u9w3 author: Chis Ster, Irina title: Transmission Parameters of the 2001 Foot and Mouth Epidemic in Great Britain date: 2007-06-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Despite intensive ongoing research, key aspects of the spatial-temporal evolution of the 2001 foot and mouth disease (FMD) epidemic in Great Britain (GB) remain unexplained. Here we develop a Markov Chain Monte Carlo (MCMC) method for estimating epidemiological parameters of the 2001 outbreak for a range of simple transmission models. We make the simplifying assumption that infectious farms were completely observed in 2001, equivalent to assuming that farms that were proactively culled but not diagnosed with FMD were not infectious, even if some were infected. We estimate how transmission parameters varied through time, highlighting the impact of the control measures on the progression of the epidemic. We demonstrate statistically significant evidence for assortative contact patterns between animals of the same species. Predictive risk maps of the transmission potential in different geographic areas of GB are presented for the fitted models. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1876810/ doi: 10.1371/journal.pone.0000502 id: cord-133273-kvyzuayp author: Christ, Andreas title: Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020) date: 2020-10-05 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes 'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students. url: https://arxiv.org/pdf/2010.16241v1.pdf doi: nan id: cord-302336-zj3oixvk author: Clift, Ash K title: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study date: 2020-10-21 words: 7352.0 sentences: 320.0 pages: flesch: 44.0 cache: ./cache/cord-302336-zj3oixvk.txt txt: ./txt/cord-302336-zj3oixvk.txt summary: 13 The use of primary care datasets with linkage to registries such as death records, hospital admissions data, and covid-19 testing results represents a novel approach to clinical risk prediction modelling for covid-19. Patients entered the cohort on 24 January 2020 (date of first confirmed case of covid-19 in the UK) and were followed up until they had the outcome of interest or the end of the first study period (30 April 2020), which was the date up to which linked data were available at the time of the derivation of the model, or the second time period (1 May 2020 until 30 June 2020) for the temporal cohort validation. 25 D statistics (a discrimination measure that quantifies the separation in survival between patients with different levels of predicted risks) and Harrell''s C statistics (a discrimination metric that quantifies the extent to which people with higher risk scores have earlier events) were evaluated at 97 days (the maximum followup period available at the time of the derivation of the model) and 60 days for the second temporal validation, with corresponding 95% confidence intervals. abstract: OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R(2)); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves. url: https://www.ncbi.nlm.nih.gov/pubmed/33082154/ doi: 10.1136/bmj.m3731 id: cord-308652-i6q23olv author: Cobos-Sanchiz, David title: The Importance of Work-Related Events and Changes in Psychological Distress and Life Satisfaction amongst Young Workers in Spain: A Gender Analysis date: 2020-06-30 words: 7149.0 sentences: 339.0 pages: flesch: 50.0 cache: ./cache/cord-308652-i6q23olv.txt txt: ./txt/cord-308652-i6q23olv.txt summary: The aim of this paper is therefore to understand the importance of work-related events and changes experienced in the last year in psychological distress and life satisfaction for young people in Spain, including satisfaction with the job role, self-esteem, and emotional and instrumental social support in the prediction model, all of which will be assessed by analyzing men and women separately. To test the hypotheses and determine the importance of the number of work-related events and changes, job satisfaction, self-esteem and social support in psychological distress, and life satisfaction amongst men and women, hierarchical multiple regression analyses were made. Model 3, with all the independent variables in the equation, predicted 28% In Table 1 are the correlation coefficients between the age, level of studies, number of work-related events and changes, job satisfaction, self-esteem and social support with the psychological distress, and life satisfaction amongst men and women. abstract: A relentless stream of social, technological, and economic changes have impacted the workplace, affecting young people in particular. Such changes can be a major source of stress and can cause a threat to health and well-being. The aim of this paper is to understand the importance of work-related events and changes in the psychological distress and life satisfaction of young workers in Spain. A transversal study was carried out on a sample comprising 509 men and 396 women aged between 26 and 35 years old. The results showed that there were no differences between the men and women in the number of work-related events and changes experienced in the last 12 months, nor in terms of job satisfaction. The results from the multiple regression analysis showed that a greater number of work-related events and changes experienced during the last 12 months were associated with increased psychological distress and reduced life satisfaction amongst men, but this was not the case for women. Although job satisfaction was independent from the men and women’s psychological distress when self-esteem and social support was included in the regression equation, greater job satisfaction was associated with greater life satisfaction for both men and women. It concludes that work-related events and job satisfaction are important for the health and well-being of young people, even though a larger number of work-related events and changes is associated with increased psychological distress and reduced life satisfaction for men only. url: https://www.ncbi.nlm.nih.gov/pubmed/32629853/ doi: 10.3390/ijerph17134697 id: cord-048461-397hp1yt author: Coelho, Flávio C title: Epigrass: a tool to study disease spread in complex networks date: 2008-02-26 words: 4006.0 sentences: 212.0 pages: flesch: 53.0 cache: ./cache/cord-048461-397hp1yt.txt txt: ./txt/cord-048461-397hp1yt.txt summary: BACKGROUND: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. RESULTS: A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. The Epigrass system is driven by a graphical user interface(GUI), which handles several input files required for model definition and manages the simulation and output generation (figure 2). To run a network epidemic model in Epigrass, the user is required to provide three separate text files (Optionally, also a shapefile with the map layer): abstract: BACKGROUND: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. Such data, which frequently resides on large geo-referenced databases, has to be processed and assigned to the various components of the model. All this just to construct the model, then it still has to be simulated and analyzed under different epidemiological scenarios. This workflow can only be achieved efficiently by computational tools that can automate most, if not all, these time-consuming tasks. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. RESULTS: A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. Results show that the topological context of the starting point of the epidemic is of great importance from both control and preventive perspectives. CONCLUSION: Epigrass is shown to facilitate greatly the construction, simulation and analysis of complex network models. The output of model results in standard GIS file formats facilitate the post-processing and analysis of results by means of sophisticated GIS software. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275240/ doi: 10.1186/1751-0473-3-3 id: cord-340564-3fu914lk author: Cohen, Joseph Paul title: Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning date: 2020-07-28 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model’s ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online. url: https://doi.org/10.7759/cureus.9448 doi: 10.7759/cureus.9448 id: cord-260966-9n23fjnz author: Comunian, Alessandro title: Inversion of a SIR-based model: A critical analysis about the application to COVID-19 epidemic date: 2020-08-12 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Calibration of a SIR (Susceptibles-Infected-Recovered) model with official international data for the COVID-19 pandemics provides a good example of the difficulties inherent the solution of inverse problems. Inverse modeling is set up in a framework of discrete inverse problems, which explicitly considers the role and the relevance of data. Together with a physical vision of the model, the present work addresses numerically the issue of parameters calibration in SIR models, it discusses the uncertainties in the data provided by international authorities, how they influence the reliability of calibrated model parameters and, ultimately, of model predictions. url: https://api.elsevier.com/content/article/pii/S0167278920303912 doi: 10.1016/j.physd.2020.132674 id: cord-258018-29vtxz89 author: Cooper, Ian title: A SIR model assumption for the spread of COVID-19 in different communities date: 2020-06-28 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by countries and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease. url: https://www.sciencedirect.com/science/article/pii/S0960077920304549?v=s5 doi: 10.1016/j.chaos.2020.110057 id: cord-266593-hmx2wy1p author: Cope, Robert C. title: Identification of the relative timing of infectiousness and symptom onset for outbreak control date: 2020-02-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Abstract In an outbreak of an emerging disease the epidemiological characteristics of the pathogen may be largely unknown. A key determinant of ability to control the outbreak is the relative timing of infectiousness and symptom onset. We provide a method for identifying this relationship with high accuracy based on data from simulated household-stratified symptom-onset data. Further, this can be achieved with observations taken on only a few specific days, chosen optimally, within each household. The information provided by this method may inform decision making processes for outbreak response. An accurate and computationally-efficient heuristic for determining the optimal surveillance scheme is introduced. This heuristic provides a novel approach to optimal design for Bayesian model discrimination. url: https://api.elsevier.com/content/article/pii/S0022519319304485 doi: 10.1016/j.jtbi.2019.110079 id: cord-240372-39yqeux4 author: Costa, Kleyton Vieira Sales da title: Forecasting Quarterly Brazilian GDP: Univariate Models Approach date: 2020-10-26 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models and the class of state-space models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; and the dynamic linear model, a state-space model. Based on statistical metrics of model comparison, the dynamic linear model presented the best forecasting model and fit performance for the analyzed period, also incorporating the growth rate structure significantly. url: https://arxiv.org/pdf/2010.13259v1.pdf doi: nan id: cord-176677-exej3zwh author: Coveney, Peter V. title: When we can trust computers (and when we can't) date: 2020-07-08 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering that are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long-term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. url: https://arxiv.org/pdf/2007.03741v1.pdf doi: nan id: cord-026384-ejk9wjr1 author: Crilly, Colin J. title: Predicting the outcomes of preterm neonates beyond the neonatal intensive care unit: What are we missing? date: 2020-05-19 words: 6059.0 sentences: 321.0 pages: flesch: 44.0 cache: ./cache/cord-026384-ejk9wjr1.txt txt: ./txt/cord-026384-ejk9wjr1.txt summary: Our review provides a comprehensive analysis and critique of risk prediction models developed for preterm neonates, specifically predicting functional outcomes instead of mortality, to reveal areas of improvement for future studies aiming to develop risk prediction tools for this population. 17 published a systematic review of risk factor models for neurodevelopmental outcomes in children born very preterm or very low birth weight (VLBW). In this article, we conduct an in-depth, narrative review of the current risk models available for predicting the functional outcomes of preterm neonates, evaluating their relative strengths and weaknesses in variable and outcome selection, and considering how risk model development and validation can be improved in the future. Risk factor models for neurodevelopmental outcomes in children born very preterm or with very low birth weight: a systematic review of methodology and reporting Is the CRIB score (Clinical Risk Index for babies) a valid tool in predicting neurodevelopmental outcome in extremely low birth weight infants? abstract: ABSTRACT: Preterm infants are a population at high risk for mortality and adverse health outcomes. With recent improvements in survival to childhood, increasing attention is being paid to risk of long-term morbidity, specifically during childhood and young-adulthood. Although numerous tools for predicting the functional outcomes of preterm neonates have been developed in the past three decades, no studies have provided a comprehensive overview of these tools, along with their strengths and weaknesses. The purpose of this article is to provide an in-depth, narrative review of the current risk models available for predicting the functional outcomes of preterm neonates. A total of 32 studies describing 43 separate models were considered. We found that most studies used similar physiologic variables and standard regression techniques to develop models that primarily predict the risk of poor neurodevelopmental outcomes. With a recently expanded knowledge regarding the many factors that affect neurodevelopment and other important outcomes, as well as a better understanding of the limitations of traditional analytic methods, we argue that there is great room for improvement in creating risk prediction tools for preterm neonates. We also consider the ethical implications of utilizing these tools for clinical decision-making. IMPACT: Based on a literature review of risk prediction models for preterm neonates predicting functional outcomes, future models should aim for more consistent outcomes definitions, standardized assessment schedules and measurement tools, and consideration of risk beyond physiologic antecedents. Our review provides a comprehensive analysis and critique of risk prediction models developed for preterm neonates, specifically predicting functional outcomes instead of mortality, to reveal areas of improvement for future studies aiming to develop risk prediction tools for this population. To our knowledge, this is the first literature review and narrative analysis of risk prediction models for preterm neonates regarding their functional outcomes. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276948/ doi: 10.1038/s41390-020-0968-5 id: cord-125330-jyppul4o author: Crokidakis, Nuno title: Modeling the evolution of drinking behavior: A Statistical Physics perspective date: 2020-08-24 words: 3629.0 sentences: 218.0 pages: flesch: 55.0 cache: ./cache/cord-125330-jyppul4o.txt txt: ./txt/cord-125330-jyppul4o.txt summary: The standard medical way of categorizing alcohol consumption [15] is in three groups -nonconsumers, moderate (or social) consumers and risk (or excessive) consumers; thus, modeling of the interactions and consequent changes of an individual from one group to another is governed by interaction parameters. This transition M → R can also occur spontaneously, with probability α, if a given agent increase his/her alcohol consumption -this is the only migration pathway from one group to another, in this model, that does not depend on the population of the receiving compartment, since it corresponds to a self-induced progression from Moderate (M) to Risk (R) drinking. The transitions among the compartments are ruled by probabilities, representing the social interactions among individuals, as well as spontaneous decisions, in particular from moderate evolving into risk drinkers, and we studied the model through analytical and numerical calculations. abstract: In this work we study a simple compartmental model for drinking behavior evolution. The population is divided in 3 compartments regarding their alcohol consumption, namely Susceptible individuals S (nonconsumers), Moderated drinkers M and Risk drinkers R. The transitions among those states are rules by probabilities. Despite the simplicity of the model, we observed the occurrence of two distinct nonequilibrium phase transitions to absorbing states. One of these states is composed only by Susceptible individuals S, with no drinkers ($M=R=0$). On the other hand, the other absorbing state is composed only by Risk drinkers R ($S=M=0$). Between these two steady states, we have the coexistence of the three subpopulations S, M and R. Comparison with abusive alcohol consumption data for Brazil shows a good agreement between the model's results and the database. url: https://arxiv.org/pdf/2008.10692v1.pdf doi: nan id: cord-174692-ljph6cao author: Dadlani, Aresh title: Deterministic Models in Epidemiology: From Modeling to Implementation date: 2020-04-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The abrupt outbreak and transmission of biological diseases has always been a long-time concern of humankind. For long, mathematical modeling has served as a simple and yet efficient tool to investigate, predict, and control spread of communicable diseases through individuals. A myriad of works on epidemic models and their variants have been reported in the literature. For better prediction of the dynamics of a particular disease, it is important to adopt the most suitable model. In this paper, we study some of the widely-appreciated deterministic epidemic models in which the population is divided into compartments based on the health status of each individual. In particular, we provide a demographic classification of such models and study each of them in terms of mathematical formulation, near equilibrium point stability properties, and disease outbreak threshold conditions (basic reproduction ratio). Furthermore, we discuss the various influential factors that need to be considered during epidemic modeling. The main objective of this article is to provide a basic understanding of the mathematical complexity incurred in deterministic epidemic models with the aid of graphical illustrations obtained through implementation. url: https://arxiv.org/pdf/2004.04675v1.pdf doi: nan id: cord-273429-dl6z8x9h author: Dandekar, R. title: A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread date: 2020-07-24 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. Finally, we have hosted our quarantine diagnosis results for the top $70$ affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike. url: https://doi.org/10.1101/2020.07.23.20160697 doi: 10.1101/2020.07.23.20160697 id: cord-285774-hvuzxlna author: Danion, J. title: Bariatric Surgical Simulation: Evaluation in a Pilot Study of SimLife, a New Dynamic Simulated Body Model date: 2020-07-03 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: BACKGROUND: The demand for bariatric surgery is high and so is the need for training future bariatric surgeons. Bariatric surgery, as a technically demanding surgery, imposes a learning curve that may initially induce higher morbidity. In order to limit the clinical impact of this learning curve, a simulation preclinical training can be offered. The aim of the work was to assess the realism of a new cadaveric model for simulated bariatric surgery (sleeve and Roux in Y gastric bypass). AIM: A face validation study of SimLife, a new dynamic cadaveric model of simulated body for acquiring operative skills by simulation. The objectives of this study are first of all to measure the realism of this model, the satisfaction of learners, and finally the ability of this model to facilitate a learning process. METHODS: SimLife technology is based on a fresh body (frozen/thawed) given to science associated to a patented technical module, which can provide pulsatile vascularization with simulated blood heated to 37 °C and ventilation. RESULTS: Twenty-four residents and chief residents from 3 French University Digestive Surgery Departments were enrolled in this study. Based on their evaluation, the overall satisfaction of the cadaveric model was rated as 8.52, realism as 8.91, anatomic correspondence as 8.64, and the model’s ability to be learning tool as 8.78. CONCLUSION: The use of the SimLife model allows proposing a very realistic surgical simulation model to realistically train and objectively evaluate the performance of young surgeons. url: https://doi.org/10.1007/s11695-020-04829-1 doi: 10.1007/s11695-020-04829-1 id: cord-339374-2hxnez28 author: De Kort, Hanne title: Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors date: 2020-09-22 words: 9182.0 sentences: 388.0 pages: flesch: 31.0 cache: ./cache/cord-339374-2hxnez28.txt txt: ./txt/cord-339374-2hxnez28.txt summary: The overall SDM framework is not just an interesting tool for identifying areas of local conservation concern or areas not yet occupied but potentially suitable; it has the potential to contribute substantially to the global protection of biodiversity and ecosystem services threatened by multiple environmental stressors, including land-use change and habitat fragmentation, climate change, invasive alien species, pollution, and overexploitation (Franklin, 2013; Kok et al., 2017; Wiens, Stralberg, Jongsomjit, Howell, & Snyder, 2009 ). Patches occupied by larger butterflies (representing better dispersers) are predicted to be accessible due to dispersal evolution (after DeKort, Prunier, et al., 2018) boost opens the door for comparing and synthesizing published SDM studies for answering taxon-wide and large-scale research questions, including the role of species'' traits and evolutionary potential in driving general species distribution shifts in response to land-use change. abstract: Global biodiversity declines, largely driven by climate and land‐use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra‐ and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land‐use change interact to govern population dynamics and species’ distributions, depending on species’ dispersal and evolutionary abilities. We particularly emphasize that both land‐use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans. url: https://doi.org/10.1002/ece3.6753 doi: 10.1002/ece3.6753 id: cord-142398-glq4mjau author: Dhar, Abhishek title: A critique of the Covid-19 analysis for India by Singh and Adhikari date: 2020-04-11 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In a recent paper (arXiv:2003.12055), Singh and Adhikari present results of an analysis of a mathematical model of epidemiology based on which they argue that a $49$ day lockdown is required in India for containing the pandemic in India. We assert that as a model study with the stated assumptions, the analysis presented in the paper is perfectly valid, however, any serious comparison with real data and attempts at prediction from the model are highly problematic. The main point of the present paper is to convincingly establish this assertion while providing a warning that the results and analysis of such mathematical models should not be taken at face value and need to be used with great caution by policy makers. url: https://arxiv.org/pdf/2004.05373v1.pdf doi: nan id: cord-126012-h7er0prc author: Diaz, Victor Hugo Grisales title: COVID-19: Forecasting mortality given mobility trend data and non-pharmaceutical interventions date: 2020-09-25 words: 3165.0 sentences: 156.0 pages: flesch: 52.0 cache: ./cache/cord-126012-h7er0prc.txt txt: ./txt/cord-126012-h7er0prc.txt summary: We develop a novel hybrid epidemiological model and a specific methodology for its calibration to distinguish and assess the impact of mobility restrictions (given by Apple''s mobility trends data) from other complementary non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19. Using the calibrated model, we estimate that mobility restrictions contribute to 47 % (US States) and 47 % (worldwide) of the overall suppression of the disease transmission rate using data up to 13/08/2020. At the same time, we evaluate the effectiveness of restrictions on mobility (i.e., walking, driving and transport) on the reduction of the disease transmission rate and hence the control of the cumulative number of infected and deceased individuals. In this contribution, our previous model [5] is extended to predict mortality and to include a term to estimate the reduction on the contagious rates given reported mobility data. abstract: We develop a novel hybrid epidemiological model and a specific methodology for its calibration to distinguish and assess the impact of mobility restrictions (given by Apple's mobility trends data) from other complementary non-pharmaceutical interventions (NPIs) used to control the spread of COVID-19. Using the calibrated model, we estimate that mobility restrictions contribute to 47 % (US States) and 47 % (worldwide) of the overall suppression of the disease transmission rate using data up to 13/08/2020. The forecast capacity of our model was evaluated doing four-weeks ahead predictions. Using data up to 30/06/20 for calibration, the mean absolute percentage error (MAPE) of the prediction of cumulative deceased individuals was 5.0 % for the United States (51 states) and 6.7 % worldwide (49 countries). This MAPE was reduced to 3.5% for the US and 3.8% worldwide using data up to 13/08/2020. We find that the MAPE was higher for the total confirmed cases at 11.5% worldwide and 10.2% for the US States using data up to 13/08/2020. Our calibrated model achieves an average R-Squared value for cumulative confirmed and deceased cases of 0.992 using data up to 30/06/20 and 0.98 using data up to 13/08/20. url: https://arxiv.org/pdf/2009.12171v2.pdf doi: nan id: cord-309010-tmfm5u5h author: Dietert, Kristina title: Spectrum of pathogen- and model-specific histopathologies in mouse models of acute pneumonia date: 2017-11-20 words: 7842.0 sentences: 414.0 pages: flesch: 34.0 cache: ./cache/cord-309010-tmfm5u5h.txt txt: ./txt/cord-309010-tmfm5u5h.txt summary: Here, we systematically describe and compare the distinctive histopathological features of established models of acute pneumonia in mice induced by Streptococcus (S.) pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Legionella pneumophila, Escherichia coli, Middle East respiratory syndrome (MERS) coronavirus, influenza A virus (IAV) and superinfection of IAV-incuced pneumonia with S. Systematic comparisons of the models revealed striking differences in the distribution of lesions, the characteristics of pneumonia induced, principal inflammatory cell types, lesions in adjacent tissues, and the detectability of the pathogens in histological sections. Transnasal infection with MERS-CoV following adenoviral transduction of human DPP4 yielded an expansive, (Fig 7A) interstitial pneumonia with severe alveolar epithelial cell necrosis and infiltration of mainly macrophages, lymphocytes, and fewer neutrophils (Fig 7B) . Different mouse models of acute pneumonia differ widely, with an obvious strong dependence on pathogen-specific features of virulence and spread, route of infection, infectious dose and other factors. abstract: Pneumonia may be caused by a wide range of pathogens and is considered the most common infectious cause of death in humans. Murine acute lung infection models mirror human pathologies in many aspects and contribute to our understanding of the disease and the development of novel treatment strategies. Despite progress in other fields of tissue imaging, histopathology remains the most conclusive and practical read out tool for the descriptive and semiquantitative evaluation of mouse pneumonia and therapeutic interventions. Here, we systematically describe and compare the distinctive histopathological features of established models of acute pneumonia in mice induced by Streptococcus (S.) pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Legionella pneumophila, Escherichia coli, Middle East respiratory syndrome (MERS) coronavirus, influenza A virus (IAV) and superinfection of IAV-incuced pneumonia with S. pneumoniae. Systematic comparisons of the models revealed striking differences in the distribution of lesions, the characteristics of pneumonia induced, principal inflammatory cell types, lesions in adjacent tissues, and the detectability of the pathogens in histological sections. We therefore identified core criteria for each model suitable for practical semiquantitative scoring systems that take into account the pathogen- and model-specific patterns of pneumonia. Other critical factors that affect experimental pathologies are discussed, including infectious dose, time kinetics, and the genetic background of the mouse strain. The substantial differences between the model-specific pathologies underscore the necessity of pathogen- and model-adapted criteria for the comparative quantification of experimental outcomes. These criteria also allow for the standardized validation and comparison of treatment strategies in preclinical models. url: https://doi.org/10.1371/journal.pone.0188251 doi: 10.1371/journal.pone.0188251 id: cord-266189-b3b36d72 author: Dignum, Frank title: Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation date: 2020-06-15 words: 7608.0 sentences: 416.0 pages: flesch: 63.0 cache: ./cache/cord-266189-b3b36d72.txt txt: ./txt/cord-266189-b3b36d72.txt summary: In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions. Based on data from previous pandemics, initial economic policies were based on the expectation of getting back to normal within a limited amount of time, with many governments soldering the costs for the current period, it is increasingly clear that impact may be way above what governments can cope with, and a new ''normal'' economy will need to be found (Bénassy-Quéré et al. In this section, we describe the epidemics, economics and social science models that are needed to support decision makers on policies concerning the COVID-19 crisis and the complexity of combining these models. We model the direct and indirect effect on the spread of the virus when schools are closed and people work from home. abstract: During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions. url: https://doi.org/10.1007/s11023-020-09527-6 doi: 10.1007/s11023-020-09527-6 id: cord-130967-cvbpgvso author: Dinamarca, Jos''e Luis title: Clinical concepts might be included in health-related mathematic models date: 2020-04-23 words: 1499.0 sentences: 77.0 pages: flesch: 63.0 cache: ./cache/cord-130967-cvbpgvso.txt txt: ./txt/cord-130967-cvbpgvso.txt summary: It would be ideal to correct and refine the referred model, bearing in mind clinical concepts described, to take advantage of the proposal and generate a more accurate response, which can serve as an input both in the implementation of measures and in the prediction of the behavior of a pandemic like the current one. The construc on of mathema cal models that allow comprehensive approach of decision-making in situa ons of absence of robust evidence is important. This, because the authors minimize the impact of three relevant situa ons that are substan al issue of the integral process: First, in rela on to a contextual jus fica on, they use the assump on that different country-reali es are comparable. It would be ideal to correct and refine the presented model, bearing in mind the concepts described, to take advantage of the proposal and generate a more accurate response, which can serve as an input both in the implementa on of measures and in the predic on of the behavior of a pandemic like the current one. abstract: The construction of mathematical models that allow comprehensive approach of decision-making in situations of absence of robust evidence is important. While it is interesting to use models that are easy to understand, using values of direct interpretation, we analized a published index (COVID-19 Burden Index) and found it seems to be oversimplified. It is possible that the proposed index, with current data, could be useful in geographically and administratively narrowed places. But it is inaccurate to be applied throughout the process and in places as broad as American countries. It would be ideal to correct and refine the referred model, bearing in mind clinical concepts described, to take advantage of the proposal and generate a more accurate response, which can serve as an input both in the implementation of measures and in the prediction of the behavior of a pandemic like the current one. However, what we propose is to improve the accuracy of the model in terms of quantities and applicability, agreeing with the concept of"stay at home". The approach between complementary areas of knowledge should be the door that we must open to generate the new evidence we need. Mathematics should not dispense clinical sciences. url: https://arxiv.org/pdf/2004.13555v2.pdf doi: nan id: cord-326908-l9wrrapv author: Duchêne, David A. title: Evaluating the Adequacy of Molecular Clock Models Using Posterior Predictive Simulations date: 2015-07-10 words: 7596.0 sentences: 370.0 pages: flesch: 47.0 cache: ./cache/cord-326908-l9wrrapv.txt txt: ./txt/cord-326908-l9wrrapv.txt summary: We test the power of this approach using simulated data and find that the method is sensitive to bias in the estimates of branch lengths, which tends to occur when using underparameterized clock models. 2001) ; uncorrelated beta-distributed rate variation among lineages; misleading node-age priors (i.e., node calibrations that differ considerably from the true node ages); and when data were generated under a strict clock but analyzed with an underparameterized substitution model ( fig. The substitution model was identified as inadequate for the coronavirus data set by the multinomial test statistic estimated using posterior predictive data sets from a clock analysis (P < 0.05); however, it was identified as adequate when using a clock-free method (P = 0.20). In addition, our metric of uncertainty in posterior predictive branch lengths is sensitive to some cases of misspecification of clock models and node-age priors, but not to substitution model misspecification, as shown for our analyses of the coronavirus data set. abstract: Molecular clock models are commonly used to estimate evolutionary rates and timescales from nucleotide sequences. The goal of these models is to account for rate variation among lineages, such that they are assumed to be adequate descriptions of the processes that generated the data. A common approach for selecting a clock model for a data set of interest is to examine a set of candidates and to select the model that provides the best statistical fit. However, this can lead to unreliable estimates if all the candidate models are actually inadequate. For this reason, a method of evaluating absolute model performance is critical. We describe a method that uses posterior predictive simulations to assess the adequacy of clock models. We test the power of this approach using simulated data and find that the method is sensitive to bias in the estimates of branch lengths, which tends to occur when using underparameterized clock models. We also compare the performance of the multinomial test statistic, originally developed to assess the adequacy of substitution models, but find that it has low power in identifying the adequacy of clock models. We illustrate the performance of our method using empirical data sets from coronaviruses, simian immunodeficiency virus, killer whales, and marine turtles. Our results indicate that methods of investigating model adequacy, including the one proposed here, should be routinely used in combination with traditional model selection in evolutionary studies. This will reveal whether a broader range of clock models to be considered in phylogenetic analysis. url: https://www.ncbi.nlm.nih.gov/pubmed/26163668/ doi: 10.1093/molbev/msv154 id: cord-007147-0v8ltunv author: Dungan, R. S. title: BOARD-INVITED REVIEW: Fate and transport of bioaerosols associated with livestock operations and manures date: 2010-11-17 words: 8223.0 sentences: 399.0 pages: flesch: 39.0 cache: ./cache/cord-007147-0v8ltunv.txt txt: ./txt/cord-007147-0v8ltunv.txt summary: Although most studies at animal operations and wastewater spray irrigation sites suggest a decreased risk of bioaerosol exposure with increasing distance from the source, many challenges remain in evaluating the health effects of aerosolized pathogens and allergens in outdoor environments. An area of growing interest is airborne pathogens and microbial by-products generated at AFO and during the land application of manures (Chang et al., 2001b; Wilson et al., 2002; Cole et al., 2008; Chinivasagam et al., 2009; Dungan and Leytem, 2009a; Millner, 2009) , which can potentially affect the health of livestock, farm workers, and individuals in nearby residences (Heederik et al., 2007) . With most bioaerosol studies, whether conducted at AFO, composting facilities, wastewater treatment plants, biosolids application sites, or wastewater spray irrigations sites, the general trend observed is that the airborne microorganism concentrations decrease with distance from the source (Goff et al., 1973; Katzenelson and Teltch, 1976; Boutin et al., 1988; Taha et al., 2005; Green et al., 2006; Low et al., 2007) . abstract: Airborne microorganisms and microbial by-products from intensive livestock and manure management systems are a potential health risk to workers and individuals in nearby communities. This report presents information on zoonotic pathogens in animal wastes and the generation, fate, and transport of bioaerosols associated with animal feeding operations and land applied manures. Though many bioaerosol studies have been conducted at animal production facilities, few have investigated the transport of bioaerosols during the land application of animal manures. As communities in rural areas converge with land application sites, concerns over bioaerosol exposure will certainly increase. Although most studies at animal operations and wastewater spray irrigation sites suggest a decreased risk of bioaerosol exposure with increasing distance from the source, many challenges remain in evaluating the health effects of aerosolized pathogens and allergens in outdoor environments. To improve our ability to understand the off-site transport and diffusion of human and livestock diseases, various dispersion models have been utilized. Most studies investigating the transport of bioaerosols during land application events have used a modified Gaussian plume model. Because of the disparity among collection and analytical techniques utilized in outdoor studies, it is often difficult to evaluate health effects associated with aerosolized pathogens and allergens. Invaluable improvements in assessing the health effects from intensive livestock practices could be made if standardized bioaerosol collection and analytical techniques, as well as the use of specific target microorganisms, were adopted. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109640/ doi: 10.2527/jas.2010-3094 id: cord-033010-o5kiadfm author: Durojaye, Olanrewaju Ayodeji title: Potential therapeutic target identification in the novel 2019 coronavirus: insight from homology modeling and blind docking study date: 2020-10-02 words: 8125.0 sentences: 375.0 pages: flesch: 53.0 cache: ./cache/cord-033010-o5kiadfm.txt txt: ./txt/cord-033010-o5kiadfm.txt summary: RESULTS: This study describes the detailed computational process by which the 2019-nCoV main proteinase coding sequence was mapped out from the viral full genome, translated and the resultant amino acid sequence used in modeling the protein 3D structure. Our current study took advantage of the availability of the SARS CoV main proteinase amino acid sequence to map out the nucleotide coding region for the same protein in the 2019-nCoV. The predicted secondary structure composition shows a high degree of alpha helix and beta sheets, respectively, occupying 45 and 47% of the total residues with the percentage loop occupancy at 8% regarded as comparative modeling, constructs atomic models based on known structures or structures that have been determined experimentally and likewise share more than 40% sequence homology. abstract: BACKGROUND: The 2019-nCoV which is regarded as a novel coronavirus is a positive-sense single-stranded RNA virus. It is infectious to humans and is the cause of the ongoing coronavirus outbreak which has elicited an emergency in public health and a call for immediate international concern has been linked to it. The coronavirus main proteinase which is also known as the 3C-like protease (3CLpro) is a very important protein in all coronaviruses for the role it plays in the replication of the virus and the proteolytic processing of the viral polyproteins. The resultant cytotoxic effect which is a product of consistent viral replication and proteolytic processing of polyproteins can be greatly reduced through the inhibition of the viral main proteinase activities. This makes the 3C-like protease of the coronavirus a potential and promising target for therapeutic agents against the viral infection. RESULTS: This study describes the detailed computational process by which the 2019-nCoV main proteinase coding sequence was mapped out from the viral full genome, translated and the resultant amino acid sequence used in modeling the protein 3D structure. Comparative physiochemical studies were carried out on the resultant target protein and its template while selected HIV protease inhibitors were docked against the protein binding sites which contained no co-crystallized ligand. CONCLUSION: In line with results from this study which has shown great consistency with other scientific findings on coronaviruses, we recommend the administration of the selected HIV protease inhibitors as first-line therapeutic agents for the treatment of the current coronavirus epidemic. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529470/ doi: 10.1186/s43042-020-00081-5 id: cord-315685-ute3dxwu author: Ehaideb, Salleh N. title: Evidence of a wide gap between COVID-19 in humans and animal models: a systematic review date: 2020-10-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: BACKGROUND: Animal models of COVID-19 have been rapidly reported after the start of the pandemic. We aimed to assess whether the newly created models reproduce the full spectrum of human COVID-19. METHODS: We searched the MEDLINE, as well as BioRxiv and MedRxiv preprint servers for original research published in English from January 1 to May 20, 2020. We used the search terms (COVID-19) OR (SARS-CoV-2) AND (animal models), (hamsters), (nonhuman primates), (macaques), (rodent), (mice), (rats), (ferrets), (rabbits), (cats), and (dogs). Inclusion criteria were the establishment of animal models of COVID-19 as an endpoint. Other inclusion criteria were assessment of prophylaxis, therapies, or vaccines, using animal models of COVID-19. RESULT: Thirteen peer-reviewed studies and 14 preprints met the inclusion criteria. The animals used were nonhuman primates (n = 13), mice (n = 7), ferrets (n = 4), hamsters (n = 4), and cats (n = 1). All animals supported high viral replication in the upper and lower respiratory tract associated with mild clinical manifestations, lung pathology, and full recovery. Older animals displayed relatively more severe illness than the younger ones. No animal models developed hypoxemic respiratory failure, multiple organ dysfunction, culminating in death. All species elicited a specific IgG antibodies response to the spike proteins, which were protective against a second exposure. Transient systemic inflammation was observed occasionally in nonhuman primates, hamsters, and mice. Notably, none of the animals unveiled a cytokine storm or coagulopathy. CONCLUSIONS: Most of the animal models of COVID-19 recapitulated mild pattern of human COVID-19 with full recovery phenotype. No severe illness associated with mortality was observed, suggesting a wide gap between COVID-19 in humans and animal models. url: https://www.ncbi.nlm.nih.gov/pubmed/33023604/ doi: 10.1186/s13054-020-03304-8 id: cord-252166-qah877pk author: Ekins, S title: In silico pharmacology for drug discovery: applications to targets and beyond date: 2007-09-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets. url: https://www.ncbi.nlm.nih.gov/pubmed/17549046/ doi: 10.1038/sj.bjp.0707306 id: cord-308219-97gor71p author: Elzeiny, Sami title: Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images date: 2020-09-17 words: 5697.0 sentences: 312.0 pages: flesch: 52.0 cache: ./cache/cord-308219-97gor71p.txt txt: ./txt/cord-308219-97gor71p.txt summary: By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models'' accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models. In this study, a new stress classification approach is proposed to classify the individual stress state into stressed or non-stressed by converting spatial images of inter-beat intervals of a PPG signal to frequency domain images and we use these pictures to train several CNN models. abstract: Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models. url: https://www.ncbi.nlm.nih.gov/pubmed/32957479/ doi: 10.3390/s20185312 id: cord-292699-855am0mv author: Engbert, Ralf title: Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics date: 2020-04-17 words: 5369.0 sentences: 363.0 pages: flesch: 54.0 cache: ./cache/cord-292699-855am0mv.txt txt: ./txt/cord-292699-855am0mv.txt summary: The key motivation of the current study was to apply sequential data assimilation of the stochastic SEIR model to estimate the contact parameter. An approximative instantaneous negative log-likelihood L(t k , β) of the contact parameter β at observation time t k is obtained from the ensemble Kalman filter (see Model inference based on sequential data assimilation). Forward iteration with the estimated time-varying contact parameter show that the slope of the epidemic curve is approximately reproduced by the model (Fig. 3a ,c; grey lines indicate the ensemble of simulated trajectories; blue points are observed data). In scenario I, we started with the adapted ensemble of internal model states after data assimilation (April 4th) and iterated the model forward with the mean contact parameter estimated in the week March 29th to April 4th after implementation of interventions (Fig. 4 , green area). abstract: Newly emerging pandemics like COVID-19 call for better predictive models to implement early and precisely tuned responses to their deep impact on society. Standard epidemic models provide a theoretically well-founded description of dynamics of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which challenges containment strategies, in particular, due to delayed epidemic responses to non-pharmaceutical interventions. However, spatial heterogeneity questions the adequacy of modeling epidemic outbreaks on the level of a whole country. Here we show that sequential data assimilation of a stochastic version of the standard SEIR epidemic model captures dynamical behavior of outbreaks on the regional level. Such regional modeling of epidemics with relatively low numbers of infected and realistic demographic noise accounts for both spatial heterogeneity and stochasticity. Based on adapted regional models, population level short-term predictions can be achieved. More realistic epidemic models that include spatial heterogeneity are within reach via sequential data assimilation methods. url: https://doi.org/10.1101/2020.04.13.20063768 doi: 10.1101/2020.04.13.20063768 id: cord-316393-ozl28ztz author: Enrique Amaro, José title: Global analysis of the COVID-19 pandemic using simple epidemiological models date: 2020-10-22 words: 5293.0 sentences: 294.0 pages: flesch: 58.0 cache: ./cache/cord-316393-ozl28ztz.txt txt: ./txt/cord-316393-ozl28ztz.txt summary: The Death or ''D'' model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations – also developed in this work – predict similar trends and suggest a common pandemic evolution with universal parameters. Additionally, D-model calculations are benchmarked with more sophisticated and reliable calculations using the extended SIR (ESIR) and Monte Carlo Planck (MCP) models -also developed in this work -which provide similar results, but allow for a more coherent spatial-time disentanglement of the various effects present during a pandemic. abstract: Several analytical models have been developed in this work to describe the evolution of fatalities arising from coronavirus COVID-19 worldwide. The Death or ‘D’ model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations – also developed in this work – predict similar trends and suggest a common pandemic evolution with universal parameters. The evolution of the COVID-19 pandemic in several countries shows the typical behavior in concord with our model trends, characterized by a rapid increase of death cases followed by a slow decline, typically asymmetric with respect to the pandemic peak. The fact that the D and ESIR models predict similar results – without and with recovery, respectively – indicates that COVID-19 is a highly contagious virus, but that most people become asymptomatic (D model) and eventually recover (ESIR model). url: https://api.elsevier.com/content/article/pii/S0307904X20306028 doi: 10.1016/j.apm.2020.10.019 id: cord-274732-mh0xixzh author: Faizal, W.M. title: Computational fluid dynamics modelling of human upper airway: a review date: 2020-06-26 words: 8820.0 sentences: 499.0 pages: flesch: 44.0 cache: ./cache/cord-274732-mh0xixzh.txt txt: ./txt/cord-274732-mh0xixzh.txt summary: RESULTS: This review found that the human upper airway was well studied through the application of computational fluid dynamics, which had considerably enhanced the understanding of flow in HUA. However, to predict the flow accurately in the study of the upper airway, the selected numerical method must have the capability to simulate the low-Reynolds number turbulence model in a complex geometry [51] . This article presents review on the experimental and numerical method such as, computational fluid dynamics approach, and its application in the analysis of human upper airway (HUA), including the fluid-structure interaction. Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow Fluid structure interaction simulations of the upper airway in obstructive sleep apnea patients before and after maxillomandibular advancement surgery abstract: BACKGROUND AND OBJECTIVE: Human upper airway (HUA) has been widely investigated by many researchers covering various aspects, such as the effects of geometrical parameters on the pressure, velocity and airflow characteristics. Clinically significant obstruction can develop anywhere throughout the upper airway, leading to asphyxia and death; this is where recognition and treatment are essential and lifesaving. The availability of advanced computer, either hardware or software, and rapid development in numerical method have encouraged researchers to simulate the airflow characteristics and properties of HUA by using various patient conditions at different ranges of geometry and operating conditions. Computational fluid dynamics (CFD) has emerged as an efficient alternative tool to understand the airflow of HUA and in preparing patients to undergo surgery. The main objective of this article is to review the literature that deals with the CFD approach and modeling in analyzing HUA. METHODS: This review article discusses the experimental and computational methods in the study of HUA. The discussion includes computational fluid dynamics approach and steps involved in the modeling used to investigate the flow characteristics of HUA. From inception to May 2020, databases of PubMed, Embase, Scopus, the Cochrane Library, BioMed Central, and Web of Science have been utilized to conduct a thorough investigation of the literature. There had been no language restrictions in publication and study design of the database searches. A total of 117 articles relevant to the topic under investigation were thoroughly and critically reviewed to give a clear information about the subject. The article summarizes the review in the form of method of studying the HUA, CFD approach in HUA, and the application of CFD for predicting HUA obstacle, including the type of CFD commercial software are used in this research area. RESULTS: This review found that the human upper airway was well studied through the application of computational fluid dynamics, which had considerably enhanced the understanding of flow in HUA. In addition, it assisted in making strategic and reasonable decision regarding the adoption of treatment methods in clinical settings. The literature suggests that most studies were related to HUA simulation that considerably focused on the aspects of fluid dynamics. However, there is a literature gap in obtaining information on the effects of fluid-structure interaction (FSI). The application of FSI in HUA is still limited in the literature; as such, this could be a potential area for future researchers. Furthermore, majority of researchers present the findings of their work through the mechanism of airflow, such as that of velocity, pressure, and shear stress. This includes the use of Navier–Stokes equation via CFD to help visualize the actual mechanism of the airflow. The above-mentioned technique expresses the turbulent kinetic energy (TKE) in its result to demonstrate the real mechanism of the airflow. Apart from that, key result such as wall shear stress (WSS) can be revealed via turbulent kinetic energy (TKE) and turbulent energy dissipation (TED), where it can be suggestive of wall injury and collapsibility tissue to the HUA. url: https://doi.org/10.1016/j.cmpb.2020.105627 doi: 10.1016/j.cmpb.2020.105627 id: cord-344252-6g3zzj0o author: Farooq, Junaid title: A Novel Adaptive Deep Learning Model of Covid-19 with focus on mortality reduction strategies date: 2020-07-21 words: 6951.0 sentences: 361.0 pages: flesch: 56.0 cache: ./cache/cord-344252-6g3zzj0o.txt txt: ./txt/cord-344252-6g3zzj0o.txt summary: We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. In this work, we employ deep learning to propose an Artificial Neural Network (ANN) based real-time online incremental learning technique to estimate parameters of a data stream guided analytical model of Covid-19 to study the transmission dynamics and prevention mechanism for SARS-Cov-2 novel coronavirus in order to aid in optimal policy formulation, efficient decision making, forecasting and simulation. To the best of our knowledge, this paper develops for the first time a deep learning model of epidemic diseases with data science approach in which parameters are intelligently adapted to the new ground realities with fast evolving infection dynamics. abstract: We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems poses an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive model eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent spread of infection and thus most of the deaths occurring in this group are avoided. url: https://doi.org/10.1016/j.chaos.2020.110148 doi: 10.1016/j.chaos.2020.110148 id: cord-311868-40bri19f author: Fattahi, A. title: A systemic approach to analyze integrated energy system modeling tools: A review of national models date: 2020-11-30 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We reviewed the literature focusing on nineteen integrated Energy System Models (ESMs) to: (i) identify the capabilities and shortcomings of current ESMs to analyze adequately the transition towards a low-carbon energy system; (ii) assess the performance of the selected models by means of the derived criteria, and (iii) discuss some potential solutions to address the ESM gaps. This paper delivers three main outcomes. First, we identify key criteria for analyzing current ESMs and we describe seven current and future low-carbon energy system modeling challenges: the increasing need for flexibility, further electrification, emergence of new technologies, technological learning and efficiency improvements, decentralization, macroeconomic interactions, and the role of social behavior in the energy system transition. These criteria are then translated into required modeling capabilities such as the need for hourly temporal resolution, sectoral coupling technologies (e.g., P2X), technological learning, flexibility technologies, stakeholder behavior, cross border trade, and linking with macroeconomic models. Second, a Multi-Criteria Analysis (MCA) is used as a framework to identify modeling gaps while clarifying high modeling capabilities of MARKAL, TIMES, REMix, PRIMES, and METIS. Third, to bridge major energy modeling gaps, two conceptual modeling suites are suggested, based on both optimization and simulation methodologies, in which the integrated ESM is hard-linked with a regional model and an energy market model and soft-linked with a macroeconomic model. url: https://www.sciencedirect.com/science/article/pii/S1364032120304858 doi: 10.1016/j.rser.2020.110195 id: cord-232238-aicird98 author: Ferrario, Andrea title: A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations date: 2020-10-09 words: 6857.0 sentences: 329.0 pages: flesch: 39.0 cache: ./cache/cord-232238-aicird98.txt txt: ./txt/cord-232238-aicird98.txt summary: However, at the basis of any discussion on post-hoc explanations lies the assumption that the machine learning model whose outcomes have to be explained remains "stable" or does not change, in a given time frame of interest [2, 9, 19] . This time delay may lead to the emergence of unfavorable cases-called "unfortunate counterfactual events" (UCE) in these notes-where the retraining of the machine learning model invalidates the efforts of an individual who successfully implemented the scenario originally recommended by a feasible, actionable and possibly sparse counterfactual explanation. As noted in Section 3, the degree of certainty of counterfactual scenarios is computed as result of the machine learning model retraining, i.e., only after the generation of the corresponding counterfactual explanation (at time 0 ). In Table 1 we enumerate all possible cases that emerge from the change in time of data points, machine learning models and their outcomes, when considering the implementation of counterfactual scenarios. abstract: Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence research domain. They provide individuals with alternative scenarios and a set of recommendations to achieve a sought-after machine learning model outcome. Recently, the literature has identified desiderata of counterfactual explanations, such as feasibility, actionability and sparsity that should support their applicability in real-world contexts. However, we show that the literature has neglected the problem of the time dependency of counterfactual explanations. We argue that, due to their time dependency and because of the provision of recommendations, even feasible, actionable and sparse counterfactual explanations may not be appropriate in real-world applications. This is due to the possible emergence of what we call"unfortunate counterfactual events."These events may occur due to the retraining of machine learning models whose outcomes have to be explained via counterfactual explanation. Series of unfortunate counterfactual events frustrate the efforts of those individuals who successfully implemented the recommendations of counterfactual explanations. This negatively affects people's trust in the ability of institutions to provide machine learning-supported decisions consistently. We introduce an approach to address the problem of the emergence of unfortunate counterfactual events that makes use of histories of counterfactual explanations. In the final part of the paper we propose an ethical analysis of two distinct strategies to cope with the challenge of unfortunate counterfactual events. We show that they respond to an ethically responsible imperative to preserve the trustworthiness of credit lending organizations, the decision models they employ, and the social-economic function of credit lending. url: https://arxiv.org/pdf/2010.04687v1.pdf doi: nan id: cord-264136-jjtsd4n3 author: Ferstad, Johannes Opsahl title: A model to forecast regional demand for COVID-19 related hospital beds date: 2020-03-30 words: 2758.0 sentences: 147.0 pages: flesch: 49.0 cache: ./cache/cord-264136-jjtsd4n3.txt txt: ./txt/cord-264136-jjtsd4n3.txt summary: [6, 7] In order to plan their response, hospital and public health officials need to understand how many people in their area are likely to require hospitalization for COVID-19; how these numbers compare to the number of available intensive care and acute care beds; and how to project the impact of socialdistancing measures on utilization. To facilitate use by hospital and public health officials, the model is deployed through an interactive online website that allows users to generate dynamic, static, and spatial estimates of the number and rate of severe, critical, and mortality case rates for each county or group of counties. In this report, we describe an online, real-time, interactive simulation model to facilitate local policy making and regional coordination by providing estimates of hospital bed demand and the impact of measures to slow the spread of the infection. abstract: COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19. Link to online model: https://surf.stanford.edu/covid-19-tools/covid-19/ url: https://doi.org/10.1101/2020.03.26.20044842 doi: 10.1101/2020.03.26.20044842 id: cord-028420-z8sv9f5k author: Filighera, Anna title: Fooling Automatic Short Answer Grading Systems date: 2020-06-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: With the rising success of adversarial attacks on many NLP tasks, systems which actually operate in an adversarial scenario need to be reevaluated. For this purpose, we pose the following research question: How difficult is it to fool automatic short answer grading systems? In particular, we investigate the robustness of the state of the art automatic short answer grading system proposed by Sung et al. towards cheating in the form of universal adversarial trigger employment. These are short token sequences that can be prepended to students’ answers in an exam to artificially improve their automatically assigned grade. Such triggers are especially critical as they can easily be used by anyone once they are found. In our experiments, we discovered triggers which allow students to pass exams with passing thresholds of [Formula: see text] without answering a single question correctly. Furthermore, we show that such triggers generalize across models and datasets in this scenario, nullifying the defense strategy of keeping grading models or data secret. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334174/ doi: 10.1007/978-3-030-52237-7_15 id: cord-264994-j8iawzp8 author: Fitzpatrick, Meagan C. title: Modelling microbial infection to address global health challenges date: 2019-09-20 words: 7105.0 sentences: 345.0 pages: flesch: 32.0 cache: ./cache/cord-264994-j8iawzp8.txt txt: ./txt/cord-264994-j8iawzp8.txt summary: Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Compartmental models analysing the interplay between vaccine uptake and disease dynamics confirmed the hypothesis that increases in vaccination were a response to the pertussis infection risk 61 , and showed that incorporating this interplay can improve epidemiological forecasts. abstract: The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making. url: https://doi.org/10.1038/s41564-019-0565-8 doi: 10.1038/s41564-019-0565-8 id: cord-299439-xvfab24g author: Fokas, A. S. title: COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA date: 2020-05-12 words: 2078.0 sentences: 134.0 pages: flesch: 56.0 cache: ./cache/cord-299439-xvfab24g.txt txt: ./txt/cord-299439-xvfab24g.txt summary: title: COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA We have recently introduced two novel mathematical models for characterizing the dynamics of the cumulative number of individuals in a given country reported to be infected with COVID-19. 64 In a recent paper (9) we presented a model for the dynamics of the accumulative number of 65 individuals in a given country that are reported at time t to be infected by COVID-19. Here we will show that the Ricatti equation introduced in (9) can also be used for determining the 82 time evolution of the number, N(t), of deaths in a given country caused by the COVID-19 epidemic. Thus, the birational and (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. abstract: We have recently introduced two novel mathematical models for characterizing the dynamics of the cumulative number of individuals in a given country reported to be infected with COVID-19. Here we show that these models can also be used for determining the time-evolution of the associated number of deaths. In particular, using data up to around the time that the rate of deaths reaches a maximum, these models provide estimates for the time that a plateau will be reached signifying that the epidemic is approaching its end, as well as for the cumulative number of deaths at that time. The plateau is defined to occur when the rate of deaths is 5% of the maximum rate. Results are presented for South Korea, Italy, Spain, France, UK, Germany, and USA. The number of COVID-19 deaths in other counties can be analyzed similarly. url: http://medrxiv.org/cgi/content/short/2020.05.08.20095489v1?rss=1 doi: 10.1101/2020.05.08.20095489 id: cord-238342-ecuex64m author: Fong, Simon James title: Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction date: 2020-03-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. url: https://arxiv.org/pdf/2003.09868v1.pdf doi: nan id: cord-020888-ov2lzus4 author: Formal, Thibault title: Learning to Rank Images with Cross-Modal Graph Convolutions date: 2020-03-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We are interested in the problem of cross-modal retrieval for web image search, where the goal is to retrieve images relevant to a text query. While most of the current approaches for cross-modal retrieval revolve around learning how to represent text and images in a shared latent space, we take a different direction: we propose to generalize the cross-modal relevance feedback mechanism, a simple yet effective unsupervised method, that relies on standard information retrieval heuristics and the choice of a few hyper-parameters. We show that we can cast it as a supervised representation learning problem on graphs, using graph convolutions operating jointly over text and image features, namely cross-modal graph convolutions. The proposed architecture directly learns how to combine image and text features for the ranking task, while taking into account the context given by all the other elements in the set of images to be (re-)ranked. We validate our approach on two datasets: a public dataset from a MediaEval challenge, and a small sample of proprietary image search query logs, referred as WebQ. Our experiments demonstrate that our model improves over standard baselines. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148208/ doi: 10.1007/978-3-030-45439-5_39 id: cord-330474-c6eq1djd author: Fox, J title: Rapid translation of clinical guidelines into executable knowledge: a case study of COVID‐19 and on‐line demonstration date: 2020-06-18 words: 3323.0 sentences: 156.0 pages: flesch: 45.0 cache: ./cache/cord-330474-c6eq1djd.txt txt: ./txt/cord-330474-c6eq1djd.txt summary: The initial goal is to assess whether the platform is adequate for rapidly building executable models of clinical expertise, while the longer term goal is to use the resulting COVID‐19 knowledge model as a reference and resource for medical training, research and, with partners, develop products and services for better patient care. The Polyphony project was initiated on 18 March 2020 with the following mission To create, validate, publish and maintain knowledge of best medical practice regarding the detection, diagnosis and management of COVID-19 infections, in a computer executable form. The purpose is to provide a resource for clinicians and researchers, healthcare provider organisations, technology developers and other users, to (1) develop point of care products and services which (2) embody best clinical practice in decision-making, workflow, data analysis and other "intelligent" services across the COVID patient journey. abstract: The Polyphony programme is a rapidly established collaboration whose aim is to build and maintain a collection of current healthcare knowledge about detection, diagnosis and treatment of COVID‐19 infections, and use Artificial Intelligence (knowledge engineering) techniques to apply the results in patient care. The initial goal is to assess whether the platform is adequate for rapidly building executable models of clinical expertise, while the longer term goal is to use the resulting COVID‐19 knowledge model as a reference and resource for medical training, research and, with partners, develop products and services for better patient care. In this Polyphony progress‐report we describe the first prototype of a care pathway and decision support system that is accessible on OpenClinical.net, a knowledge sharing repository. Pathfinder 1 demonstrates services including situation assessment and inference, decision making, outcome prediction and workflow management. Pathfinder 1 represents encouraging evidence that it is possible to rapidly develop and deploy practical clinical services for patient care and we hope to validate an advanced version in a collaborative internet trial. Finally, we discuss wider implications of the Polyphony framework for developing rapid learning systems in healthcare, and how we may prepare for using AI in future public health emergencies. This article is protected by copyright. All rights reserved. url: https://doi.org/10.1002/lrh2.10236 doi: 10.1002/lrh2.10236 id: cord-027337-eorjnma3 author: Fratrič, Peter title: Integrating Agent-Based Modelling with Copula Theory: Preliminary Insights and Open Problems date: 2020-05-22 words: 4860.0 sentences: 253.0 pages: flesch: 50.0 cache: ./cache/cord-027337-eorjnma3.txt txt: ./txt/cord-027337-eorjnma3.txt summary: The motivation for such a framework is illustrated on a artificial market functioning with canonical asset pricing models, showing that dependencies specified by copulas can enrich agent-based models to capture both micro-macro effects (e.g. herding behaviour) and macro-level dependencies (e.g. asset price dependencies). Section 2 provides some background: it elaborates on the combined need of agent-based modeling and of quantitative methods, illustrating the challenges on a running example based on canonical trader models for asset pricing, and gives a short presentation on copula theory. In other words, by this formula, it is possible to calculate the probability of rare events, and therefore estimate systematic risk, based on the dependencies of aggregation variables and on the knowledge of micro-behaviour specified by group density functions of the agent-based models. abstract: The paper sketches and elaborates on a framework integrating agent-based modelling with advanced quantitative probabilistic methods based on copula theory. The motivation for such a framework is illustrated on a artificial market functioning with canonical asset pricing models, showing that dependencies specified by copulas can enrich agent-based models to capture both micro-macro effects (e.g. herding behaviour) and macro-level dependencies (e.g. asset price dependencies). In doing that, the paper highlights the theoretical challenges and extensions that would complete and improve the proposal as a tool for risk analysis. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304032/ doi: 10.1007/978-3-030-50420-5_16 id: cord-030681-4brd2efp author: Friston, Karl J. title: Dynamic causal modelling of COVID-19 date: 2020-08-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431977/ doi: 10.12688/wellcomeopenres.15881.2 id: cord-130240-bfnav9sn author: Friston, Karl J. title: Dynamic causal modelling of COVID-19 date: 2020-04-09 words: 13594.0 sentences: 688.0 pages: flesch: 51.0 cache: ./cache/cord-130240-bfnav9sn.txt txt: ./txt/cord-130240-bfnav9sn.txt summary: The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Specifically, the posterior densities (i.e., Bayesian beliefs) over states and parameters-and the precision of random fluctuations-are optimised by maximising a variational bound on the model''s marginal likelihood, also known as model evidence. This figure reports the differences among countries in terms of selected parameters of the generative model, ranging from the effective population size, through to the probability of testing its denizens. In this example (based upon posterior expectations for the United Kingdom and Bayesian parameter averages over countries), death rates (per day) decrease progressively with social distancing. abstract: This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process. url: https://arxiv.org/pdf/2004.04463v1.pdf doi: nan id: cord-268959-wh28s0ws author: Gao, Da-peng title: Optimal control analysis of a tuberculosis model()() date: 2017-12-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper, we extend the model of Liu and Zhang (Math Comput Model 54:836-845, 2011) by incorporating three control terms and apply optimal control theory to the resulting model. Optimal control strategies are proposed to minimize both the disease burden and the intervention cost. We prove the existence and uniqueness of optimal control paths and obtain these optimal paths analytically using Pontryagin’s Maximum Principle. We analyse our results numerically to compare various strategies of proposed controls. It is observed that implementation of three controls is most effective and less expensive among all the strategies. Thus, we conclude that in order to reduce tuberculosis threat all the three controls must be taken into consideration concurrently. url: https://doi.org/10.1016/j.apm.2017.12.027 doi: 10.1016/j.apm.2017.12.027 id: cord-266090-f40v4039 author: Gao, Wei title: New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system date: 2020-08-03 words: 2737.0 sentences: 177.0 pages: flesch: 51.0 cache: ./cache/cord-266090-f40v4039.txt txt: ./txt/cord-266090-f40v4039.txt summary: title: New investigation of bats-hosts-reservoir-people coronavirus model and application to 2019-nCoV system According to the report presented by the World Health Organization, a new member of viruses, namely, coronavirus, shortly 2019-nCoV, which arised in Wuhan, China, on January 7, 2020, has been introduced to the literature. Whereas the obtained results show the effectiveness of the theoretical method considered for the governing system, the results also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. The obtained results show the effectiveness of the theoretical method considering the governing system and also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. In this subsection, by using VIM we numerically investigate the Bats-Hosts-Reservoir-People coronavirus model. Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative Application of variational iteration method to nonlinear differential equations of fractional order abstract: According to the report presented by the World Health Organization, a new member of viruses, namely, coronavirus, shortly 2019-nCoV, which arised in Wuhan, China, on January 7, 2020, has been introduced to the literature. The main aim of this paper is investigating and finding the optimal values for better understanding the mathematical model of the transfer of 2019-nCoV from the reservoir to people. This model, named Bats-Hosts-Reservoir-People coronavirus (BHRPC) model, is based on bats as essential animal beings. By using a powerful numerical method we obtain simulations of its spreading under suitably chosen parameters. Whereas the obtained results show the effectiveness of the theoretical method considered for the governing system, the results also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. url: https://www.ncbi.nlm.nih.gov/pubmed/32834818/ doi: 10.1186/s13662-020-02831-6 id: cord-276782-3fpmatkb author: Garbey, M. title: A Model of Workflow in the Hospital During a Pandemic to Assist Management date: 2020-05-02 words: 5717.0 sentences: 295.0 pages: flesch: 61.0 cache: ./cache/cord-276782-3fpmatkb.txt txt: ./txt/cord-276782-3fpmatkb.txt summary: The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. In some hospitals, the floor might be shared by patients who are 92 recovering from COVID-19 and palliative care patients.Despite this, we will separate 93 these functional units in our model to clarify the workflow process according to what 94 each patient stage requires in terms of resources and time to deliver adequate care. Number of Staff required at each care unit per beds in reference to the Workflow of Figure 1 Let us describe the data set we are using to construct our model. abstract: We present a computational model of workflow in the hospital during a pandemic. The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. The model was fitted with good accuracy to France's data set that gives information on hospitalized patients and is provided online by the French government. The goal of this work is both practical in offering hospital management a tool to deal with the present crisis of COVID-19 and offering a conceptual illustration of the benefit of computational science during a pandemic. url: http://medrxiv.org/cgi/content/short/2020.04.28.20083154v1?rss=1 doi: 10.1101/2020.04.28.20083154 id: cord-258762-vabyyx01 author: Garbey, Marc title: A Systems Approach to Assess Transport and Diffusion of Hazardous Airborne Particles in a Large Surgical Suite: Potential Impacts on Viral Airborne Transmission date: 2020-07-27 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Airborne transmission of viruses, such as the coronavirus 2 (SARS-CoV-2), in hospital systems are under debate: it has been shown that transmission of SARS-CoV-2 virus goes beyond droplet dynamics that is limited to 1 to 2 m, but it is unclear if the airborne viral load is significant enough to ensure transmission of the disease. Surgical smoke can act as a carrier for tissue particles, viruses, and bacteria. To quantify airborne transmission from a physical point of view, we consider surgical smoke produced by thermal destruction of tissue during the use of electrosurgical instruments as a marker of airborne particle diffusion-transportation. Surgical smoke plumes are also known to be dangerous for human health, especially to surgical staff who receive long-term exposure over the years. There are limited quantified metrics reported on long-term effects of surgical smoke on staff’s health. The purpose of this paper is to provide a mathematical framework and experimental protocol to assess the transport and diffusion of hazardous airborne particles in every large operating room suite. Measurements from a network of air quality sensors gathered during a clinical study provide validation for the main part of the model. Overall, the model estimates staff exposure to airborne contamination from surgical smoke and biological material. To address the clinical implication over a long period of time, the systems approach is built upon previous work on multi-scale modeling of surgical flow in a large operating room suite and takes into account human behavior factors. url: https://doi.org/10.3390/ijerph17155404 doi: 10.3390/ijerph17155404 id: cord-307340-00m2g55u author: Gerasimov, A. title: Reaching collective immunity for COVID-19: an estimate with a heterogeneous model based on the data for Italy date: 2020-05-25 words: 2252.0 sentences: 126.0 pages: flesch: 46.0 cache: ./cache/cord-307340-00m2g55u.txt txt: ./txt/cord-307340-00m2g55u.txt summary: Because of the high heterogeneity of COVID-19 infection risk across the different age groups, with a higher susceptibility for the elderly, homogeneous models overestimate the level of collective immunity needed for the disease to stop spreading. Because of the high heterogeneity of COVID-19 infection risk across the different age groups, with a higher susceptibility for the elderly, homogeneous models overestimate the level of collective immunity needed for the disease to stop spreading. Here we developed a mathematical model for assessing the minimum incidence of COVID-19 needed to reach collective immunity, which would assure that the epidemic cannot restart the cessation of quarantine measures. While this search yielded several useful references regarding COVID-19 modeling, the basic reproduction number of this disease, and age-related heterogeneity, we did not find an approach similar to ours to modeling COVID-19 dynamics and estimating the total incidence and population immunity. abstract: Background. At the current stage of COVID-19 pandemic, forecasts become particularly important regarding the possibility that the total incidence could reach the level where the disease stops spreading because a considerable portion of the population has become immune and collective immunity could be reached. Such forecasts are valuable because the currently undertaken restrictive measures prevent mass morbidity but do not result in the development of a robust collective immunity. Thus, in the absence of efficient vaccines and medical treatments, lifting restrictive measures carries the risk that a second wave of the epidemic could occur. Methods. We developed a heterogeneous model of COVID-19 dynamics. The model accounted for the differences in the infection risk across subpopulations, particularly the age-depended susceptibility to the disease. Based on this model, an equation for the minimal number of infections was calculated as a condition for the epidemic to start declining. The basic reproductive number of 2.5 was used for the disease spread without restrictions. The model was applied to COVID-19 data from Italy. Findings. We found that the heterogeneous model of epidemic dynamics yielded a lower proportion, compared to a homogeneous model, for the minimal incidence needed for the epidemic to stop. When applied to the data for Italy, the model yielded a more optimistic assessment of the minimum total incidence needed to reach collective immunity: 43% versus 60% estimated with a homogeneous model. Interpretation. Because of the high heterogeneity of COVID-19 infection risk across the different age groups, with a higher susceptibility for the elderly, homogeneous models overestimate the level of collective immunity needed for the disease to stop spreading. This inaccuracy can be corrected by the homogeneous model introduced here. To improve the estimate even further additional factors should be considered that contribute to heterogeneity, including social and professional activity, gender and individual resistance to the pathogen. url: https://doi.org/10.1101/2020.05.24.20112045 doi: 10.1101/2020.05.24.20112045 id: cord-225640-l0z56qx4 author: Ghamizi, Salah title: Data-driven Simulation and Optimization for Covid-19 Exit Strategies date: 2020-06-12 words: 4956.0 sentences: 242.0 pages: flesch: 54.0 cache: ./cache/cord-225640-l0z56qx4.txt txt: ./txt/cord-225640-l0z56qx4.txt summary: We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. As illustrated in Figure 1 , we propose to combine a genetic algorithm (to search for policy schedules), a deep learning model (to predict the evolution of the effective reproduction number induced by a given policy schedule) and an epidemiological model (to forecast, based on the computed effective reproduction numbers, the effect of the scheduled policies on public health over time, e.g. deaths and hospitalization occupancy). Epidemiological models predict the state of a population struck by a pandemic over time, based on state transition parameters and the evolution of the effective reproductive number, R t , of the disease. abstract: The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R2 score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies. url: https://arxiv.org/pdf/2006.07087v1.pdf doi: nan id: cord-147202-clje3b2r author: Ghanam, Ryad title: SEIRD Model for Qatar Covid-19 Outbreak: A Case Study date: 2020-05-26 words: 3867.0 sentences: 272.0 pages: flesch: 66.0 cache: ./cache/cord-147202-clje3b2r.txt txt: ./txt/cord-147202-clje3b2r.txt summary: This work provides a tutorial on building a compartmental model using Susceptibles, Exposed, Infected, Recovered and Deaths status through time. Figure shows the plots of the Active Infections, Recovered and Deaths data for Qatar for the days since February . In addition to changes in infection rates α, impulse functions can be used to model dramatic one time shifts in transitions between states. Recall that β A is associated with the Dirac delta function for impulse to model the jump in transition rate from Exposed to Infected at day . Figure shows the model ts for Active Infections, Recovered and Deaths with posterior predictive bands. This work has demonstrated how to build a SEIRD model for the Covid-outbreak in the State of Qatar, include interventions, estimate model parameters and generate posterior predictive intervals using a Bayesian framework. abstract: The Covid-19 outbreak of 2020 has required many governments to develop mathematical-statistical models of the outbreak for policy and planning purposes. This work provides a tutorial on building a compartmental model using Susceptibles, Exposed, Infected, Recovered and Deaths status through time. A Bayesian Framework is utilized to perform both parameter estimation and predictions. This model uses interventions to quantify the impact of various government attempts to slow the spread of the virus. Predictions are also made to determine when the peak Active Infections will occur. url: https://arxiv.org/pdf/2005.12777v1.pdf doi: nan id: cord-331374-3gau0vmc author: Giorgi, Gabriele title: Expatriates’ Multiple Fears, from Terrorism to Working Conditions: Development of a Model date: 2016-10-13 words: 7417.0 sentences: 362.0 pages: flesch: 44.0 cache: ./cache/cord-331374-3gau0vmc.txt txt: ./txt/cord-331374-3gau0vmc.txt summary: Structural equation model analyses showed that fear of expatriation mediates the relationship of mental health with fear of economic crisis and with perceived dangerous working conditions. Then, a structural model was performed to estimate the fit to the data of the hypothesized model in which fear of expatriation mediates the relationship of mental health problems with economic stress and perceived dangerous working conditions (Hypotheses 1 and 2) . A CFA was, therefore, performed with Mplus, version 7.11 (Muthén and Muthén, 1998-2010) , with the four variables measuring mental health problems, fear of expatriation, economic stress, and perceived dangerous working conditions. Next, we compared the hypothesized model with a nonmediation model (Model 3), which only included direct paths from mental health problems and fear of expatriation to economic stress and perceived dangerous working conditions. Furthermore, because mental health problems, fear of expatriation, economic stress, and perceived dangerous working conditions were all measured at the same time, reverse relationships could also be expected between the four variables. abstract: Companies’ internationalization appears to be fundamental in the current globalized and competitive environment and seems important not only for organizational success, but also for societal development and sustainability. On one hand, global business increases the demand for managers for international assignment. On the other hand, emergent fears, such as terrorism, seem to be developing around the world, enhancing the risk of expatriates’ potential health problems. The purpose of this paper is to examine the relationships between the emergent concept of fear of expatriation with further workplace fears (economic crisis and dangerous working conditions) and with mental health problems. The study uses a quantitative design. Self-reported data were collected from 265 Italian expatriate workers assigned to both Italian and worldwide projects. Structural equation model analyses showed that fear of expatriation mediates the relationship of mental health with fear of economic crisis and with perceived dangerous working conditions. As expected, in addition to fear, worries of expatriation are also related to further fears. Although, the study is based on self-reports and the cross-sectional study design limits the possibility of making causal inferences, the new constructs introduced add to previous research. url: https://www.ncbi.nlm.nih.gov/pubmed/27790173/ doi: 10.3389/fpsyg.2016.01571 id: cord-350240-bmppif8g author: Girardi, Paolo title: Robust inference for nonlinear regression models from the Tsallis score: application to COVID‐19 contagion in Italy date: 2020-08-12 words: 3228.0 sentences: 181.0 pages: flesch: 57.0 cache: ./cache/cord-350240-bmppif8g.txt txt: ./txt/cord-350240-bmppif8g.txt summary: title: Robust inference for nonlinear regression models from the Tsallis score: application to COVID‐19 contagion in Italy In particular, we focus on deaths and intensive care unit hospitalizations data, that are expected to aid the detection of the time when the peaks and the upper asymptotes of contagion, both in daily new cases and total cases, are reached, so that preventive measures (such as mobility restrictions) can be applied and/or relaxed. In contrast, the asymptotic distribution of the scoring rule ratio statisis a linear combination of independent chi-square random variables with coefficients related to the eigenvalues of the matrix J(θ)K(θ) −1 (Dawid et al., 2016) . The robust fits (Tsallis estimates and 95% confidence intervals) of the parameters e (inflection point) and d (upper asymptote) for the models are summarized in Tables 1 and 2 for DD and ICU, respectively. abstract: We discuss an approach of robust fitting on nonlinear regression models, both in a frequentist and a Bayesian approach, which can be employed to model and predict the contagion dynamics of COVID‐19 in Italy. The focus is on the analysis of epidemic data using robust dose‐response curves, but the functionality is applicable to arbitrary nonlinear regression models. url: https://doi.org/10.1002/sta4.309 doi: 10.1002/sta4.309 id: cord-026742-us7llnva author: Gonçalves, Judite title: Effects of self-employment on hospitalizations: instrumental variables analysis of social security data date: 2020-06-15 words: 8629.0 sentences: 400.0 pages: flesch: 48.0 cache: ./cache/cord-026742-us7llnva.txt txt: ./txt/cord-026742-us7llnva.txt summary: Our main findings, based on a sample of about 6,500 individuals followed monthly from 2005 to 2011 and who switch between self-employment and wage work along that period, suggest that self-employment has a positive effect on health as it reduces the likelihood of hospital admission by at least half. A recent study finds significantly lower work-related stress among self-employed individuals without employees compared with wage workers, using longitudinal data from Australia and controlling for individual fixed effects (Hessels et al. The main research question in this study is "What is the impact of self-employment on the likelihood of hospital admission?" We answer this question based on a large sample of administrative social security records representative of the working-age population in Portugal, that includes almost 130,000 self-employed and wage workers followed between January 2005 and December 2011. abstract: The importance of self-employment and small businesses raises questions about their health effects and public policy implications, which can only be addressed with suitable data. We explore the relationship between self-employment and health by drawing on comprehensive longitudinal administrative data to explore variation in individual work status and by applying novel instrumental variables. We focus on an objective outcome—hospital admissions—that is not subject to recall or other biases that may affect previous studies. Our main findings, based on a sample of about 6,500 individuals followed monthly from 2005 to 2011 and who switch between self-employment and wage work along that period, suggest that self-employment has a positive effect on health as it reduces the likelihood of hospital admission by at least half. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293961/ doi: 10.1007/s11187-020-00360-w id: cord-222868-k3k0iqds author: Goswami, Anindya title: Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning date: 2020-08-02 words: 9749.0 sentences: 520.0 pages: flesch: 60.0 cache: ./cache/cord-222868-k3k0iqds.txt txt: ./txt/cord-222868-k3k0iqds.txt summary: Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments. While the former used only the moneyness parameter (ratio of spot and strike values) and time-to-maturity as inputs to their learning model, the latter also used historical volatility, interest rate, and lagged prices of the underlying asset and option contract. Model evaluation metrics for models trained and tested on BANKNIFTY options contract price data From the results shown in Table 5 and Table 4 , it is evident that Approach III ANN models perform significantly better than all other proposed models. Table 11 presents the values of the performance metrics, for when the pre-trained Approach III models (constructed in sections 5.2 and 5.4) are tested on 2019 − 2020 data for the NIFTY50 Index. abstract: We propose three different data driven approaches for pricing European style call options using supervised machine-learning algorithms. The proposed approaches are tested on two stock market indices, NIFTY50 and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash, Jan 2019 to April 2020. url: https://arxiv.org/pdf/2008.00462v1.pdf doi: nan id: cord-133917-uap1vvbm author: Grave, Mal''u title: Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction Epidemiological Models date: 2020-10-22 words: 5926.0 sentences: 409.0 pages: flesch: 57.0 cache: ./cache/cord-133917-uap1vvbm.txt txt: ./txt/cord-133917-uap1vvbm.txt summary: In this work, we extend the formulation of SEIRD compartmental models to diffusion-reaction systems of partial differential equations to capture the continuous spatio-temporal dynamics of COVID-19. We implement the whole model in texttt{libMesh}, an open finite element library that provides a framework for multiphysics, considering adaptive mesh refinement and coarsening. We study a compartmental SEIRD model (susceptible, exposed, infected, recovered, deceased) that incorporates spatial spread through diffusion terms [16, 22, 8, 9, 23] . Adaptive mesh refinement and coarsening [24] can resolve population dynamics from local (street, city) to regional (district, state), providing an accurate spatio-temporal description of the infection spreading. Note that the EPIDEMIC model''s dynamics does not represent the actual COVID19 dynamics since, in the case of COVID19, the exposed population may be asymptomatic and recover without becoming infected and still spread the virus. In this section we briefly introduce the Galerkin finite element formulation, the time discretization, and the the libMesh implementation, supporting adaptive mesh refinement and coarsening. abstract: The outbreak of COVID-19 in 2020 has led to a surge in the interest in the mathematical modeling of infectious diseases. Disease transmission may be modeled as compartmental models, in which the population under study is divided into compartments and has assumptions about the nature and time rate of transfer from one compartment to another. Usually, they are composed of a system of ordinary differential equations (ODE's) in time. A class of such models considers the Susceptible, Exposed, Infected, Recovered, and Deceased populations, the SEIRD model. However, these models do not always account for the movement of individuals from one region to another. In this work, we extend the formulation of SEIRD compartmental models to diffusion-reaction systems of partial differential equations to capture the continuous spatio-temporal dynamics of COVID-19. Since the virus spread is not only through diffusion, we introduce a source term to the equation system, representing exposed people who return from travel. We also add the possibility of anisotropic non-homogeneous diffusion. We implement the whole model in texttt{libMesh}, an open finite element library that provides a framework for multiphysics, considering adaptive mesh refinement and coarsening. Therefore, the model can represent several spatial scales, adapting the resolution to the disease dynamics. We verify our model with standard SEIRD models and show several examples highlighting the present model's new capabilities. url: https://arxiv.org/pdf/2010.11861v2.pdf doi: nan id: cord-254339-djmibi3a author: Griette, Quentin title: Unreported Cases for Age Dependent COVID-19 Outbreak in Japan date: 2020-06-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We investigate the age structured data for the COVID-19 outbreak in Japan. We consider a mathematical model for the epidemic with unreported infectious patient with and without age structure. In particular, we build a new mathematical model and a new computational method to fit the data by using age classes dependent exponential growth at the early stage of the epidemic. This allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of public mitigation measures. url: https://www.ncbi.nlm.nih.gov/pubmed/32560572/ doi: 10.3390/biology9060132 id: cord-344115-gtbkwuqv author: Grimm, Volker title: Three questions to ask before using model outputs for decision support date: 2020-09-30 words: 1920.0 sentences: 116.0 pages: flesch: 50.0 cache: ./cache/cord-344115-gtbkwuqv.txt txt: ./txt/cord-344115-gtbkwuqv.txt summary: Without knowing its purpose, it is impossible to assess whether a model''s outputs can be used to support decisions affecting the real world. Decision makers can quickly understand which aspects of the real world are included, and which are excluded, by assessing: what entities are present in the model (e.g., individuals, populations, companies), what state variables characterize these entities (e.g., age, nationality, bank balance), what processes (e.g., movement patterns, meeting rates) link entities and their variables to system dynamics, and what are the temporal and spatial resolution and extent? The three screening questions support decision makers to assess whether a model is suitable for addressing real-world decisions and provide a common language for communication. The three questions do not replace more detailed guidelines on GMP 6,7 , but they provide a simple and effective common language that will allow us to develop models and use their outputs for decision support in a more transparent, robust, and safe way. abstract: Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs. url: https://www.ncbi.nlm.nih.gov/pubmed/32999285/ doi: 10.1038/s41467-020-17785-2 id: cord-350510-o4libq5d author: Grinfeld, M. title: On Linear Growth in COVID-19 Cases date: 2020-06-22 words: 2154.0 sentences: 121.0 pages: flesch: 65.0 cache: ./cache/cord-350510-o4libq5d.txt txt: ./txt/cord-350510-o4libq5d.txt summary: We present an elementary model of COVID-19 propagation that makes explicit the connection between testing strategies and rates of transmission and the linear growth in new cases observed in many parts of the world. MODELS We derive, in its simplest and most illuminating form, a system of two difference equations for the rate of growth of new positive test results and the number of people that have been exposed to the virus; that is, we neglect the asymptomatics. (A5) We assume that the information stream is dominated by the rate of increase of the numbers of new positive tests. It would be interesting to investigate models in which g is a function of more than the last day''s data, or of undominated maxima in the number of new cases, but we assume here for simplicity that R(n) is a reasonable proxy for the information stream. abstract: We present an elementary model of COVID-19 propagation that makes explicit the connection between testing strategies and rates of transmission and the linear growth in new cases observed in many parts of the world. An essential feature of the model is that it captures the population-level response to the infection statistics information provided by governments and other organisations. The conclusions from this model have important implications regarding benefits of wide-spread testing for the presence of the virus, something that deserves greater attention. url: http://medrxiv.org/cgi/content/short/2020.06.19.20135640v1?rss=1 doi: 10.1101/2020.06.19.20135640 id: cord-336747-8m7n5r85 author: Grossmann, G. title: Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study date: 2020-05-08 words: 7033.0 sentences: 432.0 pages: flesch: 55.0 cache: ./cache/cord-336747-8m7n5r85.txt txt: ./txt/cord-336747-8m7n5r85.txt summary: In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We calibrate both, ODE-models and stochastic models with interaction structure to the same basic reproduction number R 0 or to the same infection peak and compare the corresponding results. In the last decade, research focused largely on epidemic spreading, where interactions were constrained by contact networks, i.e. a graph representing the individuals (as nodes) and their connectivity (as edges). SIS-type models require knowledge of the spreading parameters (infection strength, recovery rate, etc.) and the contact network, which can partially be inferred from real-world observations. We are interested in the relationship between the contact network structure, R 0 , the height and time point of the infection-peak, and the number of individuals ultimately affected by the epidemic. abstract: In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time. In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic's dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights. url: https://doi.org/10.1101/2020.05.05.20091736 doi: 10.1101/2020.05.05.20091736 id: cord-140624-lphr5prl author: Grundel, Sara title: How much testing and social distancing is required to control COVID-19? Some insight based on an age-differentiated compartmental model date: 2020-11-02 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper, we provide insights on how much testing and social distancing is required to control COVID-19. To this end, we develop a compartmental model that accounts for key aspects of the disease: 1) incubation time, 2) age-dependent symptom severity, and 3) testing and hospitalization delays; the model's parameters are chosen based on medical evidence, and, for concreteness, adapted to the German situation. Then, optimal mass-testing and age-dependent social-distancing policies are determined by solving optimal control problems both in open loop and within a model predictive control framework. We aim to minimize testing and/or social distancing until herd immunity sets in under a constraint on the number of available intensive care units. We find that an early and short lockdown is inevitable but can be slowly relaxed over the following months. url: https://arxiv.org/pdf/2011.01282v1.pdf doi: nan id: cord-273815-7ftztaqn author: Gupta, R. K. title: Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date: 2020-07-26 words: 4918.0 sentences: 288.0 pages: flesch: 40.0 cache: ./cache/cord-273815-7ftztaqn.txt txt: ./txt/cord-273815-7ftztaqn.txt summary: We also assessed the discrimination of each candidate model for standardised outcomes of: (a) our composite endpoint of clinical deterioration; and (b) mortality, across a range of pre-specified time horizons from admission (7 days, 14 days, 30 days and any time during hospital admission), by calculating time-dependent AUROCs (with cumulative sensitivity and dynamic specificity) 18 . In order to further benchmark the performance of candidate prognostic models, we then computed AUROCs for a limited number of univariable predictors considered to be of highest importance a priori, based on clinical knowledge and existing data, for prediction of our composite endpoints of clinical deterioration and mortality (7 days, 14 days, 30 days and any time during hospital admission). We compared net benefit for each prognostic model (for its original intended endpoint) to the strategies of treating all patients, treating no patients, and using the most discriminating univariable predictor for either deterioration (i.e. oxygen saturation on air) or mortality (i.e. patient age) to stratify treatment (Supplementary Figure 9 ). abstract: Background The number of proposed prognostic models for COVID-19, which aim to predict disease outcomes, is growing rapidly. It is not known whether any are suitable for widespread clinical implementation. We addressed this question by independent and systematic evaluation of their performance among hospitalised COVID-19 cases. Methods We conducted an observational cohort study to assess candidate prognostic models, identified through a living systematic review. We included consecutive adults admitted to a secondary care hospital with PCR-confirmed or clinically diagnosed community-acquired COVID-19 (1st February to 30th April 2020). We reconstructed candidate models as per their original descriptions and evaluated performance for their original intended outcomes (clinical deterioration or mortality) and time horizons. We assessed discrimination using the area under the receiver operating characteristic curve (AUROC), and calibration using calibration plots, slopes and calibration-in-the-large. We calculated net benefit compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses, based on a limited subset of a priori candidates. Results We tested 22 candidate prognostic models among a cohort of 411 participants, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. The highest AUROCs were achieved by the NEWS2 score for prediction of deterioration over 24 hours (0.78; 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74-0.82). Calibration appeared generally poor for models that used probability outcomes. In univariable analyses, admission oxygen saturation on room air was the strongest predictor of in-hospital deterioration (AUROC 0.76; 0.71-0.81), while age was the strongest predictor of in-hospital mortality (AUROC 0.76; 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than using the most discriminating univariable predictors to stratify treatment, across a range of threshold probabilities. Conclusions Oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated offer incremental value for patient stratification to these univariable predictors. url: http://medrxiv.org/cgi/content/short/2020.07.24.20149815v1?rss=1 doi: 10.1101/2020.07.24.20149815 id: cord-267150-hf0jtfmx author: Gupta, Rajan title: SEIR and Regression Model based COVID-19 outbreak predictions in India date: 2020-04-03 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals. url: https://doi.org/10.1101/2020.04.01.20049825 doi: 10.1101/2020.04.01.20049825 id: cord-335465-sckfkciz author: Gupta, Rishi K. title: Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study date: 2020-09-25 words: 5052.0 sentences: 246.0 pages: flesch: 33.0 cache: ./cache/cord-335465-sckfkciz.txt txt: ./txt/cord-335465-sckfkciz.txt summary: We aimed to address this knowledge gap by systematically evaluating the performance of proposed prognostic models, among consecutive patients hospitalised with a final diagnosis of COVID-19 at a single centre, when using predictors measured at the point of hospital admission. We also assessed the discrimination of each candidate model for standardised outcomes of: (a) our composite endpoint of clinical deterioration; and (b) mortality, across a range of pre-specified time horizons from admission (7 days, 14 days, 30 days and any time during hospital admission), by calculating time-dependent AUROCs (with cumulative sensitivity and dynamic specificity) [18] . In order to further benchmark the performance of candidate prognostic models, we then computed AUROCs for a limited number of univariable predictors considered to be of highest importance a priori, based on clinical knowledge and existing data, for prediction of our composite endpoints of clinical deterioration and mortality (7 days, 14 days, 30 days and any time during hospital admission). abstract: BACKGROUND: The number of proposed prognostic models for COVID-19 is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation. METHODS: We independently externally validated the performance candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses. RESULTS: We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78; 95% CI 0.73–0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74–0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76; 0.71–0.81), and age for in-hospital mortality (AUROC 0.76; 0.71–0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities. CONCLUSIONS: Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors. url: https://doi.org/10.1183/13993003.03498-2020 doi: 10.1183/13993003.03498-2020 id: cord-280064-rz8cglyt author: Gwizdałła, Tomasz title: Viral disease spreading in grouped population date: 2020-08-27 words: 6851.0 sentences: 392.0 pages: flesch: 56.0 cache: ./cache/cord-280064-rz8cglyt.txt txt: ./txt/cord-280064-rz8cglyt.txt summary: In the section devoted to the presentation of results, we concentrate on the epidemic curves, which are presented in two forms, i.e., the number of new cases and the number of recovered persons (in the absence of the mortality rate), and on the analysis of intervention, considered as the minimization of the number of contacts between neighbors in the network. This continuous approach enables easy calculation of one of the most interesting values describing the potential effect of an outbreak, i.e., the basic reproduction number, which is a simple function of the ODE parameters: Although continuous models based on the ODEs give many interesting and practical results, it is well known [3] that there exists a large stochastic effect in the epidemic process. Considering once more the effect of intervention (see plots (b) and (d) in Fig. 3 and 4) , we can observe that, with intervention included, the duration of the epidemic does not strongly depend on the parameters of the model. abstract: Background and Objective The currently active COVID-19 pandemic has increased, among others, public interest in the computational techniques enabling the study of disease-spreading processes. Thus far, numerous approaches have been used to study the development of epidemics, with special attention paid to the identification of crucial elements that can strengthen or weaken the dynamics of the process. The main thread of this research is associated with the use of the ordinary differential equations method. There also exist several approaches based on the analysis of flows in the Cellular Automata (CA) approach. Methods In this paper, we propose a new approach to disease-spread modeling. We start by creating a network that reproduces contacts between individuals in a community. This assumption makes the presented model significantly different from the ones currently dominant in the field. It also changes the approach to the act of infection. Usually, some parameters that describe the rate of new infections by taking into account those infected in the previous time slot are considered. With our model, we can individualize this process, considering each contact individually. Results The typical output from calculations of a similar type are epidemic curves. In our model, except of presenting the average curves, we show the deviations or ranges for particular results obtained in different simulation runs, which usually lead to significantly different results. This observation is the effect of the probabilistic character of the infection process, which can impact, in different runs, individuals with different significance to the community. We can also easily present the effects of different types of intervention. The effects are studied for different methods used to create the graph representing a community, which can correspond to different social bonds. Conclusions We see the potential usefulness of the proposition in the detailed study of epidemic development for specific environments and communities. The ease of entering new parameters enables the analysis of several specific scenarios for different contagious diseases. url: https://www.sciencedirect.com/science/article/pii/S0169260720315480?v=s5 doi: 10.1016/j.cmpb.2020.105715 id: cord-287145-w518a0wa author: Habib, Nahida title: Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection date: 2020-10-30 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)—CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images. url: https://doi.org/10.1007/s42979-020-00373-y doi: 10.1007/s42979-020-00373-y id: cord-171231-m54moffr author: Habli, Ibrahim title: Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models date: 2020-05-17 words: 2233.0 sentences: 110.0 pages: flesch: 45.0 cache: ./cache/cord-171231-m54moffr.txt txt: ./txt/cord-171231-m54moffr.txt summary: When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. Similar to engineered safety-critical systems, e.g. flight control software or pacemakers, the rigour and transparency with which these simulation models are developed should be proportionate to their criticality to, and influence on, public health policy -this is true for COVID-19 but also holds for other models used to support such critical decision-making. In safety-critical systems engineering it is common practice to produce an assurance case -a structured, explicit argument supported by evidence [3] . We argue that such a case has the potential to enable a wider understanding, and a critical review, of the expected benefits, limitations and assumptions that underpin the development of the simulation models and the extent to which these issues, including the different sources of uncertainty, are considered in the policy decision-making process. abstract: Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the underlying modelling assumptions and limitations communicated so that decision-makers can readily understand them? When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. We argue that any COVID-19 simulation model that is used to guide critical policy decisions would benefit from being supported with such a case to explain how, and to what extent, the evidence from the simulation can be relied on to substantiate policy conclusions. This would enable a critical review of the implicit assumptions and inherent uncertainty in modelling, and would give the overall decision-making process greater transparency and accountability. url: https://arxiv.org/pdf/2005.08381v1.pdf doi: nan id: cord-028636-wxack9zv author: Hachicha, A. title: Analysis of the bitcoin stock market indexes using comparative study of two models SV with MCMC algorithm date: 2020-07-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The purpose of this article is to find a better technique for estimating the volatility of the price of bitcoin on the one hand and to check if this special kind of asset called cryptocurrency behaves like other stock market indices. We include five stock market indexes for different countries such as Standard and Poor’s 500 composite Index (S&P), Nasdaq, Nikkei, Stoxx, and DowJones. Using daily data over the period 2010–2019. We examine two asymmetric stochastic volatility models used to describe the volatility dependencies found in most financial returns. Two models are compared, the first is the autoregressive stochastic volatility model with Student’s t-distribution (ARSV-t), and the second is the basic SVOL. To estimate these models, our analysis is based on the Markov Chain Monte-Carlo method. Therefore, the technique used is a Metropolis–Hastings (Hastings in Biometrika 57:97–109, 1970), and the Gibbs sampler (Casella and George in Am Stat 46:167–174, 1992; Gelfand and Smith in J Am Stat Assoc 85:398–409, 1990; Gilks and Wild in 41:337–348, 1992). Model comparisons illustrate that the ARSV-t model performs better performances. We conclude that this model is better than the SVOL model on the MSE and AIC function. This result concerns bitcoin as well as the other stock market indices. Without forgetting that our finding proves the efficiency of Markov Chain for our sample and the convergence and stability for all parameters to a certain level. On the whole, it seems that permanent shocks have an effect on the volatility of the price of the bitcoin and also on the other stock market. Our results will help investors better diversify their portfolio by adding this cryptocurrency. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338123/ doi: 10.1007/s11156-020-00905-w id: cord-022891-vgfv5pi4 author: Hall, Graeme M. J. title: SIMULATING NEW ZEALAND FOREST DYNAMICS WITH A GENERALIZED TEMPERATE FOREST GAP MODEL date: 2000-02-01 words: 10475.0 sentences: 541.0 pages: flesch: 53.0 cache: ./cache/cord-022891-vgfv5pi4.txt txt: ./txt/cord-022891-vgfv5pi4.txt summary: Forest gap simulation models have been developed to predict long-term impacts on forest ecosystems caused by blight, harvest management, past climates, animal browse, pollution, and large-scale disturbance by fire or storm, and to predict transients in species composition and forest structure due to changing climate, (e.g., Shugart and West 1977 , Aber et al. The LINKAGES model, as presented by Pastor and Post (1986) , required modifications to its slow-growth, available-light, and decay-rate conditions to reproduce forests characteristic of New Zealand sites. By contrast, simulations carried out using the cooler climate conditions for Reefton (typical of the South Island west coast of New Zealand) suggest that the emergent podocarp Dacrydium cupressinum, in association with the common hardwood Weinmannia racemosa, will more quickly dominate plots in this area (after the initial establishment of Aristotelia serrata, Leptospermum scoparium, and Kunzea ericoides). abstract: A generalized computer model of forest growth and nutrient dynamics (LINKAGES) was adapted for the temperate evergreen forests of New Zealand. Systematic differences in species characteristics between eastern North American species and their New Zealand counterparts prevented the initial version of the model from running acceptably with New Zealand species. Several equations were identified as responsible, and those modeling available light were extended to give more robust formulations. The resulting model (LINKNZ) was evaluated by comparing site simulations against independent field measurements of stand sequences and across temperature and moisture gradients. It successfully simulated gap dynamics and forest succession for a range of temperate forest ecosystems in New Zealand, while retaining its utility for the forests of eastern North America. These simulations provided insight into New Zealand conifer–hardwood and beech species forest succession. The adequacy of the ecological processes, such as soil moisture balance, decomposition rates, and nutrient cycling, embodied in a forest simulation model was tested by applying it to New Zealand forest ecosystems. This gave support to the model’s underlying hypothesis, derived from LINKAGES, that interactions among demographic, microbial, and geological processes can explain much of the observed variation in ecosystem carbon and nitrogen storage and cycling. The addition of a disturbance option to the model supported the hypothesis that large‐scale disturbance significantly affects New Zealand forest dynamics. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7163527/ doi: 10.1890/1051-0761(2000)010[0115:snzfdw]2.0.co;2 id: cord-346136-sqc09x9c author: Hamilton, Kyra title: Application of the Health Action Process Approach to Social Distancing Behavior During COVID‐19 date: 2020-10-02 words: 8263.0 sentences: 356.0 pages: flesch: 36.0 cache: ./cache/cord-346136-sqc09x9c.txt txt: ./txt/cord-346136-sqc09x9c.txt summary: Given that social distancing is a key evidence-based behavior that will minimise transmission of SARS-CoV-2 if performed consistently at the population level, the aim of the present study was to apply the HAPA to identify the social cognition and self-regulatory determinants of this preventive behavior in samples of adults from two countries, Australia and the US. The study adopted a prospective correlational design with self-report measures of HAPA constructs (attitudes, self-efficacy, risk perceptions, intentions, action planning, coping planning, and action control) and past engagement in social distancing behavior administered at an initial time-point (T1) in a survey administered using the Qualtrics TM online survey tool. The present research has a number of strengths including focus on social distancing, a key preventive behavior aimed at reducing transmission of SARS-CoV-2 to prevent COVID-19 infections; adoption of a fit-for-purpose theoretical model, the HAPA, that provides a set of a priori predictions on the motivational and volitional determinants of the target behavior; recruitment of samples from two countries, Australia and the US, with key demographic characteristics that closely match those of the population; and the use of prospective study design and structural equation modelling techniques. abstract: BACKGROUND: This study examined the social cognition determinants of social distancing behavior during the COVID‐19 pandemic in samples from Australia and the US guided by the health action process approach (HAPA). METHODS: Participants (Australia: N = 495, 50.1% women; US: N = 701, 48.9% women) completed HAPA social cognition constructs at an initial time‐point (T1), and one week later (T2) self‐reported their social distancing behavior. RESULTS: Single‐indicator structural equation models that excluded and included past behavior exhibited adequate fit with the data. Intention and action control were significant predictors of social distancing behavior in both samples, and intention predicted action and coping planning in the US sample. Self‐efficacy and action control were significant predictors of intention in both samples, with attitudes predicting intention in the Australia sample and risk perceptions predicting intention in the US sample. Significant indirect effects of social cognition constructs through intentions were observed. Inclusion of past behavior attenuated model effects. Multigroup analysis revealed no differences in model fit across samples, suggesting that observed variations in the parameter estimates were relatively trivial. CONCLUSION: Results indicate that social distancing is a function of motivational and volitional processes. This knowledge can be used to inform messaging regarding social distancing during COVID‐19 and in future pandemics. url: https://doi.org/10.1111/aphw.12231 doi: 10.1111/aphw.12231 id: cord-241057-cq20z1jt author: Han, Jungmin title: Statistical Physics of Epidemic on Network Predictions for SARS-CoV-2 Parameters date: 2020-07-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The SARS-CoV-2 pandemic has necessitated mitigation efforts around the world. We use only reported deaths in the two weeks after the first death to determine infection parameters, in order to make predictions of hidden variables such as the time dependence of the number of infections. Early deaths are sporadic and discrete so the use of network models of epidemic spread is imperative, with the network itself a crucial random variable. Location-specific population age distributions and population densities must be taken into account when attempting to fit these events with parametrized models. These characteristics render naive Bayesian model comparison impractical as the networks have to be large enough to avoid finite-size effects. We reformulated this problem as the statistical physics of independent location-specific `balls' attached to every model in a six-dimensional lattice of 56448 parametrized models by elastic springs, with model-specific `spring constants' determined by the stochasticity of network epidemic simulations for that model. The distribution of balls then determines all Bayes posterior expectations. Important characteristics of the contagion are determinable: the fraction of infected patients that die ($0.017pm 0.009$), the expected period an infected person is contagious ($22 pm 6$ days) and the expected time between the first infection and the first death ($25 pm 8$ days) in the US. The rate of exponential increase in the number of infected individuals is $0.18pm 0.03$ per day, corresponding to 65 million infected individuals in one hundred days from a single initial infection, which fell to 166000 with even imperfect social distancing effectuated two weeks after the first recorded death. The fraction of compliant socially-distancing individuals matters less than their fraction of social contact reduction for altering the cumulative number of infections. url: https://arxiv.org/pdf/2007.03101v1.pdf doi: nan id: cord-048325-pk7pnmlo author: Hanley, Brian title: An object simulation model for modeling hypothetical disease epidemics – EpiFlex date: 2006-08-23 words: 8900.0 sentences: 524.0 pages: flesch: 59.0 cache: ./cache/cord-048325-pk7pnmlo.txt txt: ./txt/cord-048325-pk7pnmlo.txt summary: RESULTS: EpiFlex indicates three phenomena of interest for public health: (1) R(0 )is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. EpiFlex uses a dynamic network to model the interactions between hosts at a particular location based on the skew provided and the demographic segments movement cycles. The EpiFlex system iterates through all areas in a model and allocates hosts, putting them in their initial locations, per the movement definitions for the demographic group. abstract: BACKGROUND: EpiFlex is a flexible, easy to use computer model for a single computer, intended to be operated by one user who need not be an expert. Its purpose is to study in-silico the epidemic behavior of a wide variety of diseases, both known and theoretical, by simulating their spread at the level of individuals contracting and infecting others. To understand the system fully, this paper must be read together in conjunction with study of the software and its results. EpiFlex is evaluated using results from modeling influenza A epidemics and comparing them with a variety of field data sources and other types of modeling. EpiFlex is an object-oriented Monte Carlo system, allocating entities to correspond to individuals, disease vectors, diseases, and the locations that hosts may inhabit. EpiFlex defines eight different contact types available for a disease. Contacts occur inside locations within the model. Populations are composed of demographic groups, each of which has a cycle of movement between locations. Within locations, superspreading is defined by skewing of contact distributions. RESULTS: EpiFlex indicates three phenomena of interest for public health: (1) R(0 )is variable, and the smaller the population, the larger the infected fraction within that population will be; (2) significant compression/synchronization between cities by a factor of roughly 2 occurs between the early incubation phase of a multi-city epidemic and the major manifestation phase; (3) if better true morbidity data were available, more asymptomatic hosts would be seen to spread disease than we currently believe is the case for influenza. These results suggest that field research to study such phenomena, while expensive, should be worthwhile. CONCLUSION: Since EpiFlex shows all stages of disease progression, detailed insight into the progress of epidemics is possible. EpiFlex shows the characteristic multimodality and apparently random variation characteristic of real world data, but does so as an emergent property of a carefully constructed model of disease dynamics and is not simply a stochastic system. EpiFlex can provide a better understanding of infectious diseases and strategies for response. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1570461/ doi: 10.1186/1742-4682-3-32 id: cord-347791-wofyftrs author: Hao, Tian title: Prediction of Coronavirus Disease (covid-19) Evolution in USA with the Model Based on the Eyring Rate Process Theory and Free Volume Concept date: 2020-04-22 words: 3603.0 sentences: 206.0 pages: flesch: 57.0 cache: ./cache/cord-347791-wofyftrs.txt txt: ./txt/cord-347791-wofyftrs.txt summary: title: Prediction of Coronavirus Disease (covid-19) Evolution in USA with the Model Based on the Eyring Rate Process Theory and Free Volume Concept A modification arguing that the human movement energy may change with time is made on our previous infectious disease model, in which infectious disease transmission is considered as a sequential chemical reaction and reaction rate constants obey the Eyrings rate process theory and free volume concept. For better fitting data, modification is made on our previous model 1 by introducing an idea that the energy for human individuals to transmit diseases is time dependent, which is in line with other systems like granular powder under tapping process where the energy of particles is time dependent, too 18 . Infection Dynamics of Coronavirus Disease 2019 (Covid-19) Modeled with the Integration of the Eyring Rate Process Theory and Free Volume Concept abstract: A modification arguing that the human movement energy may change with time is made on our previous infectious disease model, in which infectious disease transmission is considered as a sequential chemical reaction and reaction rate constants obey the Eyrings rate process theory and free volume concept. The modified model is employed to fit current covid-19 outbreak data in USA and to make predictions on the numbers of the infected, the removed and the death in the foreseeable future. Excellent fitting curves and regression quality are obtained, indicating that the model is working and the predictions may be close to reality. Our work could provide some ideas on what we may expect in the future and how we can prepare accordingly for this difficult period. url: https://doi.org/10.1101/2020.04.16.20068692 doi: 10.1101/2020.04.16.20068692 id: cord-244657-zp65561y author: Hawryluk, Iwona title: Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection date: 2020-09-08 words: 7250.0 sentences: 371.0 pages: flesch: 49.0 cache: ./cache/cord-244657-zp65561y.txt txt: ./txt/cord-244657-zp65561y.txt summary: In this paper we introduce a variation of the TI method, here referred to as referenced TI, which computes a single model''s evidence in an efficient way by using a reference density such as a multivariate normal where the normalising constant is known. We show that referenced TI, an asymptotically exact Monte Carlo method of calculating the normalising constant of a single model, in practice converges to the correct result much faster than other competing approaches such as the method of power posteriors. In each referenced TI scenario, we note that even if the reference approximation is poor, the estimate of the normalising constant based on Equation 3 remains asymptotically exact -only the speed of convergence may be reduced (provided assumptions such matching support of end-point densities remains). In the primary application discussed later, regarding relatively complex high-dimensional Bayesian hierarchical models, we use this approach to generate a reference density and normalising constant. abstract: Model selection is a fundamental part of Bayesian statistical inference; a widely used tool in the field of epidemiology. Simple methods such as Akaike Information Criterion are commonly used but they do not incorporate the uncertainty of the model's parameters, which can give misleading choices when comparing models with similar fit to the data. One approach to model selection in a more rigorous way that uses the full posterior distributions of the models is to compute the ratio of the normalising constants (or model evidence), known as Bayes factors. These normalising constants integrate the posterior distribution over all parameters and balance over and under fitting. However, normalising constants often come in the form of intractable, high-dimensional integrals, therefore special probabilistic techniques need to be applied to correctly estimate the Bayes factors. One such method is thermodynamic integration (TI), which can be used to estimate the ratio of two models' evidence by integrating over a continuous path between the two un-normalised densities. In this paper we introduce a variation of the TI method, here referred to as referenced TI, which computes a single model's evidence in an efficient way by using a reference density such as a multivariate normal - where the normalising constant is known. We show that referenced TI, an asymptotically exact Monte Carlo method of calculating the normalising constant of a single model, in practice converges to the correct result much faster than other competing approaches such as the method of power posteriors. We illustrate the implementation of the algorithm on informative 1- and 2-dimensional examples, and apply it to a popular linear regression problem, and use it to select parameters for a model of the COVID-19 epidemic in South Korea. url: https://arxiv.org/pdf/2009.03851v2.pdf doi: nan id: cord-122344-2lepkvby author: Hayashi, Hiroaki title: What''s New? Summarizing Contributions in Scientific Literature date: 2020-11-06 words: 7260.0 sentences: 383.0 pages: flesch: 44.0 cache: ./cache/cord-122344-2lepkvby.txt txt: ./txt/cord-122344-2lepkvby.txt summary: To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. The new task''s goal is to generate two summaries simultaneously, one strictly focused on the summarized article''s novelties and contributions, the other introducing the context of the work and previous efforts. Recent trends in abstractive text summarization show a shift of focus from designing task-specific architectures trained from scratch (See et al., 2017; Paulus et al., 2018) to leveraging large-scale Transformer-based models pre-trained on vast amounts of data (Liu & Lapata, 2019; Lewis et al., 2020) , often in multi-task settings (Raffel et al., 2019) . In this paper, we propose disentangled paper summarization, a new task in scientific paper summarizing where models simultaneously generate contribution and context summaries. abstract: With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. For this purpose, we extend the S2ORC corpus of academic articles, which spans a diverse set of domains ranging from economics to psychology, by adding disentangled"contribution"and"context"reference labels. Together with the dataset, we introduce and analyze three baseline approaches: 1) a unified model controlled by input code prefixes, 2) a model with separate generation heads specialized in generating the disentangled outputs, and 3) a training strategy that guides the model using additional supervision coming from inbound and outbound citations. We also propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs. Through a human study involving expert annotators, we show that in 79%, of cases our new task is considered more helpful than traditional scientific paper summarization. url: https://arxiv.org/pdf/2011.03161v2.pdf doi: nan id: cord-299932-c079r94n author: He, X. title: Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans date: 2020-06-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: COVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people's lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at: https://github.com/arthursdays/HKBU_HPML_COVID-19. url: http://medrxiv.org/cgi/content/short/2020.06.08.20125963v1?rss=1 doi: 10.1101/2020.06.08.20125963 id: cord-195263-i4wyhque author: Heider, Philipp title: COVID-19 mitigation strategies and overview on results from relevant studies in Europe date: 2020-05-11 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In December 2019, the first patients in Wuhan, China were diagnosed with a primary atypical pneumonia, which showed to be unknown and contagious. Since then, known as COVID-19 disease, the responsible viral pathogen, SARS-CoV-2, has spread around the world in a pandemic. Decisions on how to deal with the crisis are often based on simulations of the pandemic spread of the virus. The results of some of these, as well as their methodology and possibilities for improvement, will be described in more detail in this paper in order to inform beyond the current public health dogma called"flatten-the-curve". There are several ways to model an epidemic in order to simulate the spread of diseases. Depending on the timeliness, scope and quality of the associated real data, these multivariable models differ in the value of used parameters, but also in the selection of considered influencing factors. It was exemplarily shown that epidemics in their course are simulated more realistically by models that assume subexponential growth. Furthermore, various simulations of the COVID-19 pandemic were presented in an European perspective, compared against each other and discussed in more detail. It is difficult to estimate how credible the simulations of the pandemic models currently are, so it remains to be seen whether the spread of the pandemic can be effectively reduced by the measures taken. Whether a model works well in reality is largely determined by the quality and scope of its underlying data. Past studies have shown that countermeasures are able to reduce reproduction numbers or transmission rates in epidemics. In addition to that, the presented modelling study provides a good framework for the creation of subexponential-growth-models for assessing the spread of COVID-19. url: https://arxiv.org/pdf/2005.05249v1.pdf doi: nan id: cord-018947-d4im0p9e author: Helbing, Dirk title: Challenges in Economics date: 2012-02-10 words: 11075.0 sentences: 750.0 pages: flesch: 48.0 cache: ./cache/cord-018947-d4im0p9e.txt txt: ./txt/cord-018947-d4im0p9e.txt summary: This is also relevant for the following challenges, as boundedly rational agents may react inefficently and with delays, which questions the efficient market hypothesis, the equilibrium paradigm, and other fundamental concepts, calling for the consideration of spatial, network, and time-dependencies, heterogeneity and correlations etc. While it is a well-known problem that people tend to make unfair contributions to public goods or try to get a bigger share of them, individuals cooperate much more than one would expect according to the representative agent approach. In economics, one tries to solve the problem by introducing taxes (i.e. another incentive structure) or a "shadow of the future" (i.e. a strategic optimization over infinite time horizons in accordance with the rational agent approach) [96, 97] . One of the most important drawbacks of the representative agent approach is that it cannot explain the fundamental fact of economic exchange, since it requires one to assume a heterogeneity in resources or production costs, or to consider a variation in the value of goods among individuals. abstract: In the same way as the Hilbert Program was a response to the foundational crisis of mathematics [1], this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent financial and economic crisis has revealed. By identifying weak points of conventional approaches in economics, we identify the scientific problems which need to be addressed. We expect that solving these questions will bring scientists in a position to give better decision support and policy advice. We also indicate, what kinds of insights can be contributed by scientists from other research fields such as physics, biology, computer and social science. In order to make a quick progress and gain a systemic understanding of the whole interconnected socio-economic-environmental system, using the data, information and computer systems available today and in the near future, we suggest a multi-disciplinary collaboration as most promising research approach. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123964/ doi: 10.1007/978-3-642-24004-1_16 id: cord-289447-d93qwjui author: Helmy, Mohamed title: Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering date: 2020-10-09 words: 7405.0 sentences: 359.0 pages: flesch: 38.0 cache: ./cache/cord-289447-d93qwjui.txt txt: ./txt/cord-289447-d93qwjui.txt summary: Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. On the other hand, Jervis et al implemented an ML algorithm to model the bacterial ribosome binding sites (RBSs) sequence-phenotype relationship and accurately predicted the optimal high-producers, an approach that directly apply on wide range of metabolic engineering applications [106] . To understand the key regulatory or emergent bottleneck scenarios that limit their industrial applicability, they undertook a large scale -omics based systems biology approach where they performed time-series proteomics and metabolomics measurements, and analyzed the resultant high-throughput data using statistical analytics and genome-scale modeling. Although genome annotation, both structural and functional, affects most of the biomedical research aspects, it has a special impact on metabolic engineering in general and applications in food industry in particular. abstract: Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including the multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. url: https://doi.org/10.1016/j.mec.2020.e00149 doi: 10.1016/j.mec.2020.e00149 id: cord-001687-paax8pqh author: Henkel, Jan title: Bone Regeneration Based on Tissue Engineering Conceptions — A 21st Century Perspective date: 2013-09-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The role of Bone Tissue Engineering in the field of Regenerative Medicine has been the topic of substantial research over the past two decades. Technological advances have improved orthopaedic implants and surgical techniques for bone reconstruction. However, improvements in surgical techniques to reconstruct bone have been limited by the paucity of autologous materials available and donor site morbidity. Recent advances in the development of biomaterials have provided attractive alternatives to bone grafting expanding the surgical options for restoring the form and function of injured bone. Specifically, novel bioactive (second generation) biomaterials have been developed that are characterised by controlled action and reaction to the host tissue environment, whilst exhibiting controlled chemical breakdown and resorption with an ultimate replacement by regenerating tissue. Future generations of biomaterials (third generation) are designed to be not only osteoconductive but also osteoinductive, i.e. to stimulate regeneration of host tissues by combining tissue engineering and in situ tissue regeneration methods with a focus on novel applications. These techniques will lead to novel possibilities for tissue regeneration and repair. At present, tissue engineered constructs that may find future use as bone grafts for complex skeletal defects, whether from post-traumatic, degenerative, neoplastic or congenital/developmental “origin” require osseous reconstruction to ensure structural and functional integrity. Engineering functional bone using combinations of cells, scaffolds and bioactive factors is a promising strategy and a particular feature for future development in the area of hybrid materials which are able to exhibit suitable biomimetic and mechanical properties. This review will discuss the state of the art in this field and what we can expect from future generations of bone regeneration concepts. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472104/ doi: 10.4248/br201303002 id: cord-277237-tjsw205c author: Hernandez Vargas, Esteban Abelardo title: In-host Modelling of COVID-19 Kinetics in Humans date: 2020-03-30 words: 3609.0 sentences: 273.0 pages: flesch: 58.0 cache: ./cache/cord-277237-tjsw205c.txt txt: ./txt/cord-277237-tjsw205c.txt summary: Based on the target cell model, COVID-19 infecting time between susceptible cells (mean of 30 days approximately) is much slower than those reported for Ebola (about 3 times slower) and influenza (60 times slower). The best model to fit the data was including immune responses, which suggest a slow cell response peaking between 5 to 10 days post onset of symptoms. [29] improve the fitting respect to the target cell model (Table 2 ) even when very long eclipse phase periods 121 are assumed (e.g 100 days), implying that this mechanism could be negligible on COVID-19 infection. Here, based on the results of the 159 target cell model in Table 2 , we found that COVID-19 infecting time between cells (mean of 30 days 160 approximately) would be slower than those reported for Ebola (about 3 times slower) and influenza (60 161 times slower). Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses abstract: COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat for human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. In this work, several mathematical models are proposed to represent COVID-19 dynamics in infected patients. Considering different starting times of infection, parameters sets that represent infectivity of COVID-19 are computed and compared with other viral infections that can also cause pandemics. Based on the target cell model, COVID-19 infecting time between susceptible cells (mean of 30 days approximately) is much slower than those reported for Ebola (about 3 times slower) and influenza (60 times slower). The within-host reproductive number for COVID-19 is consistent to the values of influenza infection (1.7-5.35). The best model to fit the data was including immune responses, which suggest a slow cell response peaking between 5 to 10 days post onset of symptoms. The model with eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, both, the target cell model and the model with immune responses, predict that virus may replicate very slowly in the first days after infection, and it could be below detection levels during the first 4 days post infection. A quantitative comprehension of COVID-19 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. url: https://doi.org/10.1101/2020.03.26.20044487 doi: 10.1101/2020.03.26.20044487 id: cord-103502-asphso2s author: Herrgårdh, Tilda title: An organ-based multi-level model for glucose homeostasis: organ distributions, timing, and impact of blood flow date: 2020-10-21 words: 8160.0 sentences: 409.0 pages: flesch: 60.0 cache: ./cache/cord-103502-asphso2s.txt txt: ./txt/cord-103502-asphso2s.txt summary: Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. However, neither this model, nor any of the previously mentioned multi-level models, have subdivided the glucose uptake into the individual contributions of all of the main insulin-responding and glucose-utilizing organs: adipose tissue, muscle, and liver. The final combined model (Q4) can fit to all of the new data for glucose uptake in all organs (Fig 6) , as well as to all previous data, such as the postprandial glucose and insulin fluxes and concentrations in (Dalla Man et al. abstract: Glucose homeostasis is the tight control of glucose in the blood. This complex control is important and not yet sufficiently understood, due to its malfunction in serious diseases like diabetes. Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. Over the last 10 years, this model has been used to insert new insights from the intra-cellular level into the larger whole-body perspective. However, the original cell-organ-body translation has during these years never been updated, despite several critical shortcomings, which also have not been resolved by other modelling efforts. For this reason, we here present an updated multi-level model. This model provides a more accurate sub-division of how much glucose is being taken up by the different organs. Unlike the original model, we now also account for the different dynamics seen in the different organs. The new model also incorporates the central impact of blood flow on insulin-stimulated glucose uptake. Each new improvement is clear upon visual inspection, and they are also supported by statistical tests. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. We hope that this model will serve as an improved basis for future data integration, useful for research and drug developments within diabetes. url: https://doi.org/10.1101/2020.10.21.344499 doi: 10.1101/2020.10.21.344499 id: cord-315462-u2dj79yw author: Hewitt, Judith A. title: ACTIVating Resources for the COVID-19 Pandemic: In vivo Models for Vaccines and Therapeutics date: 2020-10-01 words: 8953.0 sentences: 469.0 pages: flesch: 44.0 cache: ./cache/cord-315462-u2dj79yw.txt txt: ./txt/cord-315462-u2dj79yw.txt summary: The selection of appropriate animal models of infection, disease manifestation, and efficacy measurements is important for vaccines and therapeutics to be compared under ACTIV''s umbrella using Master Protocols with standardized endpoints and assay readouts. Models of SARS-CoV-2 infection include mice (ACE2 transgenic strains, mouse adapted virus, and AAV transduced ACE2 mice), hamsters, rats, ferrets and non-human primates (NHPs). Following infection by the intranasal route, golden Syrian Hamsters demonstrate clinical features, viral kinetics, histopathological changes, and immune responses that closely mimic the mild to moderate disease described in human COVID-19 patients (Chan et al., 2020b; Imai et al., 2020; Sia et al., 2020) . In an initial study of SARS-CoV-2 infection of hACE2-hamsters, clinical signs were observed including elevated body temperatures, slow or reduced mobility, weight loss and mortality (1 out of 4 animals). Human angiotensin-converting enzyme 2 transgenic mice infected with SARS-CoV-2 develop severe and fatal respiratory disease. abstract: The Preclinical Working Group of Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV), a public-private partnership spearheaded by the National Institutes of Health, was charged with identifying, prioritizing, and communicating SARS-CoV-2 preclinical resources. Reviewing SARS-CoV-2 animal model data facilitates standardization and harmonization and informs knowledge gaps and prioritization of limited resources. To date, mouse, hamster, ferret, guinea pig, and non-human primates have been investigated. Several species are permissive for SARS-CoV-2 replication, often exhibiting mild disease with resolution, reflecting most human COVID-19 cases. More severe disease develops in a few models, some associated with advanced age, a risk factor for human disease. This review provides a snapshot that recommends the suitability of models for testing vaccines and therapeutics, which may evolve as our understanding of COVID-19 disease biology improves. COVID-19 is a complex disease and individual models recapitulate certain aspects of disease; therefore, the coordination and assessment of animal models is imperative. url: https://www.sciencedirect.com/science/article/pii/S1931312820305217?v=s5 doi: 10.1016/j.chom.2020.09.016 id: cord-265299-oovkoiyj author: Hickman, D.L. title: Commonly Used Animal Models date: 2016-11-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This chapter provides an introduction to animals that are commonly used for research. It presents information on basic care topics such as biology, behavior, housing, feeding, sexing, and breeding of these animals. The chapter provides some insight into the reasons why these animals are used in research. It also gives an overview of techniques that can be utilized to collect blood or to administer drugs or medicine. Each section concludes with a brief description of how to recognize abnormal signs, in addition to lists of various diseases. url: https://www.sciencedirect.com/science/article/pii/B9780128021514000074 doi: 10.1016/b978-0-12-802151-4.00007-4 id: cord-342591-6joc2ld1 author: Higazy, M. title: Novel Fractional Order SIDARTHE Mathematical Model of The COVID-19 Pandemic date: 2020-06-13 words: 3689.0 sentences: 247.0 pages: flesch: 47.0 cache: ./cache/cord-342591-6joc2ld1.txt txt: ./txt/cord-342591-6joc2ld1.txt summary: The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. In addition, we study an optimal control plans for the fractional order SIDARTHE model via four control strategies that include the availability of vaccination and existence of treatments for the infected detected three population fraction phases. Applying the fractional order differential equations numerical solver using MATLAB software, we show the dynamics of the state variables of the model and display the effect of changing the fractional derivative order on the system response. We also implement the optimal control strategies numerically for the fractional order SIDARTHE model. Figure 9 displays the phase plane of state variables: total infected ( ) and susceptible cases (S(t)) with different fractional derivative order . abstract: Nowadays, COVID-19 has put a significant responsibility on all of us around the world from its detection to its remediation. The globe suffer from lockdown due to COVID-19 pandemic. The researchers are doing their best to discover the nature of this pandemic and try to produce the possible plans to control it. One of the most effective method to understand and control the evolution of this pandemic is to model it via an efficient mathematical model. In this paper, we propose to model the COVID-19 pandemic by fractional order SIDARTHE model which not appear in the literature before. The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. The sensitivity of the fractional order COVID-19 SIDARTHE model to the fractional order and the infection rate parameters are displayed. All studies are numerically simulated using MATLAB software via fractional order differential equation solver. url: https://www.ncbi.nlm.nih.gov/pubmed/32565624/ doi: 10.1016/j.chaos.2020.110007 id: cord-254107-02bik024 author: Hillisch, Alexander title: Utility of homology models in the drug discovery process date: 2004-08-31 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Abstract Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed. url: https://api.elsevier.com/content/article/pii/S1359644604031964 doi: 10.1016/s1359-6446(04)03196-4 id: cord-318079-jvx1rh7g author: Hinch, R. title: OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing date: 2020-09-22 words: 5363.0 sentences: 283.0 pages: flesch: 45.0 cache: ./cache/cord-318079-jvx1rh7g.txt txt: ./txt/cord-318079-jvx1rh7g.txt summary: The ABM was developed to simulate different non-pharmaceutical interventions including lockdown, physical distancing, self-isolation on symptoms, testing and contact tracing. A previous study of social contacts for infectious disease modelling, based on participants being asked to recall their interactions over the past day, has estimated the mean number of interactions that individuals have by age group [12] . We present OpenABM-Covid19, a COVID-19-specific agent-based model suitable for simulating the epidemic in different settings and assessing non-pharmaceutical interventions, including contact tracing using a mobile phone app. Further, on developing symptoms or during interventions such as contact tracing, the interaction pattern of individuals change to only include those in the household. One of the key aims of OpenABM-Covid19 was to model non-pharmaceutical interventions and, in particular, different forms of contact tracing. OpenABM-Covid19 is a versatile tool to model the COVID-19 epidemic in different settings and simulate different non-pharmaceutical interventions including contact tracing. abstract: SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic. url: https://doi.org/10.1101/2020.09.16.20195925 doi: 10.1101/2020.09.16.20195925 id: cord-263987-ff6kor0c author: Holmes, Ian H. title: Solving the master equation for Indels date: 2017-05-12 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: BACKGROUND: Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events. MAIN TEXT: This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees. CONCLUSIONS: While approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances. url: https://www.ncbi.nlm.nih.gov/pubmed/28494756/ doi: 10.1186/s12859-017-1665-1 id: cord-311432-js84ruve author: Hossein Rashidi, T. title: Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment date: 2020-04-29 words: 4238.0 sentences: 194.0 pages: flesch: 48.0 cache: ./cache/cord-311432-js84ruve.txt txt: ./txt/cord-311432-js84ruve.txt summary: The classical Susceptible-[Exposed]-Infected-Recovered (SEIR/SIR) epidemic models [4] , have 15 been widely developed to simulate the transmission dynamics of COVID 19 [5, 6] and the impact of non-therapeutic interventions -e.g., travel and border restrictions [7, 8] , quarantines and isolations [5, [9] [10] [11] , or social distancing and closure of facilities-on the spread of the outbreak, and in some cases, on the healthcare demand [5, 9, [11] [12] [13] .These studies have been mostly focused on calibrating models for a specific country/region based on the data at the time 20 of the model-development and assuming a multitude of parameters initialized upon prior knowledge such as social contact structure, rate of compliance with the policy and incubation or infection period among others. abstract: Substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy. url: https://doi.org/10.1101/2020.04.24.20078923 doi: 10.1101/2020.04.24.20078923 id: cord-310844-7i92mk4x author: Hryhorowicz, Magdalena title: Application of Genetically Engineered Pigs in Biomedical Research date: 2020-06-19 words: 9011.0 sentences: 475.0 pages: flesch: 37.0 cache: ./cache/cord-310844-7i92mk4x.txt txt: ./txt/cord-310844-7i92mk4x.txt summary: Animal studies are conducted to develop models used in gene function and regulation research and the genetic determinants of certain human diseases. Short pregnancy, short generation interval, and high litter size make the production of transgenic pigs less time-consuming in comparison with other livestock species This review describes genetically modified pigs used for biomedical research and the future challenges and perspectives for the use of the swine animal models. It was demonstrated that precise integration of the human CFTR gene at a porcine safe harbor locus through CRISPR/Cas9-induced HDR-mediated knock-in allowed the achievement of persistent in vitro expression of the transgene in transduced cells. The study showed that multiple genetically modified porcine hearts were protected from complement activation and myocardial natural killer cell infiltration in an ex vivo perfusion model with human blood [86] . Biomedical applications for which genetically engineered pigs are generated include modeling human diseases, production of pharmaceutical proteins, and xenotransplantation. abstract: Progress in genetic engineering over the past few decades has made it possible to develop methods that have led to the production of transgenic animals. The development of transgenesis has created new directions in research and possibilities for its practical application. Generating transgenic animal species is not only aimed towards accelerating traditional breeding programs and improving animal health and the quality of animal products for consumption but can also be used in biomedicine. Animal studies are conducted to develop models used in gene function and regulation research and the genetic determinants of certain human diseases. Another direction of research, described in this review, focuses on the use of transgenic animals as a source of high-quality biopharmaceuticals, such as recombinant proteins. The further aspect discussed is the use of genetically modified animals as a source of cells, tissues, and organs for transplantation into human recipients, i.e., xenotransplantation. Numerous studies have shown that the pig (Sus scrofa domestica) is the most suitable species both as a research model for human diseases and as an optimal organ donor for xenotransplantation. Short pregnancy, short generation interval, and high litter size make the production of transgenic pigs less time-consuming in comparison with other livestock species This review describes genetically modified pigs used for biomedical research and the future challenges and perspectives for the use of the swine animal models. url: https://doi.org/10.3390/genes11060670 doi: 10.3390/genes11060670 id: cord-000282-phepjf55 author: Hsieh, Ying-Hen title: On epidemic modeling in real time: An application to the 2009 Novel A (H1N1) influenza outbreak in Canada date: 2010-11-05 words: 4027.0 sentences: 166.0 pages: flesch: 48.0 cache: ./cache/cord-000282-phepjf55.txt txt: ./txt/cord-000282-phepjf55.txt summary: BACKGROUND: Management of emerging infectious diseases such as the 2009 influenza pandemic A (H1N1) poses great challenges for real-time mathematical modeling of disease transmission due to limited information on disease natural history and epidemiology, stochastic variation in the course of epidemics, and changing case definitions and surveillance practices. We sought to address three critical issues in real time disease modeling for newly emerged 2009 pH1N1: (i) to estimate the basic reproduction number; (ii) to identify the main turning points in the epidemic curve that distinguish different phases or waves of disease; and (iii) to predict the future course of events, including the final size of the outbreak in the absence of intervention. We fit both the single-and multi-phase Richards models to Canadian cumulative 2009 pH1N1 cumulative case data, using publicly available disease onset dates obtained from the Public Health Agency of Canada (PHAC) website [10, 11] . abstract: BACKGROUND: Management of emerging infectious diseases such as the 2009 influenza pandemic A (H1N1) poses great challenges for real-time mathematical modeling of disease transmission due to limited information on disease natural history and epidemiology, stochastic variation in the course of epidemics, and changing case definitions and surveillance practices. FINDINGS: The Richards model and its variants are used to fit the cumulative epidemic curve for laboratory-confirmed pandemic H1N1 (pH1N1) infections in Canada, made available by the Public Health Agency of Canada (PHAC). The model is used to obtain estimates for turning points in the initial outbreak, the basic reproductive number (R(0)), and for expected final outbreak size in the absence of interventions. Confirmed case data were used to construct a best-fit 2-phase model with three turning points. R(0 )was estimated to be 1.30 (95% CI 1.12-1.47) for the first phase (April 1 to May 4) and 1.35 (95% CI 1.16-1.54) for the second phase (May 4 to June 19). Hospitalization data were also used to fit a 1-phase model with R(0 )= 1.35 (1.20-1.49) and a single turning point of June 11. CONCLUSIONS: Application of the Richards model to Canadian pH1N1 data shows that detection of turning points is affected by the quality of data available at the time of data usage. Using a Richards model, robust estimates of R(0 )were obtained approximately one month after the initial outbreak in the case of 2009 A (H1N1) in Canada. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2989981/ doi: 10.1186/1756-0500-3-283 id: cord-330596-p4k7jexz author: Hu, Ji title: An integrated classification model for incremental learning date: 2020-10-21 words: 4518.0 sentences: 252.0 pages: flesch: 51.0 cache: ./cache/cord-330596-p4k7jexz.txt txt: ./txt/cord-330596-p4k7jexz.txt summary: However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. This method consists of two parts: a pre-trained (Pt) model and a novel Truncated Gradient Confidence-weighted online classification model (TGCW). Online learning is a continuous training process in which input values are fed into the model in each round of training, and the model outputs prediction results based on the current parameters [16] . In this section, we propose a new online learning algorithm suitable for binary classification of streamed data, named TGCW, which aims to further improve the prediction accuracy and feature selection capability of the model. In addition, we will also look for improved pre-trained models or use more classifiers for integrated learning to improve the classification accuracy of complex data. abstract: Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts. url: https://www.ncbi.nlm.nih.gov/pubmed/33106746/ doi: 10.1007/s11042-020-10070-w id: cord-102850-0kiypige author: Huang, C.-C. title: A Machine Learning Study to Improve Surgical Case Duration Prediction date: 2020-06-12 words: 4728.0 sentences: 252.0 pages: flesch: 53.0 cache: ./cache/cord-102850-0kiypige.txt txt: ./txt/cord-102850-0kiypige.txt summary: The results are reported in 225 In Fig. 3 , we plotted scatter plots of actual versus predicted duration on the external 234 testing set for the average models of surgeon-and procedure-specific, and the XGB 235 model. Moreover, 251 three of the features which we computed from surgeons'' data (i.e. total surgical minutes 252 performed by the surgeon within the last 7 days and on the same day, and number of Accurate prediction of operation case duration is vital in elevating OR efficiency and 257 reducing cost. It has been reported in the past studies that primary surgeons contributed the 301 largest variability in operation case duration prediction compared to other factors 302 attributed to patients [2, 16, 23] . 356 We propose extracting additional information from operation and surgeons'' data to 357 be used as predictor variables for ML algorithm training since their importance was 358 high in the XGB model. abstract: Predictive accuracy of surgical case duration plays a critical role in reducing cost of operation room (OR) utilization. The most common approaches used by hospitals rely on historic averages based on a specific surgeon or a specific procedure type obtained from the electronic medical record (EMR) scheduling systems. However, low predictive accuracy of EMR leads to negative impacts on patients and hospitals, such as rescheduling of surgeries and cancellation. In this study, we aim to improve prediction of operation case duration with advanced machine learning (ML) algorithms. We obtained a large data set containing 170,748 operation cases (from Jan 2017 to Dec 2019) from a hospital. The data covered a broad variety of details on patients, operations, specialties and surgical teams. Meanwhile, a more recent data with 8,672 cases (from Mar to Apr 2020) was also available to be used for external evaluation. We computed historic averages from EMR for surgeon- or procedure-specific and they were used as baseline models for comparison. Subsequently, we developed our models using linear regression, random forest and extreme gradient boosting (XGB) algorithms. All models were evaluated with R-squre (R^2), mean absolute error (MAE), and percentage overage (case duration > prediction + 10 % & 15 mins), underage (case duration < prediction - 10 % & 15 mins) and within (otherwise). The XGB model was superior to the other models by having higher R^2 (85 %) and percentage within (48 %) as well as lower MAE (30.2 mins). The total prediction errors computed for all the models showed that the XGB model had the lowest inaccurate percent (23.7 %). As a whole, this study applied ML techniques in the field of OR scheduling to reduce medical and financial burden for healthcare management. It revealed the importance of operation and surgeon factors in operation case duration prediction. This study also demonstrated the importance of performing an external evaluation to better validate performance of ML models. url: http://medrxiv.org/cgi/content/short/2020.06.10.20127910v1?rss=1 doi: 10.1101/2020.06.10.20127910 id: cord-262966-8b1esll4 author: Huang, Ganyu title: Prediction of COVID-19 Outbreak in China and Optimal Return Date for University Students Based on Propagation Dynamics date: 2020-04-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: On 12 December 2019, a novel coronavirus disease, named COVID-19, began to spread around the world from Wuhan, China. It is useful and urgent to consider the future trend of this outbreak. We establish the 4+1 penta-group model to predict the development of the COVID-19 outbreak. In this model, we use the collected data to calibrate the parameters, and let the recovery rate and mortality change according to the actual situation. Furthermore, we propose the BAT model, which is composed of three parts: simulation of the return rush (Back), analytic hierarchy process (AHP) method, and technique for order preference by similarity to an ideal solution (TOPSIS) method, to figure out the best return date for university students. We also discuss the impacts of some factors that may occur in the future, such as secondary infection, emergence of effective drugs, and population flow from Korea to China. url: https://www.ncbi.nlm.nih.gov/pubmed/32288415/ doi: 10.1007/s12204-020-2167-2 id: cord-184685-ho72q46e author: Huang, Tongtong title: Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates date: 2020-08-10 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materials and Methods: We collected datasets of daily reported hospitalization and cumulative morality of COVID 19 in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council COVID 19 report, TMC daily news, and New York Times county level mortality reporting). Our model, risk stratified SIR HCD uses separate variables to model the dynamics of local contact (e.g., work from home) and high contact (e.g., work on site) subpopulations while sharing parameters to control their respective $R_0(t)$ over time. Results: We evaluated our models forecasting performance in Harris County, TX (the most populated county in the Greater Houston area) during the Phase I and Phase II reopening. Not only did our model outperform other competing models, it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. Discussion: Local mortality and hospitalization are significantly impacted by quarantine and reopening policies. No existing model has directly accounted for the effect of these policies on local trends in infections, hospitalizations, and deaths in an explicit and explainable manner. Our work is an attempt to close this important technical gap to support decision making. Conclusion: Despite several limitations, we think it is a timely effort to rethink about how to best model the dynamics of pandemics under the influence of reopening policies. url: https://arxiv.org/pdf/2008.05909v1.pdf doi: nan id: cord-319291-6l688krc author: Hung, Chun-Min title: Alignment using genetic programming with causal trees for identification of protein functions date: 2006-09-01 words: 8941.0 sentences: 632.0 pages: flesch: 52.0 cache: ./cache/cord-319291-6l688krc.txt txt: ./txt/cord-319291-6l688krc.txt summary: Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. The hybrid model, namely Alignment using Genetic programming with Causal Tree (AGCT), is a heuristic evolutionary method that contains three basic components: (i) genetic programming with innerexchanged individual strategy, (ii) causal trees [4, 28, 31] with probabilistic reasoning, and (iii) construction of hierarchical homologies with local block-to-block alignment using the methods of moment invariant and robust points matching (RPM) [24] . abstract: A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model. url: https://www.sciencedirect.com/science/article/pii/S0362546X05009028 doi: 10.1016/j.na.2005.09.048 id: cord-119104-9d421si9 author: Huynh, Tin Van title: BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models date: 2020-09-06 words: 1816.0 sentences: 134.0 pages: flesch: 63.0 cache: ./cache/cord-119104-9d421si9.txt txt: ./txt/cord-119104-9d421si9.txt summary: title: BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models In this article, we present our approach at WNUT-2020 Task 2 to identify Tweets containing information about COVID-19 on the social networking platform Twitter or not. • Firstly, we implemented four different models based on neural networks and transformers such as Bi-GRU-CNN, BERT, RoBERTa, XLNet to solve the WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. In this paper, we propose an ensemble method that combines the deep learning models with the transfer learning models to identify information about COVID-19 from users'' tweets. In this paper, we used the SOTA transfer learning models, such as BERT (Devlin et al., 2019) , RoBERTa (Liu et al., 2019) , and XLNet (Yang et al., 2019) with fine-tuning techniques for the problem of identifying informative tweet about COVID-19. abstract: The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set. url: https://arxiv.org/pdf/2009.02671v1.pdf doi: nan id: cord-332412-lrn0wpvj author: Ibrahim, Mohamed R. title: Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe date: 2020-04-24 words: 6819.0 sentences: 309.0 pages: flesch: 56.0 cache: ./cache/cord-332412-lrn0wpvj.txt txt: ./txt/cord-332412-lrn0wpvj.txt summary: Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe. Relying on deep learning, we introduce a novel variational Long-Short Term Memory (LSTM) autoencoder model to forecast the spread of coronavirus per country across the globe. The main advantages of the proposed method are: 1) It can structure and learns from different data sources, either that belongs to spatial adjacency, urban and population factors, or various historical related data, 2) the model is flexible to apply to different scales, in which currently, it can provide prediction at global and country scales, however, it can be also applied to city level. abstract: Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational LSTM-Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represent the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe. url: https://doi.org/10.1101/2020.04.20.20070938 doi: 10.1101/2020.04.20.20070938 id: cord-325321-37kyd8ak author: Iftikhar, H. title: Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study date: 2020-09-22 words: 2618.0 sentences: 144.0 pages: flesch: 57.0 cache: ./cache/cord-325321-37kyd8ak.txt txt: ./txt/cord-325321-37kyd8ak.txt summary: title: Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study In this work, we used five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple Exponential Smoothing (SES) models for forecasting confirmed, death and recovered cases. The findings show that the time series models are useful in predicting COVID-19 confirmed, deaths and recovered cases. In this work, the COVID-19 confirmed, deaths and recovered counts times series are plotted in Figure 1 (left-column) daily and Figure 1 (right-column) cumulative cases. The main purpose of this work was to forecast confirmed, deaths and recovered cases of COVID-19 for Pakistan using five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple exponential smoothing (SES) models. abstract: The increasing confirmed cases and death counts of Coronavirus disease 2019 (COVID-19) in Pakistan has disturbed not only the health sector, but also all other sectors of the country. For precise policy making, accurate and efficient forecasts of confirmed cases and death counts are important. In this work, we used five different univariate time series models including; Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Nonparametric Autoregressive (NPAR) and Simple Exponential Smoothing (SES) models for forecasting confirmed, death and recovered cases. These models were applied to Pakistan COVID-19 data, covering the period from 10, March to 3, July 2020. To evaluate models accuracy, computed two standard mean errors such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The findings show that the time series models are useful in predicting COVID-19 confirmed, deaths and recovered cases. Furthermore, MA model outperformed the rest of all models for confirmed and deaths counts prediction, while ARMA is second best model. The SES model seems superior to other models for prediction of recovered counts, however MA is competitive. On the basis of best selected models, we forecast form 4th July to 14th August, 2020, which will be helpful for decision making of public health and other sectors of Pakistan. url: http://medrxiv.org/cgi/content/short/2020.09.20.20198150v1?rss=1 doi: 10.1101/2020.09.20.20198150 id: cord-350870-a89zj5mh author: Ikeda, Hiroki title: Improving the estimation of the death rate of infected cells from time course data during the acute phase of virus infections: application to acute HIV-1 infection in a humanized mouse model date: 2014-05-21 words: 5059.0 sentences: 261.0 pages: flesch: 54.0 cache: ./cache/cord-350870-a89zj5mh.txt txt: ./txt/cord-350870-a89zj5mh.txt summary: Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. To estimate the accuracy of the parameters estimated by our two novel models, we created simulated time course data of target cell densities and viral load during the acute phase of viral infection (lasting approximately 21 days [14] [15] [16] [17] [18] [19] [20] [26] [27] [28] ) assuming biologically plausible parameter values. We created artificial data with target cell densities and virus loads during acute infection using the reduced standard model for viral infection (i.e., Eqs. abstract: BACKGROUND: Mathematical modeling of virus dynamics has provided quantitative insights into viral infections such as influenza, the simian immunodeficiency virus/human immunodeficiency virus, hepatitis B, and hepatitis C. Through modeling, we can estimate the half-life of infected cells, the exponential growth rate, and the basic reproduction number (R(0)). To calculate R(0) from virus load data, the death rate of productively infected cells is required. This can be readily estimated from treatment data collected during the chronic phase, but is difficult to determine from acute infection data. Here, we propose two new models that can reliably estimate the average life span of infected cells from acute-phase data, and apply both methods to experimental data from humanized mice infected with HIV-1. METHODS: Both new models, called as the reduced quasi-steady state (RQS) model and the piece-wise regression (PWR) model, are derived by simplification of a standard model for the acute-phase dynamics of target cells, viruses and infected cells. By having only a limited number of parameters, both models allow us to reliably estimate the death rate of productively infected cells. Simulated datasets with plausible parameter values are generated with the standard model to compare the performance of the new models with that of the major previous model (i.e., the simple exponential model). Finally, we fit models to time course data from HIV-1 infected humanized mice to estimate the several important parameters characterizing their acute infection. RESULTS AND CONCLUSIONS: The new models provided much better estimates than the previous model because they more precisely capture the de novo infection process. Both models describe the acute phase of HIV-1 infected humanized mice reasonably well, and we estimated an average death rate of infected cells of 0.61 and 0.61, an average exponential growth rate of 0.69 and 0.76, and an average basic reproduction number of 2.30 and 2.38 in the RQS model and the PWR model, respectively. These estimates are fairly close to those obtained in humans. url: https://doi.org/10.1186/1742-4682-11-22 doi: 10.1186/1742-4682-11-22 id: cord-293562-69nnyq8p author: Imran, Mudassar title: Mathematical analysis of the role of hospitalization/isolation in controlling the spread of Zika fever date: 2018-08-15 words: 5874.0 sentences: 365.0 pages: flesch: 55.0 cache: ./cache/cord-293562-69nnyq8p.txt txt: ./txt/cord-293562-69nnyq8p.txt summary: We consider a deterministic model for the transmission dynamics of the Zika virus infectious disease that spreads in, both humans and vectors, through horizontal and vertical transmission. We consider a deterministic model for the transmission dynamics of the Zika virus infectious disease that spreads in, both humans and vectors, through horizontal and vertical transmission. An in-depth stability analysis of the model is performed, and it is consequently shown, that the model has a globally asymptotically stable disease-free equilibrium when the basic reproduction number R 0 < 1. An in-depth stability analysis of the model is performed, and it is consequently shown, that the model has a globally asymptotically stable disease-free equilibrium when the basic reproduction number R 0 < 1. Since the only way to control the disease is to isolate patients who have been infected with the Zika virus, we included a new population compartment consisting of hospitalized individuals. abstract: The Zika virus is transmitted to humans primarily through Aedes mosquitoes and through sexual contact. It is documented that the virus can be transmitted to newborn babies from their mothers. We consider a deterministic model for the transmission dynamics of the Zika virus infectious disease that spreads in, both humans and vectors, through horizontal and vertical transmission. The total populations of both humans and mosquitoes are assumed to be constant. Our models consist of a system of eight differential equations describing the human and vector populations during the different stages of the disease. We have included the hospitalization/isolation class in our model to see the effect of the controlling strategy. We determine the expression for the basic reproductive number R(0) in terms of horizontal as well as vertical disease transmission rates. An in-depth stability analysis of the model is performed, and it is consequently shown, that the model has a globally asymptotically stable disease-free equilibrium when the basic reproduction number R(0) < 1. It is also shown that when R(0) > 1, there exists a unique endemic equilibrium. We showed that the endemic equilibrium point is globally asymptotically stable when it exists. We were able to prove this result in a reduced model. Furthermore, we conducted an uncertainty and sensitivity analysis to recognize the impact of crucial model parameters on R(0). The uncertainty analysis yields an estimated value of the basic reproductive number R(0) = 1.54. Assuming infection prevalence in the population under constant control, optimal control theory is used to devise an optimal hospitalization/isolation control strategy for the model. The impact of isolation on the number of infected individuals and the accumulated cost is assessed and compared with the constant control case. url: https://www.ncbi.nlm.nih.gov/pubmed/30003923/ doi: 10.1016/j.virusres.2018.07.002 id: cord-340805-qbvgnr4r author: Ioannidis, John P.A. title: Forecasting for COVID-19 has failed date: 2020-08-25 words: 6084.0 sentences: 313.0 pages: flesch: 56.0 cache: ./cache/cord-340805-qbvgnr4r.txt txt: ./txt/cord-340805-qbvgnr4r.txt summary: Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence. abstract: Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, looking at only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence. url: https://doi.org/10.1016/j.ijforecast.2020.08.004 doi: 10.1016/j.ijforecast.2020.08.004 id: cord-209221-vjfmxsks author: Ishiguro, Katsuhiko title: Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis date: 2020-10-02 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Retrosynthesis is a problem to infer reactant compounds to synthesize a given product compound through chemical reactions. Recent studies on retrosynthesis focus on proposing more sophisticated prediction models, but the dataset to feed the models also plays an essential role in achieving the best generalizing models. Generally, a dataset that is best suited for a specific task tends to be small. In such a case, it is the standard solution to transfer knowledge from a large or clean dataset in the same domain. In this paper, we conduct a systematic and intensive examination of data transfer approaches on end-to-end generative models, in application to retrosynthesis. Experimental results show that typical data transfer methods can improve test prediction scores of an off-the-shelf Transformer baseline model. Especially, the pre-training plus fine-tuning approach boosts the accuracy scores of the baseline, achieving the new state-of-the-art. In addition, we conduct a manual inspection for the erroneous prediction results. The inspection shows that the pre-training plus fine-tuning models can generate chemically appropriate or sensible proposals in almost all cases. url: https://arxiv.org/pdf/2010.00792v1.pdf doi: nan id: cord-018976-0ndb7rm2 author: Iwasa, Yoh title: Mathematical Studies of Dynamics and Evolution of Infectious Diseases date: 2007 words: 1796.0 sentences: 109.0 pages: flesch: 48.0 cache: ./cache/cord-018976-0ndb7rm2.txt txt: ./txt/cord-018976-0ndb7rm2.txt summary: Mathematical modeling of infectious diseases is the most advanced subfield of theoretical studies in biology and the life sciences. The papers included in this volume are for mathematical studies of models on infectious diseases and cancer. This introductory chapter is followed by four papers on infectious disease dynamics, in which the roles of time delay (Chaps. Then, there are two chapters that discuss competition between strains and evolution occurring in the host population (Chap. By considering the appearance of novel strains with different properties from those of the resident population of pathogens, and tracing their abundance, we can discuss the evolutionary dynamics of infectious diseases. Iwasa and his colleagues derive a result that, without cross-immunity among strains, the pathogenicity of the disease tends to increase by any evolutionary changes. Beretta and his colleagues summarize their study of modeling of an immune system dynamics in which time delay is incorporated. abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7124004/ doi: 10.1007/978-3-540-34426-1_1 id: cord-340827-vx37vlkf author: Jackson, Matthew O. title: Chapter 14 Diffusion, Strategic Interaction, and Social Structure date: 2011-12-31 words: 13725.0 sentences: 754.0 pages: flesch: 56.0 cache: ./cache/cord-340827-vx37vlkf.txt txt: ./txt/cord-340827-vx37vlkf.txt summary: Seminal studies by Ryan and Gross (1943) and Griliches (1957) examined the effects of social connections on the adoption of a new behavior, specifically the adoption of hybrid corn in the U.S. Looking at aggregate adoption rates in different states, these authors illustrated that the diffusion of hybrid corn followed an S-shape curve over time: starting out slowly, accelerating, and then ultimately decelerating. The shape of the distribution F determines which equilibria are tipping points: equilibria such that only a slight addition to the fraction of agents choosing the action 1 shifts the population, under the best response dynamics, to the next higher equilibrium level of adoption (we return to a discussion of tipping and stable points when we consider a more general model of strategic interactions on networks below). While the above models provide some ideas about how social structure impacts diffusion, they are limited to settings where, roughly speaking, the probability that a given individual adopts a behavior is simply proportional to the infection rate of neighbors. abstract: Abstract We provide an overview and synthesis of the literature on how social networks influence behaviors, with a focus on diffusion. We discuss some highlights from the empirical literature on the impact of networks on behaviors and diffusion. We also discuss some of the more prominent models of network interactions, including recent advances regarding interdependent behaviors, modeled via games on networks. JEL Classification Codes: D85, C72, L14, Z13 url: https://api.elsevier.com/content/article/pii/B9780444531872000140 doi: 10.1016/b978-0-444-53187-2.00014-0 id: cord-299852-t0mqe7yy author: Janssen, Loes H. C. title: Does the COVID-19 pandemic impact parents’ and adolescents’ well-being? An EMA-study on daily affect and parenting date: 2020-10-16 words: 8570.0 sentences: 476.0 pages: flesch: 51.0 cache: ./cache/cord-299852-t0mqe7yy.txt txt: ./txt/cord-299852-t0mqe7yy.txt summary: In this ecological momentary assessment study, we investigated if the COVID-19 pandemic affected positive and negative affect of parents and adolescents and parenting behaviors (warmth and criticism). However, Intolerance of uncertainty, nor any pandemic related characteristics (i.e. living surface, income, relatives with COVID-19, hours of working at home, helping children with school and contact with COVID-19 patients at work) were linked to the increase of parents'' negative affect during COVID-19. In addition, we asked parents and adolescents about daily difficulties and helpful activities during the COVID-19 pandemic that possibly influenced their affect in positive and negative ways. During the COVID-19 pandemic, the most reported daily difficulties across the 14 days of EMA for parents were (1) missing social contact with friends (14.6%), (2) concerns about the coronavirus in general (13.5%), (3) irritations with family members (12.8%), (4) worrying about health of others (8.3%), and (5) coronavirus-related news items (8.0%). abstract: Due to the COVID- 19 outbreak in the Netherlands (March 2020) and the associated social distancing measures, families were enforced to stay at home as much as possible. Adolescents and their families may be particularly affected by this enforced proximity, as adolescents strive to become more independent. Yet, whether these measures impact emotional well-being in families with adolescents has not been examined. In this ecological momentary assessment study, we investigated if the COVID-19 pandemic affected positive and negative affect of parents and adolescents and parenting behaviors (warmth and criticism). Additionally, we examined possible explanations for the hypothesized changes in affect and parenting. To do so, we compared daily reports on affect and parenting that were gathered during two periods of 14 consecutive days, once before the COVID-19 pandemic (2018–2019) and once during the COVID-19 pandemic. Multilevel analyses showed that only parents’ negative affect increased as compared to the period before the pandemic, whereas this was not the case for adolescents’ negative affect, positive affect and parenting behaviors (from both the adolescent and parent perspective). In general, intolerance of uncertainty was linked to adolescents’ and parents’ negative affect and adolescents’ positive affect. However, Intolerance of uncertainty, nor any pandemic related characteristics (i.e. living surface, income, relatives with COVID-19, hours of working at home, helping children with school and contact with COVID-19 patients at work) were linked to the increase of parents’ negative affect during COVID-19. It can be concluded that on average, our sample (consisting of relatively healthy parents and adolescents) seems to deal fairly well with the circumstances. The substantial heterogeneity in the data however, also suggest that whether or not parents and adolescents experience (emotional) problems can vary from household to household. Implications for researchers, mental health care professionals and policy makers are discussed. url: https://doi.org/10.1371/journal.pone.0240962 doi: 10.1371/journal.pone.0240962 id: cord-353200-5csewb1k author: Jehi, Lara title: Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19 date: 2020-08-11 words: 4344.0 sentences: 226.0 pages: flesch: 40.0 cache: ./cache/cord-353200-5csewb1k.txt txt: ./txt/cord-353200-5csewb1k.txt summary: OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. Hospitalization risk prediction and outcomes in COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0237419 August 11, 2020 2 / 15 ethical restrictions by the Cleveland clinic regulatory bodies including the institutional review Board and legal counsel. We also develop and validate a statistical model that can assist with individualized prediction of hospitalization risk for a patient with COVID-19. abstract: BACKGROUND: Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. SETTING: One healthcare system in Ohio and Florida. PARTICIPANTS: All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. RESULTS: 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886–0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. LIMITATION: Retrospective cohort design. CONCLUSION: Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making. url: https://www.ncbi.nlm.nih.gov/pubmed/32780765/ doi: 10.1371/journal.pone.0237419 id: cord-318562-jif88gof author: Jiménez-Liso, Maria Rut title: Changing How We Teach Acid-Base Chemistry: A Proposal Grounded in Studies of the History and Nature of Science Education date: 2020-08-15 words: 9892.0 sentences: 412.0 pages: flesch: 48.0 cache: ./cache/cord-318562-jif88gof.txt txt: ./txt/cord-318562-jif88gof.txt summary: Controversial moments in science from 1923, when three researchers (Bronsted, Lowry, and Lewis) independently enunciated two theories from two different paradigms (dissociation and valence electron), underpin our first sequence with an explicit NoS approach for both lower secondary school and upper secondary or university levels. In this theoretical article examining teaching practice, we want to focus on the historical development of acid-base theories (Arrhenius, Bronsted-Lowry and Lewis) to analyse the steps to follow to design sequences of activities for different NoS approaches. We examine conventional teaching approaches to the topic and its consequences in terms of students'' alternative conceptions and their difficulties to transfer and apply knowledge and to recognize acid-base models'' limits of applicability. The science education literature is replete with examples of the consequences for students'' learning of this typical way of teaching acid-base content focused on the definition of its concepts and with two or three theories introduced simultaneously. abstract: We propose explicit and implicit approaches for the teaching of acid-base chemistry based on research into the history and nature of science (NoS). To support these instructional proposals, we identify four rationales for students to understand acid-base processes: daily life, socio-scientific, curriculum, and history of science. The extensive bibliography on misconceptions at all educational levels justifies the need for a change from the usual pedagogical approaches to teaching the acid-base domain (traditionally involving conceptual-focused teaching) to a deeper and more meaningful approach that provides (implicitly or explicitly) a chance to reflect on how scientific knowledge is constructed. Controversial moments in science from 1923, when three researchers (Bronsted, Lowry, and Lewis) independently enunciated two theories from two different paradigms (dissociation and valence electron), underpin our first sequence with an explicit NoS approach for both lower secondary school and upper secondary or university levels. Our inquiry teaching cycle promotes the transformation of a hands-on activity (using cabbage as an indicator) into an inquiry, and subsequently, we use an historical model to propose a sequence of activities based on the modeling cycle of Couso and Garrido-Espeja for lower secondary school. Finally, we identify some implications for a model-focused teaching approach for upper secondary and university levels using more sophisticated models. url: https://www.ncbi.nlm.nih.gov/pubmed/32836880/ doi: 10.1007/s11191-020-00142-6 id: cord-025348-sh1kehrh author: Jurj, Sorin Liviu title: Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities date: 2020-05-02 words: 7396.0 sentences: 311.0 pages: flesch: 54.0 cache: ./cache/cord-025348-sh1kehrh.txt txt: ./txt/cord-025348-sh1kehrh.txt summary: This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user''s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. abstract: This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251932/ doi: 10.1007/978-3-030-48791-1_4 id: cord-269559-gvvnvcfo author: Kergaßner, Andreas title: Memory-based meso-scale modeling of Covid-19: County-resolved timelines in Germany date: 2020-08-03 words: 4492.0 sentences: 257.0 pages: flesch: 54.0 cache: ./cache/cord-269559-gvvnvcfo.txt txt: ./txt/cord-269559-gvvnvcfo.txt summary: Here, we combine a spatially resolved county-level infection model for Germany with a memory-based integro-differential approach capable of directly including medical data on the course of disease, which is not possible when using traditional SIR-type models. Based on the history of S, other quantities and subgroups can be determined directly from including medical data on the various courses and infectiousness levels of the disease via corresponding integration weights: We distinguish between the states infectious γ I , symptomatic γ S , tested and quarantined γ Q , hospitalized γ H , in intensive care γ ICU , recovered γ R and deceased γ D . Figure 6 shows the model predicted spatial distribution at county resolution of infectious, symptomatic, hospitalized, and patients in intensive care, following from the individual disease courses in Fig. 1 . abstract: The COVID-19 pandemic has led to an unprecedented world-wide effort to gather data, model, and understand the viral spread. Entire societies and economies are desperate to recover and get back to normality. However, to this end accurate models are of essence that capture both the viral spread and the courses of disease in space and time at reasonable resolution. Here, we combine a spatially resolved county-level infection model for Germany with a memory-based integro-differential approach capable of directly including medical data on the course of disease, which is not possible when using traditional SIR-type models. We calibrate our model with data on cumulative detected infections and deaths from the Robert-Koch Institute and demonstrate how the model can be used to obtain county- or even city-level estimates on the number of new infections, hospitality rates and demands on intensive care units. We believe that the present work may help guide decision makers to locally fine-tune their expedient response to potential new outbreaks in the near future. url: https://doi.org/10.1007/s00466-020-01883-5 doi: 10.1007/s00466-020-01883-5 id: cord-033882-uts6wfqw author: Khakharia, Aman title: Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning date: 2020-10-16 words: 5853.0 sentences: 381.0 pages: flesch: 64.0 cache: ./cache/cord-033882-uts6wfqw.txt txt: ./txt/cord-033882-uts6wfqw.txt summary: The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9% ± 3.9% was developed for 10 high population and high density countries. The data on the spread of COVID-19 in the top 10 densely populated countries, viz., India, Bangladesh, the Democratic Republic of Congo, Pakistan, China, Philippines, Germany, Indonesia, Ethiopia, and Nigeria were analyzed. The best outbreak prediction model was selected for each country depending on the accuracy values obtained decisions. Let us represent the Prediction plots for the number of COVID-19 patients that would rise in the next 5 days for some countries, where an exponential increase in the curve is expected or the rise in the cases would remain constant. abstract: The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9% ± 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567006/ doi: 10.1007/s40745-020-00314-9 id: cord-164964-vcxx1s6k author: Kharkwal, Himanshu title: University Operations During a Pandemic: A Flexible Decision Analysis Toolkit date: 2020-10-20 words: 7390.0 sentences: 381.0 pages: flesch: 51.0 cache: ./cache/cord-164964-vcxx1s6k.txt txt: ./txt/cord-164964-vcxx1s6k.txt summary: There exist several models for each of these components developed at different times as the knowledge about the disease evolved, along with available data such as list of courses for Fall 2020, course selections, mask use policy, number of in person courses, and number of students, faculty, and staff on campus. For this study, we analyze the cumulative infected students due to community transmission of COVID-19 in section 3, hence the fraction of agents who leave the system (severe illness or mortality) or get recovered is immaterial for our simulations because neither of the states impact new infections. Although the current focus is on the pandemic operations of a major university, the framework is flexible enough to analyze the spread of infectious diseases involving human interactions in a big campus if any kind, given relevant models and parameters. Figure 6 : Impact of different mask types on cumulative infected students due to the community transmission of COVID-19 within university campus abstract: Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help understand the healthcare burden posed by a pandemic and respond accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons:(i) social contact in colleges are structured and can be engineered for chosen objectives, (ii) the last pandemic to cause such societal disruption was over 100 years ago, when higher education was not a critical part of society, (ii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known, and (iii) today with distance learning, remote operation of an academic institution is possible. Our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent based modeling (ABM) and stochastic network approach, and models the interactions among individual entities, e.g., students, instructors, classrooms, residences, etc. in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enable the administrator to make informed decisions. While current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our ABM approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota's Sunrise Plan is presented. For each decisions made, its impact was assessed, and results used to get a measure of confidence. We believe this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium sized businesses. url: https://arxiv.org/pdf/2010.10112v1.pdf doi: nan id: cord-299312-asc120pn author: Khoshnaw, Sarbaz H.A. title: A Quantitative and Qualitative Analysis of the COVID–19 Pandemic Model date: 2020-05-25 words: 2083.0 sentences: 133.0 pages: flesch: 39.0 cache: ./cache/cord-299312-asc120pn.txt txt: ./txt/cord-299312-asc120pn.txt summary: Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. Interestingly, we identify that 27 transition rates between asymptomatic infected with both reported and unreported 28 symptomatic infected individuals are very sensitive parameters concerning model variables 29 This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. One of the identified key parameters is the transmission rate 515 between asymptomatic infected and reported symptomatic individuals. abstract: Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically. url: https://api.elsevier.com/content/article/pii/S0960077920303313 doi: 10.1016/j.chaos.2020.109932 id: cord-276218-dcg9oq6y author: Kim, Jihoon title: Human organoids: model systems for human biology and medicine date: 2020-07-07 words: 10681.0 sentences: 496.0 pages: flesch: 38.0 cache: ./cache/cord-276218-dcg9oq6y.txt txt: ./txt/cord-276218-dcg9oq6y.txt summary: The use of classical cell line and animal model systems in biomedical research during the late twentieth and early twenty-first centuries has been successful in many areas, such as improving our understanding of cellular signalling pathways, identifying potential drug targets and guiding the design of candidate drugs for pathologies including cancer and infectious disease. The advent of human induced pluripotent stem cell (iPSC) technology and diverse human AdSC culture methods has made it possible, for the first time, to generate laboratory models specific to an individual 32 . A number of studies have used 3D human stem cell-derived systems, including neurosphere culture and brain organoid models, to reveal the effect of ZIKV infection on human brain development 80, 81 . abstract: The historical reliance of biological research on the use of animal models has sometimes made it challenging to address questions that are specific to the understanding of human biology and disease. But with the advent of human organoids — which are stem cell-derived 3D culture systems — it is now possible to re-create the architecture and physiology of human organs in remarkable detail. Human organoids provide unique opportunities for the study of human disease and complement animal models. Human organoids have been used to study infectious diseases, genetic disorders and cancers through the genetic engineering of human stem cells, as well as directly when organoids are generated from patient biopsy samples. This Review discusses the applications, advantages and disadvantages of human organoids as models of development and disease and outlines the challenges that have to be overcome for organoids to be able to substantially reduce the need for animal experiments. url: https://www.ncbi.nlm.nih.gov/pubmed/32636524/ doi: 10.1038/s41580-020-0259-3 id: cord-339649-ppgmmeuz author: Klein, Michael G. title: COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review date: 2020-09-10 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: OBJECTIVE: Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time. METHODS: We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar, and the Google search engine. RESULTS: The search strategy identified 690 articles. For a detailed review, we selected 6 models that met our predefined criteria. Half of the models did not include age-stratified parameters, and only 1 included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with length of stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator. CONCLUSION: The results of our study provide information to physicians, hospital administrators, emergency response personnel, and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak. url: https://www.ncbi.nlm.nih.gov/pubmed/32907668/ doi: 10.1017/dmp.2020.332 id: cord-102359-k1xxz4hc author: Klotsa, Daphne title: Electronic Transport in DNA date: 2005-04-04 words: 6669.0 sentences: 412.0 pages: flesch: 62.0 cache: ./cache/cord-102359-k1xxz4hc.txt txt: ./txt/cord-102359-k1xxz4hc.txt summary: In most models of electronic transport [13, 60] it has been assumed that the transmission channels are along the long axis of the DNA molecule [61] and that the conduction path is due to π-orbital overlap between consecutive bases [52] ; density-functional calculations [37] have shown that the bases, especially Guanine, are rich in π-orbitals. The main advantage of both methods is that they work reliably (i) for short DNA strands ranging from 13 (DFT studies [37] ) base pairs up to 30 base pairs length which are being used in the nanoscopic transport measurements [15] as well as (ii) for somewhat longer DNA sequences as modelled in the electron transfer results and (iii) even for complete DNA sequences which contain, e.g. for human chromosomes up to 245 million base pairs [2] . The fishbone and ladder models studied in the present paper give qualitatively similar results, i.e. a gap in the DOS on the order of the hopping energies to the backbone, extended states for periodic DNA sequences and localised states for any non-zero disorder strength. abstract: We study the electronic properties of DNA by way of a tight-binding model applied to four particular DNA sequences. The charge transfer properties are presented in terms of localisation lengths, crudely speaking the length over which electrons travel. Various types of disorder, including random potentials, are employed to account for different real environments. We have performed calculations on poly(dG)-poly(dC), telomeric-DNA, random-ATGC DNA and lambda-DNA. We find that random and lambda-DNA have localisation lengths allowing for electron motion among a few dozen base pairs only. A novel enhancement of localisation lengths is observed at particular energies for an increasing binary backbone disorder. We comment on the possible biological relevance of sequence dependent charge transfer in DNA. url: https://arxiv.org/pdf/q-bio/0504004v1.pdf doi: 10.1529/biophysj.105.064014 id: cord-017003-3farxcc3 author: Koibuchi, Yukio title: Numerical Simulation of Urban Coastal Zones date: 2010 words: 7577.0 sentences: 476.0 pages: flesch: 53.0 cache: ./cache/cord-017003-3farxcc3.txt txt: ./txt/cord-017003-3farxcc3.txt summary: Such a mixing process continues until the river water reaches the same density as the surrounding sea water, resulting in vertical circulation in the bays that is is several to ten times greater than the river flux (Unoki 1998) . The ecosystem model introduced here was developed to simulate the nutrient budget of an urban coastal zone. To quantify the nutrients budget, we applied our numerical model to Tokyo Bay. The computational domain was divided into 1km horizontal grids with 20 vertical layers. Fig. 3-13 shows the calculation results of an annual budget of nitrogen and phosphorus in Tokyo Bay. The annual budget is useful in understanding nutrient cycles. We have developed a water quality model to simulate both nutrient cycles and pathogens distributions, and coupled it with a three-dimensional hydrodynamic model of urban coastal areas. We applied this model to the Tokyo Bay and simulated water column temperatures, salinity, and nutrient concentrations that were closely linked with field observations. abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121458/ doi: 10.1007/978-4-431-99720-7_3 id: cord-307133-bm9z8gss author: Kong, Lingcai title: Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model date: 2016-02-24 words: 4611.0 sentences: 247.0 pages: flesch: 48.0 cache: ./cache/cord-307133-bm9z8gss.txt txt: ./txt/cord-307133-bm9z8gss.txt summary: Finally, we calibrated the model with the number of daily cases of severe acute respiratory syndrome (SARS) in Beijing in 2003, and the estimated parameters show that the control measures taken at that time were effective. A low level of heterogeneity results in dynamics similar to those predicted by the homogeneous-mixing model with a frequency-dependent transmission term, βSI N . The greatest difference is that at the overall level, the heterogeneity slows the transmission speed and decreases the peak sizes, which means milder disease outbreaks, because in the scenario with a high level of heterogeneity, only a small proportion of susceptible individuals have chances of coming into contact with infectious individuals and becoming infected, which results in a slower increase of the infected population. Our results show that, keeping other conditions identical, the higher is the level of heterogeneity in contact rates, the greater is the difference in the disease dynamics observed from those predicted using the homogeneous-mixing models. abstract: Mathematical models have been used to understand the transmission dynamics of infectious diseases and to assess the impact of intervention strategies. Traditional mathematical models usually assume a homogeneous mixing in the population, which is rarely the case in reality. Here, we construct a new transmission function by using as the probability density function a negative binomial distribution, and we develop a compartmental model using it to model the heterogeneity of contact rates in the population. We explore the transmission dynamics of the developed model using numerical simulations with different parameter settings, which characterize different levels of heterogeneity. The results show that when the reproductive number, [Formula: see text] , is larger than one, a low level of heterogeneity results in dynamics similar to those predicted by the homogeneous mixing model. As the level of heterogeneity increases, the dynamics become more different. As a test case, we calibrated the model with the case incidence data for severe acute respiratory syndrome (SARS) in Beijing in 2003, and the estimated parameters demonstrated the effectiveness of the control measures taken during that period. url: https://www.ncbi.nlm.nih.gov/pubmed/26927140/ doi: 10.3390/ijerph13030253 id: cord-025517-rb4sr8r4 author: Koutsomitropoulos, Dimitrios A. title: Automated MeSH Indexing of Biomedical Literature Using Contextualized Word Representations date: 2020-05-06 words: 4474.0 sentences: 236.0 pages: flesch: 52.0 cache: ./cache/cord-025517-rb4sr8r4.txt txt: ./txt/cord-025517-rb4sr8r4.txt summary: 4 presents our methodology and approach, by outlining the indexing procedure designed, describing the algorithms used and discussing optimizations regarding dataset balancing, distributed processing and training parallelization. There are two steps in this method: first, constructing MeSH term graph based on its RDF data and sampling the MeSH term sequences and, second, employing the FastText subword embedding model to learn the distributed word embeddings based on text sequences and MeSH term sequences. We then proceed by evaluating and reporting on two prominent embedding algorithms, namely Doc2Vec and ELMo. The models constructed with these algorithms, once trained, can be used to suggest thematic classification terms from the MeSH vocabulary. This body of text is next fed into the model and its vector similarity score is computed against the list of MeSH terms available in the vocabulary. Training datasets comprise biomedical literature from open access repositories including PubMed [19], EuropePMC [3] and ClinicalTrials [17] along with their handpicked MeSH terms. abstract: Appropriate indexing of resources is necessary for their efficient search, discovery and utilization. Relying solely on manual effort is time-consuming, costly and error prone. On the other hand, the special nature, volume and broadness of biomedical literature pose barriers for automated methods. We argue that current word embedding algorithms can be efficiently used to support the task of biomedical text classification. Both deep- and shallow network approaches are implemented and evaluated. Large datasets of biomedical citations and full texts are harvested for their metadata and used for training and testing. The ontology representation of Medical Subject Headings provides machine-readable labels and specifies the dimensionality of the problem space. These automated approaches are still far from entirely substituting human experts, yet they can be useful as a mechanism for validation and recommendation. Dataset balancing, distributed processing and training parallelization in GPUs, all play an important part regarding the effectiveness and performance of proposed methods. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256379/ doi: 10.1007/978-3-030-49161-1_29 id: cord-336687-iw3bzy0m author: Kraemer, M. U. G. title: Big city, small world: density, contact rates, and transmission of dengue across Pakistan date: 2015-10-06 words: 4517.0 sentences: 231.0 pages: flesch: 41.0 cache: ./cache/cord-336687-iw3bzy0m.txt txt: ./txt/cord-336687-iw3bzy0m.txt summary: Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of ''mixing parameters'' when disaggregating data across an urban–rural gradient. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. In no application of the TSIR model to date has the potential for variation in these parameters been assessed, leaving the extent to which inhomogeneity of mixing varies across space and time as an open question in the study of infectious disease dynamics. To assess the potential for spatial variation in the inhomogeneity of mixing as it pertains dengue transmission, we performed an analysis of district-level time series of dengue transmission in the Punjab province of Pakistan using a TSIR model with separate mixing parameters for urban and rural districts. abstract: Macroscopic descriptions of populations commonly assume that encounters between individuals are well mixed; i.e. each individual has an equal chance of coming into contact with any other individual. Relaxing this assumption can be challenging though, due to the difficulty of acquiring detailed knowledge about the non-random nature of encounters. Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of ‘mixing parameters’ when disaggregating data across an urban–rural gradient. We show that dynamics across this gradient are subject not only to differing transmission intensities but also to differing strengths of nonlinearity due to differences in mixing. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. We furthermore show that neglecting spatial variation in mixing can lead to substantial underestimates of the level of effort needed to control a pathogen with vaccines or other interventions. We complement this analysis with estimates of the relationships between dengue transmission intensity and other putative environmental drivers thereof. url: https://doi.org/10.1098/rsif.2015.0468 doi: 10.1098/rsif.2015.0468 id: cord-004416-qw6tusd2 author: Krishna, Smriti M. title: Development of a two-stage limb ischemia model to better simulate human peripheral artery disease date: 2020-02-26 words: 8149.0 sentences: 464.0 pages: flesch: 49.0 cache: ./cache/cord-004416-qw6tusd2.txt txt: ./txt/cord-004416-qw6tusd2.txt summary: HLI was more severe in mice receiving the 2-stage compared to the 1-stage ischemia induction procedure as assessed by LDPI (p = 0.014), and reflected in a higher ischemic score (p = 0.004) and lower average distance travelled on a treadmill test (p = 0.045). Mice undergoing the 2-stage HLI also had lower expression of angiogenesis markers (vascular endothelial growth factor, p = 0.004; vascular endothelial growth factorreceptor 2, p = 0.008) and shear stress response mechano-transducer transient receptor potential vanilloid 4 (p = 0.041) within gastrocnemius muscle samples, compared to animals having the 1-stage HLI procedure. In contrast, the most commonly used animal model for initial testing of novel therapies for PAD is a model of acute blood supply interruption through ligation or excision of the femoral artery (referred to here as the 1-stage hind limb ischemia (HLI) model) 14, 15 . abstract: Peripheral arterial disease (PAD) develops due to the narrowing or blockage of arteries supplying blood to the lower limbs. Surgical and endovascular interventions are the main treatments for advanced PAD but alternative and adjunctive medical therapies are needed. Currently the main preclinical experimental model employed in PAD research is based on induction of acute hind limb ischemia (HLI) by a 1-stage procedure. Since there are concerns regarding the ability to translate findings from this animal model to patients, we aimed to develop a novel clinically relevant animal model of PAD. HLI was induced in male Apolipoprotein E (ApoE(−/−)) deficient mice by a 2-stage procedure of initial gradual femoral artery occlusion by ameroid constrictors for 14 days and subsequent excision of the femoral artery. This 2-stage HLI model was compared to the classical 1-stage HLI model and sham controls. Ischemia severity was assessed using Laser Doppler Perfusion Imaging (LDPI). Ambulatory ability was assessed using an open field test, a treadmill test and using established scoring scales. Molecular markers of angiogenesis and shear stress were assessed within gastrocnemius muscle tissue samples using quantitative polymerase chain reaction. HLI was more severe in mice receiving the 2-stage compared to the 1-stage ischemia induction procedure as assessed by LDPI (p = 0.014), and reflected in a higher ischemic score (p = 0.004) and lower average distance travelled on a treadmill test (p = 0.045). Mice undergoing the 2-stage HLI also had lower expression of angiogenesis markers (vascular endothelial growth factor, p = 0.004; vascular endothelial growth factor- receptor 2, p = 0.008) and shear stress response mechano-transducer transient receptor potential vanilloid 4 (p = 0.041) within gastrocnemius muscle samples, compared to animals having the 1-stage HLI procedure. Mice subjected to the 2-stage HLI receiving an exercise program showed significantly greater improvement in their ambulatory ability on a treadmill test than a sedentary control group. This study describes a novel model of HLI which leads to more severe and sustained ischemia than the conventionally used model. Exercise therapy, which has established efficacy in PAD patients, was also effective in this new model. This new model maybe useful in the evaluation of potential novel PAD therapies. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044206/ doi: 10.1038/s41598-020-60352-4 id: cord-027286-mckqp89v author: Ksieniewicz, Paweł title: Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks date: 2020-05-23 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that makes use, among others, of a regression method. The main focus of that study is put on the construction of efficient regression model and its application for the initial optimization problem. We therefore perform extensive experiments using realistic network assumptions and then draw conclusions regarding efficient approach configuration. According to the results, the approach performs best using multi-layer perceptron regressor, whose prediction ability was the highest among all tested methods. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303703/ doi: 10.1007/978-3-030-50423-6_16 id: cord-217139-d9q7zkog author: Kumar, Sumit title: Future of COVID-19 in Italy: A mathematical perspective date: 2020-04-18 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We have proposed an SEIR compartmental mathematical model. The prime objective of this study is to analyze and forecast the pandemic in Italy for the upcoming months. The basic reproduction number has been calculated. Based on the current situation in Italy, in this paper, we will estimate the possible time for the end of the pandemic in the country. The impact of lockdown and rapid isolation on the spread of the pandemic are also discussed. Further, we have studied four of the most pandemic affected regions in Italy. Using the proposed model, a prediction has been made about the duration of pandemic in these regions. The variation in the basic reproduction number corresponding to the sensitive parameters of the model is also examined. url: https://arxiv.org/pdf/2004.08588v1.pdf doi: nan id: cord-017934-3wyebaxb author: Kurahashi, Setsuya title: An Agent-Based Infectious Disease Model of Rubella Outbreaks date: 2019-05-07 words: 3239.0 sentences: 195.0 pages: flesch: 57.0 cache: ./cache/cord-017934-3wyebaxb.txt txt: ./txt/cord-017934-3wyebaxb.txt summary: We aim to study the relationship between antibody holding rate of men and the spread of infection by constructing infection of rubella virus with the agent-based model and repeating simulation experiment on a computer. Although our previous study described the infectious disease model of smallpox and Ebola [6] , this paper proposes a new model of rubella which has caused crucial problems for pregnant women in recent years. As results of experiments showed that (1) in a base model in which any infectious disease measures were not taken, the epidemic spread within 82 days and 30% of people died, (2) a trace vaccination measure was effective but it was difficult to trace all contacts to patients in an underground railway or an airport, (3) a mass vaccination measure was effective, but the number of vaccinations would be huge so it was not realistic and (4) epidemic quenching was also effective, and reactive household trace vaccination along with pre-emptive vaccination of hospital workers showed a dramatic effect. abstract: This study proposes a simulation model of rubella. SIR (Susceptible, Infected, Recovered) model has been widely used to analyse infectious diseases such as influenza, smallpox, bioterrorism, to name a few. On the other hand, agent-based model begins to spread in recent years. The model enables to represent the behaviour of each person on the computer. It also reveals the spread of infection by simulation of the contact process among people in the model. The study designs a model based on smallpox and Ebola fever model in which several health policies are decided such as vaccination, the gender-specific workplace and so on. The infectious simulation of rubella, which has not yet vaccinated completely for men in Japan, is implemented in the model. As results of experiments using the model, it has been found that preventive vaccine to all the men is crucial factors to prevent the spread in women. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122630/ doi: 10.1007/978-981-13-8679-4_20 id: cord-352543-8il0dh58 author: Kuzdeuov, A. title: A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies date: 2020-05-06 words: 6262.0 sentences: 311.0 pages: flesch: 47.0 cache: ./cache/cord-352543-8il0dh58.txt txt: ./txt/cord-352543-8il0dh58.txt summary: In this scenario, epidemiological models can be used to project the future course of the disease, and to estimate the impact of non-pharmaceutical interventions (NPIs) and related control measures that might be used to slow the contagion, and thereby provide time to enhance health care resources and develop effective immunological defenses such as new vaccines. We have developed and implemented a network-based stochastic epidemic simulator (leveraging our prior work [8] ) which models cities and regions as nodes in a graph, and the edges between nodes representing transit links of roads, railways, and air travel routes to model the mobility of inhabitants amongst cities. In each node, the simulator runs a compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) model, such that individuals can cycle through the four stages based on state transition probabilities. abstract: In this work, we present an open-source stochastic epidemic simulator, calibrated with extant epidemic experience of COVID-19. Our simulator incorporates information ranging from population demographics and mobility data to health care resource capacity, by region, with interactive controls of system variables to allow dynamic and interactive modeling of events. The simulator can be generalized to model the propagation of any disease, in any territory, but for this experiment was customized to model the spread of COVID-19 in the Republic of Kazakhstan, and estimate outcomes of policy options to inform deliberations on governmental interdiction policies. url: https://doi.org/10.1101/2020.05.02.20089136 doi: 10.1101/2020.05.02.20089136 id: cord-294586-95iwcocn author: Kwuimy, C. A. K. title: Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action date: 2020-07-16 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully and to use that as a basis to explore the nonlinear behavior. We use a nonlinear susceptible, exposed, infectious and removed transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID-19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics. url: https://www.ncbi.nlm.nih.gov/pubmed/32836814/ doi: 10.1007/s11071-020-05815-z id: cord-297530-7zbvgvk8 author: Kühnert, Denise title: Phylogenetic and epidemic modeling of rapidly evolving infectious diseases date: 2011-08-31 words: 12826.0 sentences: 629.0 pages: flesch: 42.0 cache: ./cache/cord-297530-7zbvgvk8.txt txt: ./txt/cord-297530-7zbvgvk8.txt summary: By using Kingman''s coalescent as a prior density on trees, Bayesian inference can be used to simultaneously estimate the phylogeny of the viral sequences and the demographic history of the virus population (Drummond et al., 2002 (Drummond et al., , 2005 , see Box 1). A maximum likelihood based method (the single rate dated tips (SRDT) model; Rambaut, 2000) , estimates ancestral divergence times and overall substitution rate on a fixed tree, assuming a strict molecular clock. While the generalized skyline plot is a good tool for data exploration, and to assist in model selection (e.g., Pybus et al., 2003; Lemey et al., 2004) , it infers demographic history based on a single input tree and therefore does not account for sampling error produced by phylogenetic reconstruction nor for the intrinsic stochasticity of the coalescent process. abstract: Epidemic modeling of infectious diseases has a long history in both theoretical and empirical research. However the recent explosion of genetic data has revealed the rapid rate of evolution that many populations of infectious agents undergo and has underscored the need to consider both evolutionary and ecological processes on the same time scale. Mathematical epidemiology has applied dynamical models to study infectious epidemics, but these models have tended not to exploit – or take into account – evolutionary changes and their effect on the ecological processes and population dynamics of the infectious agent. On the other hand, statistical phylogenetics has increasingly been applied to the study of infectious agents. This approach is based on phylogenetics, molecular clocks, genealogy-based population genetics and phylogeography. Bayesian Markov chain Monte Carlo and related computational tools have been the primary source of advances in these statistical phylogenetic approaches. Recently the first tentative steps have been taken to reconcile these two theoretical approaches. We survey the Bayesian phylogenetic approach to epidemic modeling of infection diseases and describe the contrasts it provides to mathematical epidemiology as well as emphasize the significance of the future unification of these two fields. url: https://api.elsevier.com/content/article/pii/S156713481100284X doi: 10.1016/j.meegid.2011.08.005 id: cord-289325-jhokn5bu author: Lachiany, Menachem title: Effects of distribution of infection rate on epidemic models date: 2016-08-11 words: 6823.0 sentences: 446.0 pages: flesch: 59.0 cache: ./cache/cord-289325-jhokn5bu.txt txt: ./txt/cord-289325-jhokn5bu.txt summary: We show that when variability in infection rates is included in standard susciptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) models the total number of infected individuals in the late dynamics can be orders lower than predicted from the early dynamics. As will be further shown, the initial dynamics are only affected by the first moment of the distribution (the expected values of β), while the total number of infected individuals during the outbreak in the SIR model or the steady-state infected fraction in the SIS model can be strongly affected by the following moments. Thus, in some distributions, it is impossible to predict the "outcome" of the epidemics from the observed initial dynamics and the resulting estimate of Ro. To examine the behavior of the infected class as a function of time, we developed a moment closure scheme, and we use the following notations: abstract: A goal of many epidemic models is to compute the outcome of the epidemics from the observed infected early dynamics. However, often, the total number of infected individuals at the end of the epidemics is much lower than predicted from the early dynamics. This discrepancy is argued to result from human intervention or nonlinear dynamics not incorporated in standard models. We show that when variability in infection rates is included in standard susciptible-infected-susceptible ([Formula: see text]) and susceptible-infected-recovered ([Formula: see text]) models the total number of infected individuals in the late dynamics can be orders lower than predicted from the early dynamics. This discrepancy holds for [Formula: see text] and [Formula: see text] models, where the assumption that all individuals have the same sensitivity is eliminated. In contrast with network models, fixed partnerships are not assumed. We derive a moment closure scheme capturing the distribution of sensitivities. We find that the shape of the sensitivity distribution does not affect [Formula: see text] or the number of infected individuals in the early phases of the epidemics. However, a wide distribution of sensitivities reduces the total number of removed individuals in the [Formula: see text] model and the steady-state infected fraction in the [Formula: see text] model. The difference between the early and late dynamics implies that in order to extrapolate the expected effect of the epidemics from the initial phase of the epidemics, the rate of change in the average infectivity should be computed. These results are supported by a comparison of the theoretical model to the Ebola epidemics and by numerical simulation. url: https://doi.org/10.1103/physreve.94.022409 doi: 10.1103/physreve.94.022409 id: cord-326409-m3rgspxc author: Lai, Alvin C.K. title: Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors date: 2007-03-24 words: 3568.0 sentences: 196.0 pages: flesch: 51.0 cache: ./cache/cord-326409-m3rgspxc.txt txt: ./txt/cord-326409-m3rgspxc.txt summary: authors: Lai, Alvin C.K.; Chen, F.Z. title: Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors Results reveal that the standard k–ε Lagrangian model over-predicts particle deposition compared to the present turbulence-corrected Lagrangian approach. In the present work, we compared particle distribution and deposition rates for a small model chamber by the two approaches. (1), while within the concentration boundary layer, the particle wall flux is determined with a one-dimensional semi-empirical particle deposition model (Lai and Nazaroff, 2000) and the results are substituted into Eq. Overall speaking, the results modeled by the two approaches agree well with each other; as the particle size increases, the deposition fraction increases. For submicron particles, the deposition fractions predicted by Lagrangian (without near-wall turbulent correction) is higher than those predicted with correction and Eulerian drift flux prediction follows. Modeling indoor particle deposition from turbulent flow onto smooth surfaces abstract: Understanding of aerosol dispersion characteristics has many scientific and engineering applications. It is recognized that Eulerian or Lagrangian approach has its own merits and limitations. A new Eulerian model has been developed and it adopts a simplified drift–flux methodology in which external forces can be incorporated straightforwardly. A new near-wall treatment is applied to take into account the anisotropic turbulence for the modified Lagrangian model. In the present work, we present and compare both Eulerian and Lagrangian models to simulate particle dispersion in a small chamber. Results reveal that the standard k–ε Lagrangian model over-predicts particle deposition compared to the present turbulence-corrected Lagrangian approach. Prediction by the Eulerian model agrees well with the modified Lagrangian model. url: https://doi.org/10.1016/j.atmosenv.2006.05.088 doi: 10.1016/j.atmosenv.2006.05.088 id: cord-305318-cont592g author: Lancaster, Madeline A. title: Disease modelling in human organoids date: 2019-07-01 words: 10865.0 sentences: 484.0 pages: flesch: 39.0 cache: ./cache/cord-305318-cont592g.txt txt: ./txt/cord-305318-cont592g.txt summary: Thus, more recent approaches have focused on in vitro models derived from stem cells, which allow for a broader array of tissue identities, long-term expansion, better genomic integrity and improved modelling of healthy biology. established the first adult murine-tissue-derived liver organoid culture that sustains the long-term expansion of liver cells in vitro (Huch et al., 2013b) . Addition of an activator of cyclic adenosyl monophosphate (cAMP) signalling and inhibition of TGFβ signalling adapted this culture system to the expansion of adult human liver cells as self-renewing organoids that recapitulate some function of ex vivo liver tissue . (2014) was instrumental in characterizing the early stages of metanephric kidney development, particularly the formation of metanephric mesenchyme (MM), then applying the identified signalling factors to direct differentiation of mouse and human PSCs specifically towards MM cells that could form 3D structures when cocultured with mouse tissues. abstract: The past decade has seen an explosion in the field of in vitro disease modelling, in particular the development of organoids. These self-organizing tissues derived from stem cells provide a unique system to examine mechanisms ranging from organ development to homeostasis and disease. Because organoids develop according to intrinsic developmental programmes, the resultant tissue morphology recapitulates organ architecture with remarkable fidelity. Furthermore, the fact that these tissues can be derived from human progenitors allows for the study of uniquely human processes and disorders. This article and accompanying poster highlight the currently available methods, particularly those aimed at modelling human biology, and provide an overview of their capabilities and limitations. We also speculate on possible future technological advances that have the potential for great strides in both disease modelling and future regenerative strategies. url: https://doi.org/10.1242/dmm.039347 doi: 10.1242/dmm.039347 id: cord-352431-yu7kxnab author: Langbeheim, Elon title: Science Teachers’ Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module date: 2020-08-25 words: 7931.0 sentences: 432.0 pages: flesch: 45.0 cache: ./cache/cord-352431-yu7kxnab.txt txt: ./txt/cord-352431-yu7kxnab.txt summary: It examines the factors shaping the teachers'' self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. Surprisingly, the short interaction with computational modeling increased the group''s self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Therefore, the goal of this study is to examine science teachers'' attitudes towards introducing computational model construction in the context of inquiry-based learning in physics. The first research question asked how do teachers'' prior experiences in teaching physics influence their self-efficacy and attitudes towards inquiry-based learning practices in a PD workshop. 2. In order to investigate the 2nd research question regarding the influence of teachers'' prior involvement with programming on their self-efficacy in, and experience of computational modeling that involves coding in a PD workshop, we used the following data sources: abstract: This study focuses on science teachers’ first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers’ self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers’ attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers’ self-efficacy in teaching physics in 9th grade. Also, teachers’ prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group’s self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers’ predispositions towards the integration of computational modeling into the physics teaching. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10956-020-09855-3) contains supplementary material, which is available to authorized users. url: https://doi.org/10.1007/s10956-020-09855-3 doi: 10.1007/s10956-020-09855-3 id: cord-140839-rij8f137 author: Langfeld, Kurt title: Dynamics of epidemic diseases without guaranteed immunity date: 2020-07-31 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The global SARS-CoV-2 pandemic suggests a novel type of disease spread dynamics. WHO states that there is currently no evidence that people who have recovered from COVID-19 and have antibodies are immune from a second infection [WHO]. Conventional mathematical models consider cases for which a recovered individual either becomes susceptible again or develops an immunity. Here, we study the case where infected agents recover and only develop immunity if they are continuously infected for some time. Otherwise, they become susceptible again. We show that field theory bounds the peak of the infectious rate. Consequently, the theory's phases characterise the disease dynamics: (i) a pandemic phase and (ii) a response regime. The model excellently describes the epidemic spread of the SARS-CoV-2 outbreak in the city of Wuhan, China. We find that only 30% of the recovered agents have developed an immunity. We anticipate our paper to influence the decision making upon balancing the economic impact and the pandemic impact on society. As long as disease controlling measures keep the disease dynamics in the"response regime", a pandemic escalation ('second wave') is ruled out. url: https://arxiv.org/pdf/2007.15971v1.pdf doi: nan id: cord-329276-tfrjw743 author: Ledzewicz, Urszula title: On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies date: 2020-09-30 words: 12517.0 sentences: 624.0 pages: flesch: 51.0 cache: ./cache/cord-329276-tfrjw743.txt txt: ./txt/cord-329276-tfrjw743.txt summary: We discuss various aspects of the modeling of the dynamics (such as growth and interaction terms), modeling of treatment (including pharmacometrics of the drugs), and give special attention to the choice of the objective functional to be minimized. , m, represent the administration of the therapies (dose rates) and as variables are separated from the effects of the actions (which, for example, depend on the concentrations), then a model which is linear in the controls is not only adequate, but is the correct one. Choosing the objective functional in the form (17) with N = 0 (as we do not consider an immune boost), optimal chemotherapy protocols follow the concatenation structure 1s01 with 1 representing a full dose segment, s denoting administration following a singular control and 0 standing for a rest-period of the treatment. abstract: We review and discuss results obtained through an application of tools of nonlinear optimal control to biomedical problems. We discuss various aspects of the modeling of the dynamics (such as growth and interaction terms), modeling of treatment (including pharmacometrics of the drugs), and give special attention to the choice of the objective functional to be minimized. Indeed, many properties of optimal solutions are predestined by this choice which often is only made casually using some simple ad hoc heuristics. We discuss means to improve this choice by taking into account the underlying biology of the problem. url: https://doi.org/10.1007/s10957-020-01754-2 doi: 10.1007/s10957-020-01754-2 id: cord-329256-7njgmdd1 author: Leecaster, Molly title: Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics date: 2011-04-21 words: 4534.0 sentences: 228.0 pages: flesch: 48.0 cache: ./cache/cord-329256-7njgmdd1.txt txt: ./txt/cord-329256-7njgmdd1.txt summary: METHODS: The goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves. Regression analysis was used to explore the relationship between the initial exponential growth rate and the epidemic season characteristics of size, days to peak, and length using the seven epidemic seasons of RSV data from PCMC. The fit statistics for the models with either transmission parameter or Table 1 Observed RSV epidemic size, start date, days to peak, duration, and 4-week exponential growth detection fraction estimated as a constant across epidemic year did not differ substantially from those from the saturated model (Table 4) . abstract: BACKGROUND: Seasonal respiratory syncytial virus (RSV) epidemics occur annually in temperate climates and result in significant pediatric morbidity and increased health care costs. Although RSV epidemics generally occur between October and April, the size and timing vary across epidemic seasons and are difficult to predict accurately. Prediction of epidemic characteristics would support management of resources and treatment. METHODS: The goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. RSV testing data from Primary Children's Medical Center were collected on children under two years of age (July 2001-June 2008). Simple linear regression was used explore the relationship between three epidemic characteristics (final epidemic size, days to peak, and epidemic length) and exponential growth calculated from four weeks of daily case data. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves. RESULTS: The regression results indicated that exponential growth was correlated to epidemic characteristics. The transmission modeling results indicated that start time for the epidemic and the transmission parameter co-varied with the epidemic season. CONCLUSIONS: The conclusions were that exponential growth was somewhat empirically related to seasonal epidemic characteristics and that variation in epidemic start date as well as the transmission parameter over epidemic years could explain variation in seasonal epidemic size. These relationships are useful for public health, health care providers, and infectious disease researchers. url: https://www.ncbi.nlm.nih.gov/pubmed/21510889/ doi: 10.1186/1471-2334-11-105 id: cord-256289-rls5lr27 author: Leeuwenberg, Artuur M title: Prediction models for COVID-19 clinical decision making date: 2020-09-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: nan url: https://www.sciencedirect.com/science/article/pii/S2589750020302260 doi: 10.1016/s2589-7500(20)30226-0 id: cord-266626-9vn6yt8m author: Lei, Howard title: Agile Clinical Research: A Data Science Approach to Scrumban in Clinical Medicine date: 2020-10-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape. url: https://api.elsevier.com/content/article/pii/S2666521220300090 doi: 10.1016/j.ibmed.2020.100009 id: cord-213974-rtltf11w author: Lensink, Keegan title: Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients date: 2020-07-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs, is the most common presentation associated with this virus, and great attention has gone into how these changes relate to patient morbidity and mortality. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. We have collected 663 chest CT scans of COVID-19 patients from healthcare centers around the world, and created pixel wise segmentation labels for nearly 25,000 slices that segment 6 different patterns of pulmonary opacification. We provide open source implementations and pre-trained weights for multiple segmentation models trained on our dataset. Our best model achieves an opacity Intersection-Over-Union score of 0.76 on our test set, demonstrates successful domain adaptation, and predicts the volume of opacification within 1.7% of expert radiologists. Additionally, we present an analysis of the inter-observer variability inherent to this task, and propose methods for appropriate probabilistic approaches. url: https://arxiv.org/pdf/2007.03643v2.pdf doi: nan id: cord-024866-9og7pivv author: Lepenioti, Katerina title: Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing date: 2020-04-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225513/ doi: 10.1007/978-3-030-49165-9_1 id: cord-330148-yltc6wpv author: Lessler, Justin title: Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases date: 2016-07-02 words: 5911.0 sentences: 247.0 pages: flesch: 34.0 cache: ./cache/cord-330148-yltc6wpv.txt txt: ./txt/cord-330148-yltc6wpv.txt summary: Uncertainty was largely addressed through scenario-based approaches (e.g., different future epidemic trajectories were presented for different plausible sets of parameters), and for the most part, different aspects of the transmission dynamics were derived from independent studies, with only the growth rate (i.e., doubling time) estimated from incidence data. These recent attempts to quickly characterize the properties of emerging diseases are emblematic of an increasing focus on developing statistical methods, grounded in dynamical models, to estimate key epidemic parameters based on diverse data sources. High-resolution geographic data can gain additional power when paired with mechanistic models that capture changes in disease risk, as in recent analyses that accounted for the effect of birth, natural infection, and vaccine disruptions driving increases in measles susceptibility and epidemic risk in the wake of the Ebola outbreak [63] . The formal statistical integration of population genetic and epidemic models allows us to estimate the critical epidemiological parameters such as the basic reproductive number directly from pathogen sequence data [75] [76] [77] . abstract: The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such “dependent happenings” are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to “big data” are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and “traditional” epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications. url: https://www.ncbi.nlm.nih.gov/pubmed/32226711/ doi: 10.1007/s40471-016-0078-4 id: cord-332093-iluqwwxs author: Lessler, Justin title: Mechanistic Models of Infectious Disease and Their Impact on Public Health date: 2016-02-17 words: 5501.0 sentences: 231.0 pages: flesch: 38.0 cache: ./cache/cord-332093-iluqwwxs.txt txt: ./txt/cord-332093-iluqwwxs.txt summary: Though never published by Reed and Frost (versions of the model were eventually published by their students (3, 4) ), their model was one of the first mechanistic models of infectious disease transmission, and at a time long before digital computing, they may have been the first to use simulation methods to understand the epidemic process. Perhaps the first mechanistic model of infectious disease transmission used in assessing intervention strategies was a mathematical model of malaria transmission developed and refined by Ronald Ross in a series of papers published between 1908 and 1921 (9) (10) (11) , pre-dating the work of Reed and Frost by decades. The aforementioned work, particularly that of the World Health Organization Ebola Response Team, also characterized important aspects of Ebola''s natural history and epidemiology, including its basic reproductive number (R 0 ), the decline in R over the course of the epidemic, the incubation period, and the serial interval, properties of the disease that will be important to understand should it re-emerge. abstract: From the 1930s through the 1940s, Lowell Reed and Wade Hampton Frost used mathematical models and mechanical epidemic simulators as research tools and to teach epidemic theory to students at the Johns Hopkins Bloomberg School of Public Health (then the School of Hygiene and Public Health). Since that time, modeling has become an integral part of epidemiology and public health. Models have been used for explanatory and inferential purposes, as well as in planning and implementing public health responses. In this article, we review a selection of developments in the history of modeling of infectious disease dynamics over the past 100 years. We also identify trends in model development and use and speculate as to the future use of models in infectious disease dynamics. url: https://doi.org/10.1093/aje/kww021 doi: 10.1093/aje/kww021 id: cord-127900-78x19fw4 author: Leung, Abby title: Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies date: 2020-10-06 words: 5032.0 sentences: 279.0 pages: flesch: 55.0 cache: ./cache/cord-127900-78x19fw4.txt txt: ./txt/cord-127900-78x19fw4.txt summary: More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening. Only by incorporating real world contact networks into compartment models, one can disconnect network hubs to realistically simulate the effect of closure. We focus on the effects of 4 widely adopted NPIs: 1) quarantining infected and exposed individuals, 2) social distancing, 3) closing down of non-essential work places and schools, and 4) the use of face masks. • We show that structure of the contact networks significantly changes the epidemic curves and the current compartment based models are subject to overestimating the scale of the spread • We demonstrate the degree of effectiveness of different NPIs depends on the assumed underlying structure of the contact networks abstract: The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening. url: https://arxiv.org/pdf/2010.03081v1.pdf doi: nan id: cord-309301-ai84el0j author: Li, Yaqi title: Organoid based personalized medicine: from bench to bedside date: 2020-11-02 words: 17467.0 sentences: 934.0 pages: flesch: 41.0 cache: ./cache/cord-309301-ai84el0j.txt txt: ./txt/cord-309301-ai84el0j.txt summary: The mini-gut culture approach has been applied to the generation of organoids derived from the epithelial compartments of a variety of murine and human tissues of ecto-, meso-and endodermal origin, and promotes the study of stem cell biology of other tissues except for intestine. For translational research, tumorderived organoids can be used for biobanking, genetic repair and drug screening studies, both for personalized medicine (to choose the most effective treatment for a specific patient) and drug development (to test a compound library on a specific set of tumor organoids), as well as immunotherapy research similar in liver, small intestine, and colon stem cells, regardless of the large variation in cancer incidence of these organs. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell-and patient-derived tumor organoids abstract: Three-dimensional cultured organoids have become a powerful in vitro research tool that preserves genetic, phenotypic and behavioral trait of in vivo organs, which can be established from both pluripotent stem cells and adult stem cells. Organoids derived from adult stem cells can be established directly from diseased epithelium and matched normal tissues, and organoids can also be genetically manipulated by CRISPR-Cas9 technology. Applications of organoids in basic research involve the modeling of human development and diseases, including genetic, infectious and malignant diseases. Importantly, accumulating evidence suggests that biobanks of patient-derived organoids for many cancers and cystic fibrosis have great value for drug development and personalized medicine. In addition, organoids hold promise for regenerative medicine. In the present review, we discuss the applications of organoids in the basic and translational research. url: https://doi.org/10.1186/s13619-020-00059-z doi: 10.1186/s13619-020-00059-z id: cord-263620-9rvlnqxk author: Li, Zhi-Chun title: Fifty years of the bottleneck model: A bibliometric review and future research directions date: 2020-09-30 words: 19069.0 sentences: 935.0 pages: flesch: 48.0 cache: ./cache/cord-263620-9rvlnqxk.txt txt: ./txt/cord-263620-9rvlnqxk.txt summary: These insights cover various aspects, such as behavioral analysis (e.g., the nature of shifting peak, inefficiency of unpriced equilibria, behavioral difference of heterogeneous commuters, connection between morning and evening commutes, effects of commuter scheduling preferences), demand management (e.g., congestion / emission / parking pricing and tradable credit schemes, relationship between bottleneck congestion tolling and urban structure), and supply management (e.g., bottleneck / parking capacity expansion). The travel behavior analysis mainly focuses on the analysis of the trip and/or activity scheduling behavior of travelers through building various travel choice behavior models, such as departure time / route / parking / mode choices, morning vs evening commutes, piecewise constant vs time-varying scheduling preferences, normal congestion vs hypercongestion, homogeneous vs heterogeneous users, individual vs household, deterministic vs stochastic situations, single vs multiple bottlenecks, and analytical approach vs DTA (dynamic traffic assignment) approach. These extensions include considerations of other travel choice dimensions (e.g., route / parking / mode choices), morning-evening commutes, time-varying scheduling preferences, vehicle physical length in queue and hypercongestion, heterogeneous users, household travel and carpooling, stochastic models and information, multiple bottlenecks, and DTA-approach bottlenecks. abstract: Abstract The bottleneck model introduced by Vickrey in 1969 has been recognized as a benchmark representation of the peak-period traffic congestion due to its ability to capture the essence of congestion dynamics in a simple and tractable way. This paper aims to provide a 50th anniversary review of the bottleneck model research since its inception. A bibliometric analysis approach is adopted for identifying the distribution of all journal publications, influential papers, top contributing authors, and leading topics in the past half century. The literature is classified according to recurring themes into travel behavior analysis, demand-side strategies, supply-side strategies, and joint strategies of demand and supply sides. For each theme, typical extended models developed to date are surveyed. Some potential directions for further studies are discussed. url: https://api.elsevier.com/content/article/pii/S0191261520303490 doi: 10.1016/j.trb.2020.06.009 id: cord-333490-8pv5x6tz author: Liao, Yi title: Early box office prediction in China’s film market based on a stacking fusion model date: 2020-10-06 words: 5979.0 sentences: 309.0 pages: flesch: 56.0 cache: ./cache/cord-333490-8pv5x6tz.txt txt: ./txt/cord-333490-8pv5x6tz.txt summary: Specifically, combining Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and k-Nearest Neighbor (KNN) algorithms, we establish a stacking model for box office prediction during a film''s early stage of production (shooting period). (2015) added MPAA rating, competition, star value, sequels, and the number of screens to the prediction variables and proposed a pre-release box office prediction model based on a dynamic artificial neural network algorithm. Post-release prediction In addition to pre-release features, it also includes a large amount of theatre data, heat index, and audience comment information It contains the most information and the best predictive effectiveness, but the application value of the results is very low Next, we compare the contribute factors and the effectiveness of box office prediction at different stages (Table 1 ). Considering the availability of data and the predictive power of features, five pre-production factors are selected based on the film itself: genre, star value, release date, release area, and sequels. abstract: Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China’s film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model. url: https://doi.org/10.1007/s10479-020-03804-4 doi: 10.1007/s10479-020-03804-4 id: cord-241596-vh90s8vi author: Libotte, Gustavo Barbosa title: Determination of an Optimal Control Strategy for Vaccine Administration in COVID-19 Pandemic Treatment date: 2020-04-15 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: During decades, mathematical models have been used to predict the behavior of physical and biologic systems, and to define strategies aiming the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the aim is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. For this purpose, an inverse problem is formulated and solved in order to determine the parameters of the compartmental SIR (Susceptible-Infectious-Recovered) model. To solve such inverse problem, the Differential Evolution (DE) algorithm is employed. After this step, two optimal control problems (mono- and multi-objective) to determine the optimal strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment, i.e., a multi-objective optimal control problem. The solution of each optimal control problems is obtained using DE and Multi-Objective Differential Evolution (MODE) algorithms, respectively. The results regarding the proposed multi-objective optimal control problem provides a set of evidences from which an optimal strategy for vaccine administration can be chosen, according to a given criterion. url: https://arxiv.org/pdf/2004.07397v2.pdf doi: nan id: cord-001921-73esrper author: Lin, Cheng-Yung title: Zebrafish and Medaka: new model organisms for modern biomedical research date: 2016-01-28 words: 7725.0 sentences: 417.0 pages: flesch: 42.0 cache: ./cache/cord-001921-73esrper.txt txt: ./txt/cord-001921-73esrper.txt summary: Studies on gene expression patterns, regulatory cis-elements identification, and gene functions can be facilitated by using zebrafish embryos via a number of techniques, including transgenesis, in vivo transient assay, overexpression by injection of mRNAs, knockdown by injection of morpholino oligonucleotides, knockout and gene editing by CRISPR/Cas9 system and mutagenesis. In addition, transgenic lines of model fish harboring a tissue-specific reporter have become a powerful tool for the study of biological sciences, since it is possible to visualize the dynamic expression of a specific gene in the transparent embryos. generated a transgenic zebrafish line huORFZ, which harbors the upstream open reading frame (uORF) from human CCAAT/enhancer-binding protein homologous protein gene (chop), fused with the GFP reporter and driven by a cytomegalovirus promoter [54] . For example, the Tsai''s lab established a transgenic line which could be induced to knock down the expression level of cardiac troponin C at any developmental stage, including embryos, larva or adult fish. abstract: Although they are primitive vertebrates, zebrafish (Danio rerio) and medaka (Oryzias latipes) have surpassed other animals as the most used model organisms based on their many advantages. Studies on gene expression patterns, regulatory cis-elements identification, and gene functions can be facilitated by using zebrafish embryos via a number of techniques, including transgenesis, in vivo transient assay, overexpression by injection of mRNAs, knockdown by injection of morpholino oligonucleotides, knockout and gene editing by CRISPR/Cas9 system and mutagenesis. In addition, transgenic lines of model fish harboring a tissue-specific reporter have become a powerful tool for the study of biological sciences, since it is possible to visualize the dynamic expression of a specific gene in the transparent embryos. In particular, some transgenic fish lines and mutants display defective phenotypes similar to those of human diseases. Therefore, a wide variety of fish model not only sheds light on the molecular mechanisms underlying disease pathogenesis in vivo but also provides a living platform for high-throughput screening of drug candidates. Interestingly, transgenic model fish lines can also be applied as biosensors to detect environmental pollutants, and even as pet fish to display beautiful fluorescent colors. Therefore, transgenic model fish possess a broad spectrum of applications in modern biomedical research, as exampled in the following review. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730764/ doi: 10.1186/s12929-016-0236-5 id: cord-291180-xurmzmwj author: Lin, Xiaoqian title: A Review on Applications of Computational Methods in Drug Screening and Design date: 2020-03-18 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design. url: https://doi.org/10.3390/molecules25061375 doi: 10.3390/molecules25061375 id: cord-301505-np4nr7gg author: Lin, Xin title: Two types of transmembrane homomeric interactions in the integrin receptor family are evolutionarily conserved date: 2006-01-27 words: 5266.0 sentences: 279.0 pages: flesch: 49.0 cache: ./cache/cord-301505-np4nr7gg.txt txt: ./txt/cord-301505-np4nr7gg.txt summary: Our results show that two models, one involving a GxxxG‐like motif (model I) and an almost opposite form of interaction (model II) are conserved across all α and β integrin types, both in homodimers and homotrimers, with different specificities. 21 Using the TOXCAT assay, 22 a test that measures the oligomerization of a chimeric protein containing a TM helix in the Escherichia coli inner membrane via transcriptional activation of the gene for chloramphenicol acetyltransferase, a sequence critical for integrin ␣IIb-TM homodimerization that involved the GxxxG motif was suggested by Li et al. Our computational results have been obtained independently from any previous experimental data, and clearly show that two right-handed types of homomeric interaction in the transmembrane domain of ␣ and ␤ integrins (models I and II) are evolutionarily conserved. abstract: Integrins are heterodimers, but recent in vitro and in vivo experiments suggest that they are also able to associate through their transmembrane domains to form homomeric interactions. Two fundamental questions are the biological relevance of these aggregates and their form of interaction in the membrane domain. Although in vitro experiments have shown the involvement of a GxxxG‐like motif, several crosslinking in vivo data are consistent with an almost opposite form of interaction between the transmembrane α‐helices. In the present work, we have explored these two questions using molecular dynamics simulations for all available integrin types. We have tested the hypothesis that homomeric interactions are evolutionary conserved, and essential for the cell, using conservative substitutions to filter out nonnative interactions. Our results show that two models, one involving a GxxxG‐like motif (model I) and an almost opposite form of interaction (model II) are conserved across all α and β integrin types, both in homodimers and homotrimers, with different specificities. No conserved interaction was found for homotetramers. Our results are completely independent from experimental data, both during molecular dynamics simulations and in the selection of the correct models. We rationalize previous seemingly conflicting findings regarding the nature of integrin interhelical homomeric interactions. Proteins 2006. © 2006 Wiley‐Liss, Inc. url: https://www.ncbi.nlm.nih.gov/pubmed/16444740/ doi: 10.1002/prot.20882 id: cord-016261-jms7hrmp author: Liu, Chunmei title: Profiling and Searching for RNA Pseudoknot Structures in Genomes date: 2005 words: 4330.0 sentences: 221.0 pages: flesch: 53.0 cache: ./cache/cord-016261-jms7hrmp.txt txt: ./txt/cord-016261-jms7hrmp.txt summary: Profiling models based solely on sequence content such as Hidden Markov Model (HMM) [12] may miss structural homologies when directly used to search genomes for noncoding RNAs containing complex secondary structures. ERPIN searches genomes by sequentially looking for single stem loop motifs contained in the noncoding RNA gene, and reports a hit when significant alignment scores are observed for all the motifs at their corresponding locations. In this paper, we propose a new method to search for RNA pseudoknot structures using a model of multiple CMs. Unlike the model of Brown and Wilson, we use independent CM components to profile the interleaving stems in a pseudoknot. Finally, in order to test the ability of our program to cope with noncoding RNA genes with complex pseudoknot structures, we carried out an experiment where the complete DNA genomes of two bacteria were searched to find the locations of the tmRNA genes. abstract: We developed a new method that can profile and efficiently search for pseudoknot structures in noncoding RNA genes. It profiles interleaving stems in pseudoknot structures with independent Covariance Model (CM) components. The statistical alignment score for searching is obtained by combining the alignment scores from all CM components. Our experiments show that the model can achieve excellent accuracy on both random and biological data. The efficiency achieved by the method makes it possible to search for structures that contain pseudoknot in genomes of a variety of organisms. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120494/ doi: 10.1007/11567752_2 id: cord-010903-kuwy7pbo author: Liu, Jiajun title: Development of Population and Bayesian Models for Applied Use in Patients Receiving Cefepime date: 2020-03-05 words: 3735.0 sentences: 205.0 pages: flesch: 41.0 cache: ./cache/cord-010903-kuwy7pbo.txt txt: ./txt/cord-010903-kuwy7pbo.txt summary: This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. The purpose of this study was to: (1) develop and evaluate a unified cefepime population PK model for adult and pediatric patients, and (2) construct an individualized model that can be utilized to deliver precision cefepime dosing. A unified cefepime population pharmacokinetic model has been developed from adult and pediatric patients and evaluates well in independent populations. The base one-and two-compartment models (without covariate adjustment) produced reasonable fits for observed and Bayesian posterior-predicted cefepime concentrations (R 2 = 84.7% and 85.2%, respectively), but population estimates were unsatisfactory (R 2 = 22.7% and 27.8%, respectively) ( Table 1) . This study created a population and individual PK model for adult and pediatric patients and can serve as a Bayesian prior for precision dosing. abstract: BACKGROUND AND OBJECTIVE: Understanding pharmacokinetic disposition of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. METHODS: Multiple physiologically relevant models were fit to pediatric and adult subject data. To evaluate the final model performance, a withheld group of 12 pediatric patients and two separate adult populations were assessed. RESULTS: Seventy subjects with a total of 604 cefepime concentrations were included in this study. All adults (n = 34) on average weighed 82.7 kg and displayed a mean creatinine clearance of 106.7 mL/min. All pediatric subjects (n = 36) had mean weight and creatinine clearance of 16.0 kg and 195.6 mL/min, respectively. A covariate-adjusted two-compartment model described the observed concentrations well (population model R(2), 87.0%; Bayesian model R(2), 96.5%). In the evaluation subsets, the model performed similarly well (population R(2), 84.0%; Bayesian R(2), 90.2%). CONCLUSION: The identified model serves well for population dosing and as a Bayesian prior for precision dosing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40262-020-00873-3) contains supplementary material, which is available to authorized users. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222999/ doi: 10.1007/s40262-020-00873-3 id: cord-241351-li476eqy author: Liu, Junhua title: CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding date: 2020-05-11 words: 3860.0 sentences: 243.0 pages: flesch: 50.0 cache: ./cache/cord-241351-li476eqy.txt txt: ./txt/cord-241351-li476eqy.txt summary: However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. While prior works report remarkable performance on various crisis classification tasks using NN models and word embeddings, no studies are found to leverage the most recent Natural Language Understanding (NLU) techniques, such as attention-based deep classification models [21] and document-level contextual embeddings [22] , which reportedly improve state-of-the-art performance for many challenging natural language problems from upstream tasks such as Named Entity Recognition and Part of Speech Tagging, to downstream tasks such as Machine Translation and Neural Conversation. In this work, we investigate the transformer approach for crisis classification tasks and propose CrisisBERT, a transformer-based classification model that surpasses conventional linear and deep learning models in performance and robustness. abstract: Classification of crisis events, such as natural disasters, terrorist attacks and pandemics, is a crucial task to create early signals and inform relevant parties for spontaneous actions to reduce overall damage. Despite crisis such as natural disasters can be predicted by professional institutions, certain events are first signaled by civilians, such as the recent COVID-19 pandemics. Social media platforms such as Twitter often exposes firsthand signals on such crises through high volume information exchange over half a billion tweets posted daily. Prior works proposed various crisis embeddings and classification using conventional Machine Learning and Neural Network models. However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. The proposed model also demonstrates superior robustness over benchmark, as it shows marginal performance compromise while extending from 6 to 36 events with only 51.4% additional data points. We also proposed Crisis2Vec, an attention-based, document-level contextual embedding architecture for crisis embedding, which achieve better performance than conventional crisis embedding methods such as Word2Vec and GloVe. To the best of our knowledge, our works are first to propose using transformer-based crisis classification and document-level contextual crisis embedding in the literature. url: https://arxiv.org/pdf/2005.06627v2.pdf doi: nan id: cord-350001-pd2bnqbp author: Liu, L. title: Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility date: 2020-08-07 words: 5516.0 sentences: 276.0 pages: flesch: 53.0 cache: ./cache/cord-350001-pd2bnqbp.txt txt: ./txt/cord-350001-pd2bnqbp.txt summary: We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. On the other hand, compartmental models [e.g., 1, 6, 7] assume a flexible, causal story for the spread of a disease and can also incorporate mobility data as a covariate for predicting the time-varying infection rate of a disease. However, most often though we don''t know the parameters of the model beforehand, but we do have some data that can provide a learning signal to fit the parameters, One such signal is the daily number of new cases of a disease, which can be predicted by a compartmental model as the change in I + R between each day. abstract: In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19's spread. Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case data is unreliable, we infer the model's parameters conditioned on deaths data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic's local growth rate and forecast changes in death rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model's inferred relationship between different kinds of trips and the infection rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19's spread. url: http://medrxiv.org/cgi/content/short/2020.08.06.20169664v1?rss=1 doi: 10.1101/2020.08.06.20169664 id: cord-280683-5572l6bo author: Liu, Laura title: Panel forecasts of country-level Covid-19 infections() date: 2020-10-16 words: 7198.0 sentences: 494.0 pages: flesch: 61.0 cache: ./cache/cord-280683-5572l6bo.txt txt: ./txt/cord-280683-5572l6bo.txt summary: We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. In addition to forecasts from our panel data model, we also consider forecasts based on location-level time series estimates of our trend-break model and a simple SIR model. Once we decompose the set of locations into those that experienced the Covid-19 outbreak early (prior to 2020-03-28) and those that experience the outbreak later on, then we find some evidence that for the late group the panel density forecasts are more accurate than the time-series forecasts. First, as in Section 4, we generate time-series forecasts based on the trend-break model (3) for each location. abstract: We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We find some evidence that information from locations with an early outbreak can sharpen forecast accuracy for late locations. There is generally a lot of uncertainty about the evolution of active infection, due to parameter and shock uncertainty, in particular before and around the peak of the infection path. Over a one-week horizon, the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at https://laurayuliu.com/covid19-panel-forecast/. url: https://api.elsevier.com/content/article/pii/S030440762030347X doi: 10.1016/j.jeconom.2020.08.010 id: cord-016045-od0fr8l0 author: Liu, Ming title: Epidemic-Logistics Network Considering Time Windows and Service Level date: 2019-10-04 words: 5287.0 sentences: 363.0 pages: flesch: 60.0 cache: ./cache/cord-016045-od0fr8l0.txt txt: ./txt/cord-016045-od0fr8l0.txt summary: So, question researched in this study is: Based on the epidemic model analysis, how can we distribute the emergency materials to the whole EMDPs with a time windows constraint? In order to evaluate the practical efficiency of the proposed methodology, parameters of the SIR epidemic model are given as follows, b = d = 10 −5 , β = 10 −5 , α = 0.01, γ = 0.03, and initializing S = 10,000, I = 100, show the fitness and route length vary with iterate times using the new hybrid GA, respectively. Over To satisfy the emergency demand of epidemic diffusion, an efficient emergency service network, which considers how to locate the regional distribution center (RDC) and how to allocate all affected areas to these RDCs, should be urgently designed. abstract: In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120198/ doi: 10.1007/978-981-13-9353-2_13 id: cord-270249-miys1fve author: Liu, Xianbo title: COVID-19: data-driven dynamics, statistical and distributed delay models, and observations date: 2020-08-06 words: 8122.0 sentences: 407.0 pages: flesch: 56.0 cache: ./cache/cord-270249-miys1fve.txt txt: ./txt/cord-270249-miys1fve.txt summary: Based on the parameter identification approach described in this section, the COVID-19 infection dynamics for several countries from North America, South America, Europe, and Asia is found to be captured well by using the generalized logistic function Fig. 4 . By contrast, the outcome of composite global model shown in Fig. 9 , which is comprised of 148 identified sub-models, matches the worldwide COVID-19 data with good consistency for both the total number of infection cases and daily increments. The quarantine rate ζ and the infection rate β are the only two parameters that the authors can use to control against the spreading of the virus in the improved SEIQR model with distributed time delays, given by Eqs. (iii) Based on the data-driven COVID-19 dynamics studied with the distributed delay model, it is evident the measures taken in countries such as China and South Korea were effective in dropping the reproduction number R 1 to be below 0.5. abstract: COVID-19 was declared as a pandemic by the World Health Organization on March 11, 2020. Here, the dynamics of this epidemic is studied by using a generalized logistic function model and extended compartmental models with and without delays. For a chosen population, it is shown as to how forecasting may be done on the spreading of the infection by using a generalized logistic function model, which can be interpreted as a basic compartmental model. In an extended compartmental model, which is a modified form of the SEIQR model, the population is divided into susceptible, exposed, infectious, quarantined, and removed (recovered or dead) compartments, and a set of delay integral equations is used to describe the system dynamics. Time-varying infection rates are allowed in the model to capture the responses to control measures taken, and distributed delay distributions are used to capture variability in individual responses to an infection. The constructed extended compartmental model is a nonlinear dynamical system with distributed delays and time-varying parameters. The critical role of data is elucidated, and it is discussed as to how the compartmental model can be used to capture responses to various measures including quarantining. Data for different parts of the world are considered, and comparisons are also made in terms of the reproductive number. The obtained results can be useful for furthering the understanding of disease dynamics as well as for planning purposes. url: https://www.ncbi.nlm.nih.gov/pubmed/32836818/ doi: 10.1007/s11071-020-05863-5 id: cord-326540-1r4gm2d4 author: Liu, Yuliang title: Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically date: 2020-03-11 words: 7093.0 sentences: 367.0 pages: flesch: 44.0 cache: ./cache/cord-326540-1r4gm2d4.txt txt: ./txt/cord-326540-1r4gm2d4.txt summary: [28] [29] [30] [31] In this paper, we sought to propose an auxiliary diagnosis algorithm that can not only diagnose hyperlipidemia rapidly and accurately according to human hematological parameters but also provide diagnostic markers automatically, which improves the objectivity of traditional methods and the interpretability of deep learning model algorithm. The research method of diagnostic markers based on deep learning technology proposed in this paper can not only automatically synthesize large quantities of data but also effectively simplify the research process, thus reducing the research cost, as shown in Figure 2 . In this paper, an algorithm of attention deep learning is proposed which has the potential to automatically diagnose hyperlipidemia with human hematological parameters and provide the diagnostic markers and the importance of different markers for the diagnosis results at the same time. abstract: INTRODUCTION: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. METHODS: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor’s diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. RESULTS: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. DISCUSSION: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level. url: https://doi.org/10.2147/dmso.s242585 doi: 10.2147/dmso.s242585 id: cord-266424-wchxkdtj author: Lofstedt, Jeanne title: Model to Predict Septicemia in Diarrheic Calves date: 2008-06-28 words: 4633.0 sentences: 228.0 pages: flesch: 46.0 cache: ./cache/cord-266424-wchxkdtj.txt txt: ./txt/cord-266424-wchxkdtj.txt summary: 12, 16 No single laboratory test has emerged as being completely reliable for the early diagnosis of septicemia in farm animal neonates, 12, 17 therefore, various scoring systems and predictive models using easily obtainable historical, clinical, and clinicopathologic data have been developed for this purpose. For a period of time, routine blood cultures were performed on all diarrheic calves presented to the Atlantic Veterinary College Teaching Hospital regardless of whether the clinical or clinicopathologic findings indicated a diagnosis of septicemia. The prevalence of septicemia in this study was identical to that reported for calves with diarrhea, depression, and/or weakness on a veal raising facility, 14 which suggests that the predictive values of the models developed herein may be relevant to other calf populations. abstract: The difficulty in distinguishing between septicemic and nonsepticemic diarrheic calves prompted a study of variables to predict septicemia in diarrheic calves, 28 days old that were presented to a referral institution. The prevalence of septicemia in the study population was 31%. Variables whose values were significantly different (P < .10) between septicemic and nonsepticemic diarrheic calves were selected using stepwise, forward, and backward logistic regression. Variables identified as potentially useful predictors were used in the final model‐building process. Two final models were selected: 1 based on all possible types of predictors (laboratory model) and 1 based only on demographic data and physical examination results (clinical model). In the laboratory model, 5 variables retained significance: serum creatinine > 5.66 mg/dL (> 500 μmol/L) (odds ratio [OR] = 8.63, P = .021), toxic changes in neutrophils ≥ 21 (OR = 2.88, P = .026), failure of passive transfer (OR = 2.72, P = .023), presence of focal infection (OR = 2.68, P = .024), and poor suckle reflex (OR = 4.10, P = .019). Four variables retained significance in the clinical model: age ≤ 5 days (OR = 2.58, P = .006), presence of focal infection (OR = 2.45, P = .006), recumbency (OR = 2.98, P = .011), and absence of a suckling reflex (OR = 3.03, P = .031). The Hosmer—Lemeshow goodness‐of‐fit chi‐square statistics for the laboratory and clinical models had P‐values of .72 and .37, respectively, indicating that the models fit the observed data reasonably well. The laboratory model outperformed the clinical model by a small margin at a predictabilty cutoff of 0.5, however, the predictive abilities of the 2 models were quite similar. The low sensitivities (39% and 40%) of both models at a predicted probability cutoff of 0.5 meant many septicemic calves were not being detected by the models. The specificity of both models at a predicted probability cutoff of 0.5 was .90%, indicating that .90% of nonsepticemic calves would be predicted to be nonsepticemic by the 2 models. The positive and negative predictive values of the models were 66–82%, which indicated the proportion of cases for which a predictive result would be correct in a population with a prevalence of septicemia of 31%. url: https://www.ncbi.nlm.nih.gov/pubmed/10225596/ doi: 10.1111/j.1939-1676.1999.tb01134.x id: cord-032413-zbbpfaj4 author: Lu, Shasha title: A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs date: 2020-09-03 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R(2)) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R(2) and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503283/ doi: 10.3390/ma13173902 id: cord-012866-p3mb7r0v author: Luo, Yan title: Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis date: 2020-06-12 words: 5247.0 sentences: 264.0 pages: flesch: 45.0 cache: ./cache/cord-012866-p3mb7r0v.txt txt: ./txt/cord-012866-p3mb7r0v.txt summary: title: Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data meta-analysis The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). DISCUSSION: This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. Individual participant data meta-analysis (IPD-MA) has been previously employed to develop prediction models for treatment effects [3] [4] [5] [6] . In the second stage, this baseline risk score will be used as a prognostic factor and an effect modifier in an IPD meta-regression model to estimate the individualized treatment effects of CTZ. abstract: BACKGROUND: A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). METHODS: We will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individual-level data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACR-EULAR Boolean or index-based remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a two-stage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a meta-regression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using R2Jags. DISCUSSION: This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number pending (ID#157595). url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477831/ doi: 10.1186/s13643-020-01401-x id: cord-002474-2l31d7ew author: Lv, Yang title: Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China date: 2017-04-03 words: 4938.0 sentences: 270.0 pages: flesch: 51.0 cache: ./cache/cord-002474-2l31d7ew.txt txt: ./txt/cord-002474-2l31d7ew.txt summary: title: Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. Besides, according to the tested microbial density and the identified genome sequence of collected microorganisms, the hygrothermal response experiment of dominant fungal was detected, and the fitting analysis was carried out based on the prediction model, followed by a series of statistical analysis. The unit A showed the obvious microbial contamination status, though all components and airborne microorganism meet the Hygienic specification of central air conditioning ventilation system in public buildings of China 22 . abstract: The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5377260/ doi: 10.1038/srep44190 id: cord-324254-qikr9ryf author: Lyócsa, Štefan title: FX Market Volatility Modelling: Can we use low-frequency data? date: 2020-09-30 words: 5763.0 sentences: 342.0 pages: flesch: 56.0 cache: ./cache/cord-324254-qikr9ryf.txt txt: ./txt/cord-324254-qikr9ryf.txt summary: With an increased forecast horizon, low-frequency volatility models become competitive, suggesting that if high-frequency data are not available, low-frequency data can be used to estimate and predict long-term volatility in FX markets. Despite the wide interest of academia, the existing literature provides evidence only that i) volatility estimators based on high-frequency data are theoretically preferred (Andersen et al., 1 The basic specification of the HAR model has also been enhanced, e.g., by the inclusion of semivariances (Patton and Sheppard, 2015) , the disentanglement of the realized volatility into continuous and jump components (e.g., Andersen et al., 2012) , the introduction of the measurement error of the realized volatility into the HAR model as in (Bollerslev et al., 2016) , the inclusion of nontrading volatility components (Lyócsa and Molnár, 2017, Lyócsa and Todorova, 2020) , and the use of hidden Markov chains (Luo et al., 2019) . abstract: High-frequency data tend to be costly, subject to microstructure noise, difficult to manage, and lead to high computational costs. Is it always worth the extra effort? We compare the forecasting accuracy of low- and high-frequency volatility models on the market of six major foreign exchange market (FX) pairs. Our results indicate that for short-forecast horizons, high-frequency models dominate their low-frequency counterparts, particularly in periods of increased volatility. With an increased forecast horizon, low-frequency volatility models become competitive, suggesting that if high-frequency data are not available, low-frequency data can be used to estimate and predict long-term volatility in FX markets. url: https://doi.org/10.1016/j.frl.2020.101776 doi: 10.1016/j.frl.2020.101776 id: cord-004157-osol7wdp author: Ma, Junling title: Estimating epidemic exponential growth rate and basic reproduction number date: 2020-01-08 words: 5474.0 sentences: 393.0 pages: flesch: 60.0 cache: ./cache/cord-004157-osol7wdp.txt txt: ./txt/cord-004157-osol7wdp.txt summary: Typically, for an epidemic model that contains a single transmission rate b, if all other parameters can be estimated independently to the exponential growth rate l, then l determines b, and thus determines R 0 . Wallinga and Lipsitch (Wallinga & Lipsitch, 2006 ) developed a non-parametric method to infer the basic reproduction number from the exponential growth rate without assuming a model. Let cðtÞdt be the number of new infections during the time interval ½t;t þ dt, that is, cðtÞ is the incidence rate, and SðtÞ be the average susceptibility of the population, i.e., the expected susceptibility of a randomly selected individual. Equation (5) links the exponential growth rate to the basic reproduction number though the serial interval distribution only. That is, if we can estimate the serial interval distribution and the exponential growth rate independently, that we can infer the basic reproduction number. Note that the serial interval distribution wðtÞ can be estimated independently to the exponential growth rate. abstract: The initial exponential growth rate of an epidemic is an important measure of the severeness of the epidemic, and is also closely related to the basic reproduction number. Estimating the growth rate from the epidemic curve can be a challenge, because of its decays with time. For fast epidemics, the estimation is subject to over-fitting due to the limited number of data points available, which also limits our choice of models for the epidemic curve. We discuss the estimation of the growth rate using maximum likelihood method and simple models. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962332/ doi: 10.1016/j.idm.2019.12.009 id: cord-103913-jgko7b0j author: Macedo, A. M. S. title: A comparative analysis between a SIRD compartmental model and the Richards growth model date: 2020-08-06 words: 3034.0 sentences: 182.0 pages: flesch: 57.0 cache: ./cache/cord-103913-jgko7b0j.txt txt: ./txt/cord-103913-jgko7b0j.txt summary: We illustrate the use of the map between these two models by fitting the fatality curves of the COVID-19 epidemic data in Italy, Germany, Sweden, Netherlands, Cuba, and Japan. Here we improve on this analysis in two ways: (i) we extend the SIR model to a SIRD model by incorporating the deceased compartment, which is then used as the basis for the map onto the Richards model; (ii) the parameters of the SIRD model are allowed to have a time dependence, which is crucial to gain some efficacy in describing realistic cumulative epidemic curves of COVID-19. where C(t) is the cumulative number of cases/deaths at time t, r is the growth rate at the early stage, K is the final epidemic size, and the parameter α measures the asymmetry with respect to the s-shaped curve of the standard logistic model, which is recovered for α = 1. abstract: We propose a compartmental SIRD model with time-dependent parameters that can be used to give epidemiological interpretations to the phenomenological parameters of the Richards growth model. We illustrate the use of the map between these two models by fitting the fatality curves of the COVID-19 epidemic data in Italy, Germany, Sweden, Netherlands, Cuba, and Japan. url: http://medrxiv.org/cgi/content/short/2020.08.04.20168120v1?rss=1 doi: 10.1101/2020.08.04.20168120 id: cord-309096-vwbpkpxd author: Magdon-Ismail, Malik title: Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics date: 2020-03-20 words: 4881.0 sentences: 412.0 pages: flesch: 67.0 cache: ./cache/cord-309096-vwbpkpxd.txt txt: ./txt/cord-309096-vwbpkpxd.txt summary: We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. We also follow a data-driven machine learning approach to understand early dynamics of COVID-19 on the first 54 days of US confirmed infection reports (downloadable from the European Center For Disease Control). β, asymptomatic infectious force governing exponential spread γ, virulence, the fraction of mild cases that become serious later k, lag time for mild infection to become serious (an incubation time) M 0 , Unconfirmed mild asymptomatic infections at time 0 Figure 1 are the model predictions (blue envelope) and the red circles are the observed infection counts. Our results demonstrate the effectiveness of simple robust models for predicting pandemic dynamics from early data. From this solution as a starting point, we can further optimize γ, β using a gradient descent which minimizes an error-measure that captures how well the parameters β, γ, k, M 0 reproduce the observed dynamics in Figure 2 , see for example Abu-Mostafa et al. abstract: We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts. url: https://doi.org/10.1101/2020.03.17.20037309 doi: 10.1101/2020.03.17.20037309 id: cord-140977-mg04drna author: Maltezos, S. title: Methodology for Modelling the new COVID-19 Pandemic Spread and Implementation to European Countries date: 2020-06-27 words: 3985.0 sentences: 211.0 pages: flesch: 59.0 cache: ./cache/cord-140977-mg04drna.txt txt: ./txt/cord-140977-mg04drna.txt summary: Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country''s characteristics and the parameters of the parametrization. where the function c(t) applied in an epidemic spread represents the rate of the infected individuals as the new daily reported cases (DRC) and coincides with the function I(t) in the SIR model, as we can see in the following. The more analytical approach, in the general case from the mathematical point of view, comes from the fundamental study of the epidemic growth and includes a number of terms in a form of double summation related to the inverse Laplace Transform of a rational function given in [8] , referring to the "Earlier stages of an epidemic in a large population". abstract: After the breakout of the disease caused by the new virus COVID-19, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this repulsive disease in the future. url: https://arxiv.org/pdf/2006.15385v2.pdf doi: nan id: cord-321735-c40m2o5l author: Manca, Davide title: A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under Covid-19 pandemic date: 2020-06-04 words: 7164.0 sentences: 323.0 pages: flesch: 53.0 cache: ./cache/cord-321735-c40m2o5l.txt txt: ./txt/cord-321735-c40m2o5l.txt summary: Besides the predicted numbers, those models allowed also forecasting the different phases of the pandemic and quantifying some basic indicators about the daily variations, the key times, the key figures, the expected decrease, the progressive reach of a maximum plateau before facing with the decrease of ICU beds for Covid-19 which we are measuring right now. Usually, patients remain in ICU wards at least fifteen days (with twenty-day stay the standard value) (Cutuli, 2020) and, respect to Covid-19 emergency, this quite a long time allows describing the whole ICU beds inflation period with curves such as the logistic (Hosmer et al., 2013) or the Gompertz (Panik, 2014) ones. The models of Section 2.3 applied to the case study of Lombardy and Italy proved their efficiency in reproducing real data and were used to forecast the evolution of key parameters as the number of ICU patients and deaths on both short and long-time horizons. abstract: The different stages of Covid-19 pandemic can be described by two key-variables: ICU patients and deaths in hospitals. We propose simple models that can be used by medical doctors and decision makers to predict the trends on both short-term and long-term horizons. Daily updates of the models with real data allow forecasting some key indicators for decision-making (an Excel file in the Supplemental material allows computing them). These are beds allocation, residence time, doubling time, rate of renewal, maximum daily rate of change (positive/negative), halfway points, maximum plateaus, asymptotic conditions, and dates and time intervals when some key thresholds are overtaken. Doubling time of ICU beds for Covid-19 emergency can be as low as 2-3 days at the outbreak of the pandemic. The models allow identifying the possible departure of the phenomenon from the predicted trend and thus can play the role of early warning systems and describe further outbreaks. url: https://www.ncbi.nlm.nih.gov/pubmed/32565584/ doi: 10.1016/j.compchemeng.2020.106945 id: cord-255557-k0xat0u7 author: Mao, Liang title: Modeling monthly flows of global air travel passengers: An open-access data resource date: 2015-10-31 words: 4783.0 sentences: 254.0 pages: flesch: 55.0 cache: ./cache/cord-255557-k0xat0u7.txt txt: ./txt/cord-255557-k0xat0u7.txt summary: title: Modeling monthly flows of global air travel passengers: An open-access data resource Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). First, we refined existing models developed by Huang et al.(2013) to a finer temporal scale and predicted the monthly air passenger flows between directly connected airports worldwide. Second, we attempt to understand the monthly WAN as a dynamic by measuring the variation of air passenger flows by month, by route, and by airport. Our model views the air passenger flow as an outcome of spatial interactions between a pair of origin and destination airports, which can be formulated into a multiplicative function of node and link characteristics, as shown in Eq. Fig. 4 shows the monthly variation of the WAN in terms of its flight routes, passenger volume, and role of airports. abstract: Abstract The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change. url: https://www.ncbi.nlm.nih.gov/pubmed/32288373/ doi: 10.1016/j.jtrangeo.2015.08.017 id: cord-268298-25brblfq author: Mao, Liang title: Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—An agent-based simulation date: 2014-03-04 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: A typical epidemic often involves the transmission of a disease, the flow of information regarding the disease, and the spread of human preventive behaviors against the disease. These three processes diffuse simultaneously through human social networks, and interact with one another, forming negative and positive feedback loops in the complex human-disease systems. Few studies, however, have been devoted to coupling all the three diffusions together and representing their interactions. To fill the knowledge gap, this article proposes a spatially explicit agent-based model to simulate a triple-diffusion process in a metropolitan area of 1 million people. The individual-based approach, network model, behavioral theories, and stochastic processes are used to formulate the three diffusions and integrate them together. Compared to the observed facts, the model results reasonably replicate the trends of influenza spread and information propagation. The model thus could be a valid and effective tool to evaluate information/behavior-based intervention strategies. Besides its implications to the public health, the research findings also contribute to network modeling, systems science, and medical geography. url: https://www.ncbi.nlm.nih.gov/pubmed/32287519/ doi: 10.1016/j.apgeog.2014.02.005 id: cord-143539-gvt25gac author: Marmarelis, Myrl G. title: Latent Embeddings of Point Process Excitations date: 2020-05-05 words: 5357.0 sentences: 355.0 pages: flesch: 54.0 cache: ./cache/cord-143539-gvt25gac.txt txt: ./txt/cord-143539-gvt25gac.txt summary: By performing synthetic experiments on short records as well as an investigation into options markets and pathogens, we demonstrate that learning the embedding alongside a point process model uncovers the coherent, rather than spurious, interactions. The propagation of disease [1] , news topics [2] , crime patterns [3, 4] , neuronal firings [5] , and market trade-level activity [6, 7] naturally suit the form of diachronic point processes with an underlying causal-interaction network. Furnished with the causality estimates in Eq. 6 (the "Expectation" step), we perform projected gradient ascent by setting partial derivatives of the complete-data log-likelihood with respect to each kernel parameter to zero (the "Maximization" step). We demonstrated the viability of estimating embeddings for events in an interpretable metric space tied to a self-exciting point process. The block point process model for continuous-time event-based dynamic networks Latent self-exciting point process model for spatial-temporal networks abstract: When specific events seem to spur others in their wake, marked Hawkes processes enable us to reckon with their statistics. The underdetermined empirical nature of these event-triggering mechanisms hinders estimation in the multivariate setting. Spatiotemporal applications alleviate this obstacle by allowing relationships to depend only on relative distances in real Euclidean space; we employ the framework as a vessel for embedding arbitrary event types in a new latent space. By performing synthetic experiments on short records as well as an investigation into options markets and pathogens, we demonstrate that learning the embedding alongside a point process model uncovers the coherent, rather than spurious, interactions. url: https://arxiv.org/pdf/2005.02515v3.pdf doi: nan id: cord-335689-8a704p38 author: Martin, Gerardo title: Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk date: 2018-03-19 words: 8557.0 sentences: 462.0 pages: flesch: 53.0 cache: ./cache/cord-335689-8a704p38.txt txt: ./txt/cord-335689-8a704p38.txt summary: We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. One approach to identify areas at risk from emerging infectious diseases is to model the ecological niche of the causal agent and its reservoir host with spatiality explicit climatic data, and to use the model to predict their geographic distribution (Escobar and Craft 2016) . We took the following steps to build these models: (1) assigned presence points to the most likely reservoir host species present at spillover locations, (2) computed the optimal size of spatial units and determined appropriate explanatory climatic variables, (3) selected the model structure (linear and quadratic terms and interactions with AIC and cross-validation), (4) selected priors for the Bayesian model, (5) fitted the Bayesian model, (6) cross-validated, and (7) transferred models to climate change scenarios (Fig. 1 ). abstract: Disease risk mapping is important for predicting and mitigating impacts of bat-borne viruses, including Hendra virus (Paramyxoviridae:Henipavirus), that can spillover to domestic animals and thence to humans. We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. conspicillatus. We included additional climatic variables that might affect spillover risk through other biological processes (such as bat or horse behaviour, plant phenology and bat foraging habitat). Models were fit with a Poisson point process model and a log-Gaussian Cox process. In response to climate change, risk expanded southwards due to an expansion of P. alecto suitable habitat, which increased the number of horses at risk by 175–260% (110,000–165,000). In the northern limits of the current distribution, spillover risk was highly uncertain because of model extrapolation to novel climatic conditions. The extent of areas at risk of spillover from P. conspicillatus was predicted shrink. Due to a likely expansion of P. alecto into these areas, it could replace P. conspicillatus as the main HeV reservoir. We recommend: (1) HeV monitoring in bats, (2) enhancing HeV prevention in horses in areas predicted to be at risk, (3) investigate and develop mitigation strategies for areas that could experience reservoir host replacements. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10393-018-1322-9) contains supplementary material, which is available to authorized users. url: https://www.ncbi.nlm.nih.gov/pubmed/29556762/ doi: 10.1007/s10393-018-1322-9 id: cord-175366-jomeywqr author: Massonis, Gemma title: Structural Identifiability and Observability of Compartmental Models of the COVID-19 Pandemic date: 2020-06-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights -- as well as the possibility of controlling the system -- may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements. url: https://arxiv.org/pdf/2006.14295v1.pdf doi: nan id: cord-317643-pk8cabxj author: Masud, Mehedi title: Convolutional neural network-based models for diagnosis of breast cancer date: 2020-10-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models’ training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers. url: https://www.ncbi.nlm.nih.gov/pubmed/33052172/ doi: 10.1007/s00521-020-05394-5 id: cord-156676-wes5my9e author: Masud, Sarah title: Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter date: 2020-10-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Online hate speech, particularly over microblogging platforms like Twitter, has emerged as arguably the most severe issue of the past decade. Several countries have reported a steep rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech is one of the emerging research areas, the generation and spread of topic-dependent hate in the information network remain under-explored. In this work, we focus on exploring user behaviour, which triggers the genesis of hate speech on Twitter and how it diffuses via retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks, comprising over 161 million tweets from more than $41$ million unique users. We also collect over 600k contemporary news articles published online. We characterize different signals of information that govern these dynamics. Our analyses differentiate the diffusion dynamics in the presence of hate from usual information diffusion. This motivates us to formulate the modelling problem in a topic-aware setting with real-world knowledge. For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a macro F1-score of 0.85, outperforming multiple state-of-the-art models. Our analysis reveals the superlative power of RETINA to predict the retweet dynamics of hateful content compared to the existing diffusion models. url: https://arxiv.org/pdf/2010.04377v1.pdf doi: nan id: cord-017181-ywz6w2po author: Maus, Carsten title: Component-Based Modelling of RNA Structure Folding date: 2008 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: RNA structure is fundamentally important for many biological processes. In the past decades, diverse structure prediction algorithms and tools were developed but due to missing descriptions in clearly defined modelling formalisms it’s difficult or even impossible to integrate them into larger system models. We present an RNA secondary structure folding model described in ml-Devs, a variant of the Devs formalism, which enables the hierarchical combination with other model components like RNA binding proteins. An example of transcriptional attenuation will be given where model components of RNA polymerase, the folding RNA molecule, and the translating ribosome play together in a composed dynamic model. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121681/ doi: 10.1007/978-3-540-88562-7_8 id: cord-296388-ayfdsn07 author: Maziarz, Mariusz title: Agent‐based modelling for SARS‐CoV‐2 epidemic prediction and intervention assessment: A methodological appraisal date: 2020-08-21 words: 4560.0 sentences: 240.0 pages: flesch: 41.0 cache: ./cache/cord-296388-ayfdsn07.txt txt: ./txt/cord-296388-ayfdsn07.txt summary: CONCLUSIONS: Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference‐making evidence. While the 2009 swine flu pandemic was the motivation for constructing AceMod, the model was not intended to accurately represent the outbreak of the H1N1 strain, but rather as a generalized framework for studying how an infectious disease spreads through the social interactions of Australians. In cases like the current pandemic, effective interventions may best be aimed at the societal level and therefore mechanistic models that integrate social factors, human behaviour and biological aspects (something that the ABM discussed here attempts to do) are arguably best suited for providing understanding and suggesting policy decisions. 10 Our claim that AceMod calibrated for SARS-CoV-2 bears similarity to the actual mechanism of the epidemic depends on the accuracy of the empirical results used as an input for this model. Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal abstract: BACKGROUND: Our purpose is to assess epidemiological agent‐based models—or ABMs—of the SARS‐CoV‐2 pandemic methodologically. The rapid spread of the outbreak requires fast‐paced decision‐making regarding mitigation measures. However, the evidence for the efficacy of non‐pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi‐experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS‐CoV‐2. To address the pressing policy questions that have emerged as a result of COVID‐19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent‐based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions. METHODS: To address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID‐19 epidemic in Australia) as a case study of the modelling practice. RESULTS: Our example shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS‐CoV‐2 are represented sufficiently accurately. This is the case because these modellers treat empirical results as inputs for constructing modelling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables. CONCLUSIONS: Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference‐making evidence. Consequently, they may also adequately describe the effects of possible interventions. Finally, we discuss the limitations of ABMs and put forward policy recommendations. url: https://www.ncbi.nlm.nih.gov/pubmed/32820573/ doi: 10.1111/jep.13459 id: cord-332922-2qjae0x7 author: Mbuvha, Rendani title: Bayesian inference of COVID-19 spreading rates in South Africa date: 2020-08-05 words: 3224.0 sentences: 169.0 pages: flesch: 52.0 cache: ./cache/cord-332922-2qjae0x7.txt txt: ./txt/cord-332922-2qjae0x7.txt summary: In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. In this work we combine Bayesian inference with the compartmental SEIR and SIR models to infer time varying spreading rates that allow for quantification of the impact of government interventions in South Africa. SIR and SEIR model parameter inference was performed using confirmed cases data up to and including 20 April 2020 and MCMC samplers described in the methodology section. abstract: The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease. url: https://www.ncbi.nlm.nih.gov/pubmed/32756608/ doi: 10.1371/journal.pone.0237126 id: cord-329534-deoyowto author: McBryde, Emma S. title: Role of modelling in COVID-19 policy development date: 2020-06-18 words: 3143.0 sentences: 155.0 pages: flesch: 42.0 cache: ./cache/cord-329534-deoyowto.txt txt: ./txt/cord-329534-deoyowto.txt summary: Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. In this paper, we describe ways in which models have influenced policy, from the early stages of the outbreak to the current date -and anticipate the future value of models in informing suppression efforts, vaccination programs and economic interventions. For COVID-19, strategies may differ between countries depending on the acuity of the epidemic, the age groups driving the infection or at higher risk for severe disease, and the age structure of the population. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. abstract: Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus’s infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies. url: https://api.elsevier.com/content/article/pii/S1526054220300981 doi: 10.1016/j.prrv.2020.06.013 id: cord-203620-mt9ivgzi author: McCreery, Clara H. title: Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs date: 2020-08-04 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema. COVID-19 has only exacerbated this problem. Almost every government agency and healthcare organization has tried to meet the informational need of users by building online FAQs, but there is no way for people to ask their question and know if it is answered on one of these pages. While many research efforts have focused on the problem of general question similarity, these approaches do not generalize well to domains that require expert knowledge to determine semantic similarity, such as the medical domain. In this paper, we show how a double fine-tuning approach of pretraining a neural network on medical question-answer pairs followed by fine-tuning on medical question-question pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity. While other pretraining tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, an accuracy of 80.0% with a much smaller training set, and an accuracy of 84.5% when the full corpus of medical question-answer data is used. We also describe a currently live system that uses the trained model to match user questions to COVID-related FAQs. url: https://arxiv.org/pdf/2008.13546v1.pdf doi: nan id: cord-288183-pz3t29a7 author: McKibbin, Warwick J. title: Chapter 15 A Global Approach to Energy and the Environment The G-Cubed Model date: 2013-12-31 words: 20679.0 sentences: 1069.0 pages: flesch: 53.0 cache: ./cache/cord-288183-pz3t29a7.txt txt: ./txt/cord-288183-pz3t29a7.txt summary: Macroeconomic policy issues in Japan have been examined using G-Cubed by McKibbin (2002) and Callen and McKibbin (2003) where the experience of Japan during the 1990s was captured by the model as a serious of policy errors particularly in announcing fiscal expansion and generating crowding out through asset markets, but then not delivering the fiscal spending causing a persistent downward drop in GDP; in India by McKibbin and Singh (2003) where nominal income targeting was shown to be a far better monetary regime than inflation targeting given the prevalence of supply side rather than demand-side shocks in the Indian economy; in China by McKibbin and Tang (2000) and McKibbin and Huang (2000) where financial reforms where found to have profound effects on economic growth and the balance of payments adjustment but that a loss in confidence in China could devastate economic growth; and in Asia in McKibbin and Le (2004) and McKibbin and Chanthapun (1999) where flexible exchange rate regimes were found to be far better at insulating East Asian economies against global economic shocks that pegging to either the US dollar or a common Asia currency. abstract: Abstract G-Cubed is a multi-country, multi-sector, intertemporal general equilibrium model that has been used to study a variety of policies in the areas of environmental regulation, tax reform, monetary and fiscal policy, and international trade. It is designed to bridge the gaps between three areas of research – econometric general equilibrium modeling, international trade theory, and modern macroeconomics – by incorporating the best features of each. This chapter describes the theoretical and empirical structure of the model, summarizes its applications and contributions to the literature, and discusses two example applications in detail. url: https://api.elsevier.com/content/article/pii/B9780444595683000158 doi: 10.1016/b978-0-444-59568-3.00015-8 id: cord-275258-azpg5yrh author: Mead, Dylan J.T. title: Visualization of protein sequence space with force-directed graphs, and their application to the choice of target-template pairs for homology modelling date: 2019-07-26 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The protein sequence-structure gap results from the contrast between rapid, low-cost deep sequencing, and slow, expensive experimental structure determination techniques. Comparative homology modelling may have the potential to close this gap by predicting protein structure in target sequences using existing experimentally solved structures as templates. This paper presents the first use of force-directed graphs for the visualization of sequence space in two dimensions, and applies them to the choice of suitable RNA-dependent RNA polymerase (RdRP) target-template pairs within human-infective RNA virus genera. Measures of centrality in protein sequence space for each genus were also derived and used to identify centroid nearest-neighbour sequences (CNNs) potentially useful for production of homology models most representative of their genera. Homology modelling was then carried out for target-template pairs in different species, different genera and different families, and model quality assessed using several metrics. Reconstructed ancestral RdRP sequences for individual genera were also used as templates for the production of ancestral RdRP homology models. High quality ancestral RdRP models were consistently produced, as were good quality models for target-template pairs in the same genus. Homology modelling between genera in the same family produced mixed results and inter-family modelling was unreliable. We present a protocol for the production of optimal RdRP homology models for use in further experiments, e.g. docking to discover novel anti-viral compounds. (219 words) url: https://www.sciencedirect.com/science/article/pii/S109332631930333X doi: 10.1016/j.jmgm.2019.07.014 id: cord-016364-80l5mua2 author: Menotti-Raymond, Marilyn title: The Domestic Cat, Felis catus, as a Model of Hereditary and Infectious Disease date: 2008 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The domestic cat, currently the most frequent of companion animals, has enjoyed a medical surveillance, as a nonprimate species, second only to the dog. With over 200 hereditary disease pathologies reported in the cat, the clinical and physiological study of these feline hereditary diseases provides a strong comparative medicine opportunity for prevention, diagnostics, and treatment studies in a laboratory setting. Causal mutations have been characterized in 19 felid genes, with the largest representation from lysosomal storage enzyme disorders. Corrective therapeutic strategies for several disorders have been proposed and examined in the cat, including enzyme replacement, heterologous bone marrow transplantation, and substrate reduction therapy. Genomics tools developed in the cat, including the recent completion of the 2-fold whole genome sequence of the cat and genome browser, radiation hybrid map of 1793 integrated coding and microsatellite loci, a 5-cM genetic linkage map, arrayed BAC libraries, and flow sorted chromosomes, are providing resources that are being utilized in mapping and characterization of genes of interest. A recent report of the mapping and characterization of a novel causative gene for feline spinal muscular atrophy marked the first identification of a disease gene purely from positional reasoning. With the development of genomic resources in the cat and the application of complementary comparative tools developed in other species, the domestic cat is emerging as a promising resource of phenotypically defined genetic variation of biomedical significance. Additionally, the cat has provided several useful models for infectious disease. These include feline leukemia and feline sarcoma virus, feline coronavirus, and Type C retroviruses that interact with cellular oncogenes to induce leukemia, lymphoma, and sarcoma. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120622/ doi: 10.1007/978-1-59745-285-4_25 id: cord-003243-u744apzw author: Michael, Edwin title: Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination date: 2018-10-08 words: 10321.0 sentences: 336.0 pages: flesch: 33.0 cache: ./cache/cord-003243-u744apzw.txt txt: ./txt/cord-003243-u744apzw.txt summary: METHODOLOGY AND PRINCIPAL FINDINGS: We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYM-FASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. abstract: BACKGROUND: Mathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models. METHODOLOGY AND PRINCIPAL FINDINGS: We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination. SIGNIFICANCE: Knowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175292/ doi: 10.1371/journal.pntd.0006674 id: cord-027316-echxuw74 author: Modarresi, Kourosh title: Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model date: 2020-05-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Every individual text or document is generated for specific purpose(s). Sometime, the text is deployed to convey a specific message about an event or a product. Other occasions, it may be communicating a scientific breakthrough, development or new model and so on. Given any specific objective, the creators and the users of documents may like to know which part(s) of the documents are more influential in conveying their specific messages or achieving their objectives. Understanding which parts of a document has more impact on the viewer’s perception would allow the content creators to design more effective content. Detecting the more impactful parts of a content would help content users, such as advertisers, to concentrate their efforts more on those parts of the content and thus to avoid spending resources on the rest of the document. This work uses a regularized attention-based method to detect the most influential part(s) of any given document or text. The model uses an encoder-decoder architecture based on attention-based decoder with regularization applied to the corresponding weights. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304011/ doi: 10.1007/978-3-030-50420-5_20 id: cord-351411-q9kqjvvf author: Moghadas, Seyed M title: Improving public health policy through infection transmission modelling: Guidelines for creating a Community of Practice date: 2015 words: 3882.0 sentences: 189.0 pages: flesch: 37.0 cache: ./cache/cord-351411-q9kqjvvf.txt txt: ./txt/cord-351411-q9kqjvvf.txt summary: Its objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. The workshop objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. In the final session, "Developing our network and communities of practice", participants reflected on earlier presentations and discussions to clarify what is needed to continue collaboration and knowledge exchange that can increase the value of research modelling in public health. abstract: BACKGROUND: Despite significant research efforts in Canada, real application of modelling in public health decision making and practice has not yet met its full potential. There is still room to better address the diversity of the Canadian population and ensure that research outcomes are translated for use within their relevant contexts. OBJECTIVES: To strengthen connections to public health practice and to broaden its scope, the Pandemic Influenza Outbreak Research Modelling team partnered with the National Collaborating Centre for Infectious Diseases to hold a national workshop. Its objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. METHOD: The Use of Mathematical Modelling in Public Health Decision Making for Infectious Diseases workshop brought together research modellers, public health professionals, policymakers and other experts from across the country. Invited presentations set the context for topical discussions in three sessions. A final session generated reflections and recommendations for new opportunities and tasks. CONCLUSIONS: Gaps in content and research include the lack of standard frameworks and a glossary for infectious disease modelling. Consistency in terminology, clear articulation of model parameters and assumptions, and sustained collaboration will help to bridge the divide between research and practice. url: https://www.ncbi.nlm.nih.gov/pubmed/26361486/ doi: nan id: cord-190495-xpfbw7lo author: Molnar, Tamas G. title: Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays date: 2020-09-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA. url: https://arxiv.org/pdf/2009.10262v1.pdf doi: nan id: cord-175015-d2am45tu author: Moran, Rosalyn J. title: Estimating required 'lockdown' cycles before immunity to SARS-CoV-2: Model-based analyses of susceptible population sizes, 'S0', in seven European countries including the UK and Ireland date: 2020-04-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5th 2020. Two distinct generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model and second: a partially observable Markov Decision Process (MDP) or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (S0), as well as epidemic parameters. Parameter estimation (data fitting) was performed using a standard Bayesian scheme (variational Laplace) designed to allow for latent unobservable states and uncertainty in model parameters. Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. The peaks of the current waves were predicted to be in the past for four countries (Italy, Spain, Germany and Switzerland) and to emerge in 0.5-2 weeks in Ireland and 1-3 weeks in the UK. For France one model estimated the peak within the past week and the other in the future in two weeks. Crucially, Maximum a posteriori (MAP) estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. With a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% for Germany and 12.9% for Switzerland. Our results indicate that after the current wave, a large proportion of the total population will remain without immunity. url: https://arxiv.org/pdf/2004.05060v1.pdf doi: nan id: cord-317993-012hx4kc author: Movia, Dania title: Preclinical Development of Orally Inhaled Drugs (OIDs)—Are Animal Models Predictive or Shall We Move Towards In Vitro Non-Animal Models? date: 2020-07-24 words: 6885.0 sentences: 369.0 pages: flesch: 42.0 cache: ./cache/cord-317993-012hx4kc.txt txt: ./txt/cord-317993-012hx4kc.txt summary: SIMPLE SUMMARY: This commentary focuses on the methods currently available to test the efficacy and safety of new orally inhaled drugs for the treatment of uncurable respiratory diseases, such as chronic obstructive pulmonary disease (COPD), cystic fibrosis or lung cancer, prior to entering human experimentation. Inhalation is the preferred administration method for treating respiratory diseases [13] , as: (i) it delivers the drug directly at the site of action, resulting in a rapid therapeutic onset with considerably lower drug doses, (ii) it is painless and minimally invasive thus improving patients'' compliance, and (iii) it avoids first-pass metabolism, providing optimal pharmacokinetic conditions for drug absorption and reducing systemic side effects [14] [15] [16] . In the context of OID preclinical testing, lung organoids can be used for modeling respiratory diseases and, therefore, as a platform for screening the efficacy of inhalation therapies [115, 116] . abstract: SIMPLE SUMMARY: This commentary focuses on the methods currently available to test the efficacy and safety of new orally inhaled drugs for the treatment of uncurable respiratory diseases, such as chronic obstructive pulmonary disease (COPD), cystic fibrosis or lung cancer, prior to entering human experimentation. The key question that the authors try to address in this manuscript is whether there is value in using and refining current animal models for this pre-clinical testing, or whether these should be relinquished in favor of new, more human-relevant non-animal methods. ABSTRACT: Respiratory diseases constitute a huge burden in our society, and the global respiratory drug market currently grows at an annual rate between 4% and 6%. Inhalation is the preferred administration method for treating respiratory diseases, as it: (i) delivers the drug directly at the site of action, resulting in a rapid onset; (ii) is painless, thus improving patients’ compliance; and (iii) avoids first-pass metabolism reducing systemic side effects. Inhalation occurs through the mouth, with the drug generally exerting its therapeutic action in the lungs. In the most recent years, orally inhaled drugs (OIDs) have found application also in the treatment of systemic diseases. OIDs development, however, currently suffers of an overall attrition rate of around 70%, meaning that seven out of 10 new drug candidates fail to reach the clinic. Our commentary focuses on the reasons behind the poor OIDs translation into clinical products for the treatment of respiratory and systemic diseases, with particular emphasis on the parameters affecting the predictive value of animal preclinical tests. We then review the current advances in overcoming the limitation of animal animal-based studies through the development and adoption of in vitro, cell-based new approach methodologies (NAMs). url: https://doi.org/10.3390/ani10081259 doi: 10.3390/ani10081259 id: cord-326831-dvg0isgt author: Muhammad, L. J. title: Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery date: 2020-06-21 words: 2707.0 sentences: 145.0 pages: flesch: 52.0 cache: ./cache/cord-326831-dvg0isgt.txt txt: ./txt/cord-326831-dvg0isgt.txt summary: The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor. Data mining algorithm which includes decision tree, support vector machine, naive Bayes, logistic regression random forest, and K-nearest neighbor were applied directly on the dataset using python programming language to develop the models. abstract: Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients’ recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor. url: https://www.ncbi.nlm.nih.gov/pubmed/33063049/ doi: 10.1007/s42979-020-00216-w id: cord-010977-fwz7chzf author: Myserlis, Pavlos title: Translational Genomics in Neurocritical Care: a Review date: 2020-02-20 words: 11990.0 sentences: 519.0 pages: flesch: 31.0 cache: ./cache/cord-010977-fwz7chzf.txt txt: ./txt/cord-010977-fwz7chzf.txt summary: In this review, we describe some of the approaches being taken to apply translational genomics to the study of diseases commonly encountered in the neurocritical care setting, including hemorrhagic and ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and status epilepticus, utilizing both forward and reverse genomic translational techniques. Termed "reverse translation," this approach starts with humans as the model system, utilizing genomic associations to derive new information about biological mechanisms that can be in turn studied further in vitro and in animal models for target refinement (Fig. 1) . These results highlight the value of reverse genomic translation in first identifying human-relevant genetic risk factors for disease, and using model systems to understand the pathways impacted by their introduction to select rationally-informed modalities for potential treatment. These observations provide vital information about cellular mechanisms impacted by human disease-associated genetic risk factors without requiring the expense and time investment of creating, validating, and studying animal models. abstract: Translational genomics represents a broad field of study that combines genome and transcriptome-wide studies in humans and model systems to refine our understanding of human biology and ultimately identify new ways to treat and prevent disease. The approaches to translational genomics can be broadly grouped into two methodologies, forward and reverse genomic translation. Traditional (forward) genomic translation begins with model systems and aims at using unbiased genetic associations in these models to derive insight into biological mechanisms that may also be relevant in human disease. Reverse genomic translation begins with observations made through human genomic studies and refines these observations through follow-up studies using model systems. The ultimate goal of these approaches is to clarify intervenable processes as targets for therapeutic development. In this review, we describe some of the approaches being taken to apply translational genomics to the study of diseases commonly encountered in the neurocritical care setting, including hemorrhagic and ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and status epilepticus, utilizing both forward and reverse genomic translational techniques. Further, we highlight approaches in the field that could be applied in neurocritical care to improve our ability to identify new treatment modalities as well as to provide important information to patients about risk and prognosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13311-020-00838-1) contains supplementary material, which is available to authorized users. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7223188/ doi: 10.1007/s13311-020-00838-1 id: cord-261599-ddgoxape author: Nabi, Khondoker Nazmoon title: Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives date: 2020-09-21 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this work, a new compartmental mathematical model of COVID-19 pandemic has been proposed incorporating imperfect quarantine and disrespectful behavior of the citizens towards lockdown policies, which are evident in most of the developing countries. An integer derivative model has been proposed initially and then the formula for calculating basic reproductive number [Formula: see text] of the model has been presented. Cameroon has been considered as a representative for the developing countries and the epidemic threshold [Formula: see text] has been estimated to be ∼ 3.41 [Formula: see text] as of July 9, 2020. Using real data compiled by the Cameroonian government, model calibration has been performed through an optimization algorithm based on renowned trust-region-reflective (TRR) algorithm. Based on our projection results, the probable peak date is estimated to be on August 1, 2020 with approximately 1073 [Formula: see text] daily confirmed cases. The tally of cumulative infected cases could reach ∼ 20, 100 [Formula: see text] cases by the end of August 2020. Later, global sensitivity analysis has been applied to quantify the most dominating model mechanisms that significantly affect the progression dynamics of COVID-19. Importantly, Caputo derivative concept has been performed to formulate a fractional model to gain a deeper insight into the probable peak dates and sizes in Cameroon. By showing the existence and uniqueness of solutions, a numerical scheme has been constructed using the Adams-Bashforth-Moulton method. Numerical simulations enlightened the fact that if the fractional order α is close to unity, then the solutions will converge to the integer model solutions, and the decrease of the fractional-order parameter (0 < α < 1) leads to the delaying of the epidemic peaks. url: https://api.elsevier.com/content/article/pii/S0960077920306792 doi: 10.1016/j.chaos.2020.110283 id: cord-034843-cirltmy4 author: Nabipour, M. title: Deep Learning for Stock Market Prediction date: 2020-07-30 words: 8847.0 sentences: 451.0 pages: flesch: 54.0 cache: ./cache/cord-034843-cirltmy4.txt txt: ./txt/cord-034843-cirltmy4.txt summary: Employing the whole of tree-based methods, RNN, and LSTM techniques for regression problems and comparing their performance in Tehran stock exchange is a recent research activity presented in this study. Six tree-based models namely Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost, and also three neural networks-based algorithms (ANN, RNN, and LSTM) are employed in the prediction of the four stock market groups. This study employed tree-based models (Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost) and neural networks (ANN, RNN, and LSTM) to correctly forecast the values of four stock market groups (Diversified Financials, Petroleum, Non-metallic minerals, and Basic metals) as a regression problem. This study employed tree-based models (Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost) and neural networks (ANN, RNN, and LSTM) to correctly forecast the values of four stock market groups (Diversified Financials, Petroleum, Non-metallic minerals, and Basic metals) as a regression problem. abstract: The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517440/ doi: 10.3390/e22080840 id: cord-346921-3hfxv6h8 author: Nave, OPhir title: Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition date: 2020-09-21 words: 3197.0 sentences: 179.0 pages: flesch: 58.0 cache: ./cache/cord-346921-3hfxv6h8.txt txt: ./txt/cord-346921-3hfxv6h8.txt summary: In this study, we apply the singular perturbed vector field (SPVF) method to the COVID-19 mathematical model of to expose the hierarchy of the model. This decomposition enables us to rewrite the model in new coordinates in the form of fast and slow subsystems and, hence, to investigate only the fast subsystem with different asymptotic methods. We found the stable equilibrium points of the mathematical model and compared the results of the model with those reported by the Chinese authorities and found a fit of approximately 96 percent. After we transformed and presented the model in the new coordinates using the eigenvectors of the SPVF method, the model can be decomposed into the fast and slow subsystems based on the gap of the eigenvalues. As we have shown in the previous section, we obtain the stable equilibrium points of the mathematical model owing to the application of the SPVF method. abstract: In general, a mathematical model that contains many linear/nonlinear differential equations, describing a phenomenon, does not have an explicit hierarchy of system variables. That is, the identification of the fast variables and the slow variables of the system is not explicitly clear. The decomposition of a system into fast and slow subsystems is usually based on intuitive ideas and knowledge of the mathematical model being investigated. In this study, we apply the singular perturbed vector field (SPVF) method to the COVID-19 mathematical model of to expose the hierarchy of the model. This decomposition enables us to rewrite the model in new coordinates in the form of fast and slow subsystems and, hence, to investigate only the fast subsystem with different asymptotic methods. In addition, this decomposition enables us to investigate the stability analysis of the model, which is important in case of COVID-19. We found the stable equilibrium points of the mathematical model and compared the results of the model with those reported by the Chinese authorities and found a fit of approximately 96 percent. url: https://doi.org/10.7717/peerj.10019 doi: 10.7717/peerj.10019 id: cord-025283-kf65lxp5 author: Nayyeri, Mojtaba title: Embedding-Based Recommendations on Scholarly Knowledge Graphs date: 2020-05-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allow to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains. One of the applications of KGE models is to provide link predictions, which can also be viewed as a foundation for recommendation service, e.g. high confidence “co-author” links in a scholarly knowledge graph can be seen as suggested collaborations. In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. The results show a significant improvement in the accuracy of the experimented KGE models on the considered scholarly KGs using this specific loss. TransE with Soft Margin (TransE-SM) obtains a score of 79.5% Hits@10 for co-authorship link prediction task while the original TransE obtains 77.2%, on the same task. In terms of accuracy and Hits@10, TransE-SM also outperforms other state-of-the-art embedding models such as ComplEx, ConvE and RotatE in this setting. The predicted co-authorship links have been validated by evaluating profile of scholars. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250594/ doi: 10.1007/978-3-030-49461-2_15 id: cord-029311-9769dgb6 author: Nemati, Hamed title: Validation of Abstract Side-Channel Models for Computer Architectures date: 2020-06-13 words: 8427.0 sentences: 464.0 pages: flesch: 55.0 cache: ./cache/cord-029311-9769dgb6.txt txt: ./txt/cord-029311-9769dgb6.txt summary: While there are information flow analyses that try to counter these threats [3, 15] , these approaches use models that abstract from many features of modern processors, like caches and pipelining, and their effects on channels that can be accessed by an attacker, like execution time and power consumption. In step three we use symbolic execution to syn-thesize the weakest relation on program states that guarantees indistinguishability in the observational model (Sect. Through this relation, the observational model is used to drive the generation of test cases -pairs of states that satisfy the relation and can be used as inputs to the program (Sect. The following observational model attempts to overapproximate information flows for data-caches by relying on the fact that accessing two different addresses that only differ in their cache offset produces the same cache effects: Notice that by making the program counter observable, this model assumes that the attacker can infer the sequence of instructions executed by the program. abstract: Observational models make tractable the analysis of information flow properties by providing an abstraction of side channels. We introduce a methodology and a tool, Scam-V, to validate observational models for modern computer architectures. We combine symbolic execution, relational analysis, and different program generation techniques to generate experiments and validate the models. An experiment consists of a randomly generated program together with two inputs that are observationally equivalent according to the model under the test. Validation is done by checking indistinguishability of the two inputs on real hardware by executing the program and analyzing the side channel. We have evaluated our framework by validating models that abstract the data-cache side channel of a Raspberry Pi 3 board with a processor implementing the ARMv8-A architecture. Our results show that Scam-V can identify bugs in the implementation of the models and generate test programs which invalidate the models due to hidden microarchitectural behavior. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363228/ doi: 10.1007/978-3-030-53288-8_12 id: cord-340354-j3xsp2po author: Noll, N. B. title: COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2 date: 2020-05-07 words: 4322.0 sentences: 252.0 pages: flesch: 50.0 cache: ./cache/cord-340354-j3xsp2po.txt txt: ./txt/cord-340354-j3xsp2po.txt summary: Thus, to make such modeling widely available, we have developed an interactive, online tool that allows users to efficiently explore COVID-19 scenarios based upon different epidemiological assumptions and potential mitigation strategies. All source code and the aggregated surveillance data are made freely available through GitHub. We approximate the dynamics of a COVID-19 outbreak using a generalized SEIR model in which the population is partitioned into age-stratified compartments of: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), ICU overflow (O), dead (D) and recovered (R) individuals (Kermack et al., 1927) . The parameters of the model fall into three broad categories: a time-dependent infection rate β a (t); the rate of transition out of the exposed, infectious, hospitalized, and critical/overflow compartments γ e , γ i , γ h , and γ c respectively; and the age-specific fractions m a , c a and f a of mild, critical, and fatal infections respectively. abstract: The ongoing SARS-CoV-2 pandemic has caused large outbreaks around the world and every heavily affected community has experienced a substantial strain on the health care system and a high death toll. Communities therefore have to monitor the incidence of COVID-19 carefully and attempt to project the demand for health care. To enable such projections, we have developed an interactive web application that simulates an age-structured SEIR model with separate compartments for severely and critically ill patients. The tool allows the users to modify most parameters of the model, including age specific assumptions on severity. Infection control and mitigation measures that reduce transmission can be specified, as well as age-group specific isolation. The simulation of the model runs entirely on the client side in the browser; all parameter settings and results of the simulation can be exported for further downstream analysis. The tool is available at covid19-scenarios.org and the source code at github.com/neherlab/covid19_scenarios. url: http://medrxiv.org/cgi/content/short/2020.05.05.20091363v1?rss=1 doi: 10.1101/2020.05.05.20091363 id: cord-328181-b2o05j3j author: Nunez-Corrales, S. title: The Epidemiology Workbench: a Tool for Communities to Strategize in Response to COVID-19 and other Infectious Diseases date: 2020-07-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: COVID-19 poses a dramatic challenge to health, community life, and the economy of communities across the world. While the properties of the virus are similar from place to place, the impact has been dramatically different from place to place, due to such factors as population density, mobility, age distribution, etc. Thus, optimum testing and social distancing strategies may also be different from place to place. The Epidemiology Workbench provides access to an agent-based model in which demographic, geographic, and public health information a community together with a social distancing and testing strategy may be input, and a range of possible outcomes computed, to inform local authorities on coping strategies. The model is adaptable to other infectious diseases, and to other strains of coronavirus. The tool is illustrated by scenarios for the cities of Urbana and Champaign, Illinois, the home of the University of Illinois at Urbana-Champaign. Our calculations suggest that massive testing is the most effective strategy to combat the likely increase in local cases due to mass ingress of a student population carrying a higher viral load than that currently present in the community. url: https://doi.org/10.1101/2020.07.22.20159798 doi: 10.1101/2020.07.22.20159798 id: cord-219817-dqmztvo4 author: Oghaz, Toktam A. title: Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model date: 2020-04-14 words: 5198.0 sentences: 289.0 pages: flesch: 45.0 cache: ./cache/cord-219817-dqmztvo4.txt txt: ./txt/cord-219817-dqmztvo4.txt summary: Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. The shortage of labeled data for text analysis has encouraged researchers to develop novel unsupervised algorithms that consider co-occurrence of words in documents as well as emerging new techniques such as exploiting an additional source of information similar to Wikipedia knowledge-based topic models [37, 38] . We believe that what differentiates a narrative model 2 from topic analysis and summarization approaches is the ability to extract relevant sequences of text relative to the corresponding series of events associated with the same topic over time. Finally, we demonstrate that our proposed model discovers time localized topics over events that approximates the distribution of user activities on social media platforms. Our focus in the present work is on probabilistic topic modeling and extractive text summarization to provide descriptive narratives for the underlying events that occurred over a period of time. abstract: Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data. url: https://arxiv.org/pdf/2004.06793v1.pdf doi: nan id: cord-195082-7tnwkxuh author: Oodally, Ajmal title: Modeling dependent survival data through random effects with spatial correlation at the subject level date: 2020-10-12 words: 4801.0 sentences: 342.0 pages: flesch: 61.0 cache: ./cache/cord-195082-7tnwkxuh.txt txt: ./txt/cord-195082-7tnwkxuh.txt summary: Estimates are obtained through a stochastic approximation version of the Expectation Maximization algorithm combined with a Monte-Carlo Markov Chain, for which convergence is proven. Li and Ryan (2002) developed a semi-parametric spatial frailty model with Monte Carlo simulations and Laplace approximation of a rank based marginal likelihood. Along the same lines, Lin (2012) estimated parameters of a log-normal spatial frailty model using a two-iteration approach based on an approximate likelihood function, alternating between the estimation of the regression parameter and the variance components. For instance, we use as initial values for the regression parameter β and baseline components the estimated values obtained when fitting the data by a piecewise constant proportional hazards model. Furthermore, using the villages as clusters in the marginal and shared frailty models to analyse the malaria data set has serious impact on some of the parameter estimates. Convergent stochastic algorithm for parameter estimation in frailty models using integrated partial likelihood abstract: Dynamical phenomena such as infectious diseases are often investigated by following up subjects longitudinally, thus generating time to event data. The spatial aspect of such data is also of primordial importance, as many infectious diseases are transmitted from one subject to another. In this paper, a spatially correlated frailty model is introduced that accommodates for the correlation between subjects based on the distance between them. Estimates are obtained through a stochastic approximation version of the Expectation Maximization algorithm combined with a Monte-Carlo Markov Chain, for which convergence is proven. The novelty of this model is that spatial correlation is introduced for survival data at the subject level, each subject having its own frailty. This univariate spatially correlated frailty model is used to analyze spatially dependent malaria data, and its results are compared with other standard models. url: https://arxiv.org/pdf/2010.05504v1.pdf doi: nan id: cord-028789-dqa74cus author: Ouhami, Maryam title: Deep Transfer Learning Models for Tomato Disease Detection date: 2020-06-05 words: 2695.0 sentences: 145.0 pages: flesch: 55.0 cache: ./cache/cord-028789-dqa74cus.txt txt: ./txt/cord-028789-dqa74cus.txt summary: The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. In [6] , the study is based on a database of 120 images of infected rice leaves divided into three classes bacterial leaf blight, brown spot, and leaf smut (40 images for each class), Authors have converted the RGB images to an HSV color space to identify lesions, with a segmentation accuracy up to 96.71% using k-means. In plant disease detection field, many researchers have chosen deep models DensNets and VGGs for their high performance in standard computer vision tasks. In this paper we have studied three deep learning models in order to deal with the problem of plant disease detection. From the study that has been conducted it is possible to conclude that DensNet has a suitable architecture for the task of plants disease detection based on crop images. Using deep learning for image-based plant disease detection abstract: Vegetable crops in Morocco and especially in the Sous-Massa region are exposed to parasitic diseases and pest attacks which affect the quantity and the quality of agricultural production. Precision farming is introduced as one of the biggest revolutions in agriculture, which is committed to improving crop protection by identifying, analyzing and managing variability delivering effective treatment in the right place, at the right time, and with the right rate. The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. To deal with this problem we consider the deep learning models DensNet, 161 and 121 layers and VGG16 with transfer learning. Our study is based on images of infected plant leaves divided into 6 types of infections pest attacks and plant diseases. The results were promising with an accuracy up to 95.65% for DensNet161, 94.93% for DensNet121 and 90.58% for VGG16. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340929/ doi: 10.1007/978-3-030-51935-3_7 id: cord-320953-1st77mvh author: Overton, ChristopherE. title: Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example date: 2020-07-04 words: 15721.0 sentences: 734.0 pages: flesch: 48.0 cache: ./cache/cord-320953-1st77mvh.txt txt: ./txt/cord-320953-1st77mvh.txt summary: These include interpreting symptom progression and fatality ratios with delay distributions and right-censoring, exacerbated by exponential growth in cases leading to the majority of case data being on recently infected individuals; lack of clarity and consistency in denominators; inconsistency of case definitions over time and the eventual impact of interventions and changes to behaviour on transmission dynamics. We then develop a household-based contact tracing model, with which we investigate the extinction probability under weaker isolation policies paired with contact tracing, thus shedding light on possible combinations of interventions that allow us to feasibly manage the infection while minimising the social impact of control policies. Applying household isolation at 65% adherence ( 0.65 W α = ) manages to reduce the spread of infection, but appears insufficient in this model and with baseline parameters for controlling the outbreak in the long-term, unless other intervention strategies that reduce the global transmission (increasing ε) are adopted at the same time. abstract: During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic. url: https://doi.org/10.1016/j.idm.2020.06.008 doi: 10.1016/j.idm.2020.06.008 id: cord-022219-y7vsc6r7 author: PEIFFER, ROBERT L. title: Animals in Ophthalmic Research: Concepts and Methodologies date: 2013-11-17 words: 24854.0 sentences: 1191.0 pages: flesch: 46.0 cache: ./cache/cord-022219-y7vsc6r7.txt txt: ./txt/cord-022219-y7vsc6r7.txt summary: While the majority of investigations have had as their objective ultimate correlation with normal and abnormal function and structure of the human eye, laboratory studies have provided an abundance of comparative information that emphasizes that while there are numerous and amazing similarities in the peripheral visual system among the vertebrate (and even the invertebrate) animals, significant differences exist that are important to both researcher and clinician in selection of a research model and in extrapolation of data obtained from one species to another, and even among different species subdivisions. The use of laboratory animals in the investigation of infectious ocular disease has included rats, hamsters, guinea pigs, rabbits, cats, dogs, and subhuman primates. Ames and Hastings (1956) described a technique for rapid removal of the rabbit retina, together with a stump of optic nerve, for use in short-term culture experi ments including in vitro studies of retinal response to light (Ames and Gurian, 1960) . abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7155455/ doi: 10.1016/b978-0-12-278006-6.50008-2 id: cord-310863-jxbw8wl2 author: PRASAD, J. title: A data first approach to modelling Covid-19 date: 2020-05-26 words: 7177.0 sentences: 403.0 pages: flesch: 64.0 cache: ./cache/cord-310863-jxbw8wl2.txt txt: ./txt/cord-310863-jxbw8wl2.txt summary: We use the procedure to fit a set of SIR and SIRD models, with time dependent contact rate, to Covid-19 data for a set of 45 most affected countries. We find that SIR and SIRD models with constant transmission coefficients cannot fit Covid-19 data for most countries (mainly because social distancing, lockdown etc., make those time dependent). Some of the most important problems related to Covid-19 research are (1) estimating the controlling parameters of the pandemic, (2) making short term predictions using mathematical-statistical modeling which can help in mitigating policies (3) simulating the growth of the epidemic by taking into account as many contributing effects as possible and (4) quantifying the impact of mitigation measures, such as lockdown etc [ea20j] . One of the main reasons to consider these models has been that the Covid-19 data is available only for the Susceptible, Infected, Recovered and Dead compartments (for the notations used here and other places in the present work see table (1)). abstract: The primary data for Covid-19 pandemic is in the form of time series for the number of confirmed, recovered and dead cases. This data is updated every day and is available for most countries from multiple sources. In this work we present a two step procedure for model fitting to Covid-19 data. In the first step, time dependent transmission coefficients are constructed directly from the data and, in the second step, measures of those (minimum, maximum, mean, median etc.,) are used to set priors for fitting models to data. We call this approach a "data driven approach" or "data first approach". This scheme is complementary to Bayesian approach and can be used with or without that for parameter estimation. We use the procedure to fit a set of SIR and SIRD models, with time dependent contact rate, to Covid-19 data for a set of 45 most affected countries. We find that SIR and SIRD models with constant transmission coefficients cannot fit Covid-19 data for most countries (mainly because social distancing, lockdown etc., make those time dependent). We find that any time dependent contact rate, which falls gradually with time, can help to fit SIR and SIRD models for most of the countries. We also present constraints on transmission coefficients and basic reproduction number R0~ as well as effective reproduction number R(t). The main contributions of our work are as follows. (1) presenting a two step procedure for model fitting to Covid-19 data (2) constraining transmission coefficients as well as R0~ and R(t), for a set of most affected countries and (3) releasing a python package PyCov19 that can used to fit a set of compartmental models with time varying coefficients to Covid-19 data. url: https://doi.org/10.1101/2020.05.22.20110171 doi: 10.1101/2020.05.22.20110171 id: cord-027336-yk3cs8up author: Paciorek, Mateusz title: Scalable Signal-Based Simulation of Autonomous Beings in Complex Environments date: 2020-05-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Simulation of groups of autonomous beings poses a great computational challenge in terms of required time and resources. The need to simulate large environments, numerous populations of beings, and to increase the detail of models causes the need for parallelization of computations. The signal-based simulation algorithm, presented in our previous research, prove the possibility of linear scalability of such computations up to thousands of computing cores. In this paper further extensions of the signal-based models are investigated and new method for defining complex environments is presented. It allows efficient and scalable simulation of structures which cannot be defined using two dimensions, like multi-story buildings, anthills or bee hives. The solution is applied for defining a building evacuation model, which is validated using empirical data from a real-life evacuation drill. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304031/ doi: 10.1007/978-3-030-50420-5_11 id: cord-259534-hpyf0uj6 author: Panda, Sumati Kumari title: Applying fixed point methods and fractional operators in the modelling of novel coronavirus 2019-nCoV/SARS-CoV-2 date: 2020-10-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This study aims to discuss the prevalence of COVID-19 in U.S, Italy, Spain , France and China, where the virus spreads most rapidly and causes tragic outcomes. Thereafter, we present new insights of existence and uniqueness solutions of the 2019-nCoV models via fractional and fractal-fractional operators by using fixed point methods. url: https://api.elsevier.com/content/article/pii/S2211379720318994 doi: 10.1016/j.rinp.2020.103433 id: cord-123800-pxhott2p author: Pandey, Gaurav title: SEIR and Regression Model based COVID-19 outbreak predictions in India date: 2020-04-01 words: 3163.0 sentences: 178.0 pages: flesch: 60.0 cache: ./cache/cord-123800-pxhott2p.txt txt: ./txt/cord-123800-pxhott2p.txt summary: title: SEIR and Regression Model based COVID-19 outbreak predictions in India In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. For analysis and prediction of number of COVID-19 patients in India, the following models have been used. Recovered person was not sick again during the calculation period Now, considering 70% of India''s population to be approximately 966 million in susceptible class (S) and assuming only 1 person got infected in the initial part with average incubation period of 5.2, average infectious period of 2.9 and R 0 equal to 4, the SEIR model without intervention is shown in Figure 3 with the assumptions mentioned above. In this study, two machine learning models SEIR and Regression were used to analyse and predict the change in spread of COVID-19 disease. abstract: COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals. url: https://arxiv.org/pdf/2004.00958v1.pdf doi: nan id: cord-020871-1v6dcmt3 author: Papariello, Luca title: On the Replicability of Combining Word Embeddings and Retrieval Models date: 2020-03-24 words: 2135.0 sentences: 146.0 pages: flesch: 55.0 cache: ./cache/cord-020871-1v6dcmt3.txt txt: ./txt/cord-020871-1v6dcmt3.txt summary: We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval. The last 5 years have seen proof that neural network-based word embedding models provide term representations that are a useful information source for a variety of tasks in natural language processing. They are grouped in three sets: classification, clustering, and information retrieval, and compare "standard" embedding methods with the novel moVMF representation. First, text processing (e.g. tokenisation); second, creating a fixed-length vector representation for every document; finally, the third phase is determined by the goal to be achieved, i.e. classification, clustering, and retrieval. We replicated previously reported experiments that presented evidence that a new mixture model, based on von Mises-Fisher distributions, outperformed a series of other models in three tasks (classification, clustering, and retrievalwhen combined with standard retrieval models). abstract: We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval. Specifically, the hypothesis was that the use of a mixture model of von Mises-Fisher (VMF) distributions instead of Gaussian distributions would be beneficial because of the focus on cosine distances of both VMF and the vector space model traditionally used in information retrieval. Previous experiments had validated this hypothesis. Our replication was not able to validate it, despite a large parameter scan space. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148082/ doi: 10.1007/978-3-030-45442-5_7 id: cord-009481-6pm3rpzj author: Parnell, Gregory S. title: Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model date: 2009-12-11 words: 6493.0 sentences: 378.0 pages: flesch: 50.0 cache: ./cache/cord-009481-6pm3rpzj.txt txt: ./txt/cord-009481-6pm3rpzj.txt summary: In the second section, we describe a canonical model for resource allocation decision making for an intelligent adversary problem using an illustrative bioterrorism example with notional data. (16) In our example, we will use four of the recommendations: model the decisions of intelligent adversaries, include risk management, simplify the model by not assigning probabilities to the branches of uncertain events, and do not normalize the risk. (29) In our defenderattacker-defender decision analysis model, we have the two defender decisions (buy vaccine, add a Bio Watch city), the agent acquisition for the attacker is uncertain, the agent selection and target of attack is another decision, the consequences (fatalities and economic) are uncertain, the defender decision after attack to mitigate the maximum possible casualties, and the costs of defender decisions are known. We use multiple objective decision analysis with an additive value (risk) model to assign risk to the defender consequences. abstract: The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on risk analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic risk analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard risk analysis with intelligent adversary risk analysis, describes the intelligent adversary risk analysis challenges, and presents a probabilistic defender–attacker–defender model to evaluate the baseline risk and the potential risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The risk management model is demonstrated with an illustrative bioterrorism problem with notional data. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7159100/ doi: 10.1111/j.1539-6924.2009.01319.x id: cord-322577-5bboc1z0 author: Parola, Anna title: Mental Health Through the COVID-19 Quarantine: A Growth Curve Analysis on Italian Young Adults date: 2020-10-02 words: 6597.0 sentences: 319.0 pages: flesch: 47.0 cache: ./cache/cord-322577-5bboc1z0.txt txt: ./txt/cord-322577-5bboc1z0.txt summary: Despite several recent psychological researches on the coronavirus disease 2019 (COVID-19) pandemic highlighting that young adults represent a high risk category, no studies specifically focused on young adults'' mental health status have been carried out yet. This study aimed to assess and monitor Italian young adults'' mental health status during the first 4 weeks of lockdown through the use of a longitudinal panel design. The Syndromic Scales of Adult Self-Report 18-59 were used to assess the internalizing problems (anxiety/depression, withdrawn, and somatic complaints), externalizing problems (aggressive, rule-breaking, and intrusive behavior), and personal strengths. CONCLUSIONS: The results contributed to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to plan and develop efficient intervention projects able to take care of young adults'' mental health in the long term. This study assessed and monitored Italian young adults'' mental health status during the firsts 4 weeks of lockdown imposed by the government during the COVID-19 outbreak, from March 16 to April 16. abstract: INTRODUCTION: Health emergencies, such as epidemics, have detrimental and long-lasting consequences on people’s mental health, which are higher during the implementation of strict lockdown measures. Despite several recent psychological researches on the coronavirus disease 2019 (COVID-19) pandemic highlighting that young adults represent a high risk category, no studies specifically focused on young adults’ mental health status have been carried out yet. This study aimed to assess and monitor Italian young adults’ mental health status during the first 4 weeks of lockdown through the use of a longitudinal panel design. METHODS: Participants (n = 97) provided self-reports in four time intervals (1-week intervals) in 1 month. The Syndromic Scales of Adult Self-Report 18-59 were used to assess the internalizing problems (anxiety/depression, withdrawn, and somatic complaints), externalizing problems (aggressive, rule-breaking, and intrusive behavior), and personal strengths. To determine the time-varying effects of prolonged quarantine, a growth curve modeling will be performed. RESULTS: The results showed an increase in anxiety/depression, withdrawal, somatic complaints, aggressive behavior, rule-breaking behavior, and internalizing and externalizing problems and a decrease in intrusive behavior and personal strengths from T1 to T4. CONCLUSIONS: The results contributed to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to plan and develop efficient intervention projects able to take care of young adults’ mental health in the long term. url: https://doi.org/10.3389/fpsyg.2020.567484 doi: 10.3389/fpsyg.2020.567484 id: cord-320914-zf54jfol author: Parrish, Rebecca title: A Critical Analysis of the Drivers of Human Migration Patterns in the Presence of Climate Change: A New Conceptual Model date: 2020-08-19 words: 9935.0 sentences: 510.0 pages: flesch: 42.0 cache: ./cache/cord-320914-zf54jfol.txt txt: ./txt/cord-320914-zf54jfol.txt summary: Finally, we apply this model to a case study of Malawi to demonstrate how doing so can improve understanding of the local context and result in well-grounded and policy-relevant insights into the true impacts of climate change on migration. By conducting an in-depth literature review of Malawi''s political, demographic, environmental, social and economic makeup and then applying the conceptual approach described above by considering the impacts of climate change (primary, secondary and tertiary) to each key factor, we arrive at the case-specific model shown in Figure 2 below. By conducting an in-depth literature review of Malawi''s political, demographic, environmental, social and economic makeup and then applying the conceptual approach described above by considering the impacts of climate change (primary, secondary and tertiary) to each key factor, we arrive at the case-specific model shown in Figure 2 below. abstract: Both climate change and migration present key concerns for global health progress. Despite this, a transparent method for identifying and understanding the relationship between climate change, migration and other contextual factors remains a knowledge gap. Existing conceptual models are useful in understanding the complexities of climate migration, but provide varying degrees of applicability to quantitative studies, resulting in non-homogenous transferability of knowledge in this important area. This paper attempts to provide a critical review of climate migration literature, as well as presenting a new conceptual model for the identification of the drivers of migration in the context of climate change. It focuses on the interactions and the dynamics of drivers over time, space and society. Through systematic, pan-disciplinary and homogenous application of theory to different geographical contexts, we aim to improve understanding of the impacts of climate change on migration. A brief case study of Malawi is provided to demonstrate how this global conceptual model can be applied into local contextual scenarios. In doing so, we hope to provide insights that help in the more homogenous applications of conceptual frameworks for this area and more generally. url: https://www.ncbi.nlm.nih.gov/pubmed/32825094/ doi: 10.3390/ijerph17176036 id: cord-300570-xes201g7 author: Patwardhan, J. title: PREDICTIONS FOR EUROPE FOR THE COVID-19 PANDEMICAFTER LOCKDOWN WAS LIFTED USING AN SIR MODEL date: 2020-10-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: I analyze a simplified SIR model developed from a paper written by Gyan Bhanot and Charles de Lisi in May of 2020 to find the successes and limitations of their predictions. In particular, I study the predicted cases and deaths fitted to data from March and its potential application to data in September. The data is observed to fit the model as predicted until around 150 days after December 31, 2019, after which many countries lift their lockdowns and begin to reopen. A plateau in cases followed by an increase approximately 1.5 months after is also observed. In terms of deaths, the data fits the shape of the model, but the model mostly underestimates the death toll after around 160 days. An analysis of the residuals is provided to locate the precise date of the departure of each country from its accepted data estimates and test each data point to its predicted value using a Z-test to determine whether each observation can fit the given model. The observed behavior is matched to policy measures taken in each country to attach an explanation to these observations. I notice that an international reopening results in a sharp increase in cases, and aim to plot this new growth in cases and predict when the pandemic will end for each country. url: http://medrxiv.org/cgi/content/short/2020.10.03.20206359v1?rss=1 doi: 10.1101/2020.10.03.20206359 id: cord-288303-88c6qsek author: Paul, S. K. title: On nonlinear incidence rate of Covid-19 date: 2020-10-21 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Classical Susceptible-Infected-Removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread. On top of that transmission rate may vary widely in a large region due to non-stationarity of spatial features which poses difficulty in creating a global model. We modified discrete global Susceptible-Infected-Removed model by using time varying transmission rate, recovery rate and multiple spatially local models. No specific functional form of transmission rate has been assumed. We have derived the criteria for disease-free equilibrium within a specific time period. A single Convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a 10-day prediction period. Local interpretations of the model using perturbation method reveals local influence of different features on transmission rate which in turn is used to generate a set of generalized global interpretations. A what-if scenario with modified recovery rate illustrates rapid dampening of the spread when forecasted with the trained model. A comparative study with current normal scenario reveals key necessary steps to reach baseline. url: https://doi.org/10.1101/2020.10.19.20215665 doi: 10.1101/2020.10.19.20215665 id: cord-021426-zo9dx8mr author: Peiffer, Robert L. title: Models in Ophthalmology and Vision Research date: 2013-10-21 words: 14366.0 sentences: 750.0 pages: flesch: 44.0 cache: ./cache/cord-021426-zo9dx8mr.txt txt: ./txt/cord-021426-zo9dx8mr.txt summary: This chapter reviews the anatomy and physiology of the rabbit eye from a comparative perspective, summarizes documented spontaneous ocular conditions, discusses experimentally induced disease in general terms, and concludes with a summary of ob servations regarding the rabbit as a model for broad categories of research. This chapter reviews the anatomy and physiology of the rabbit eye from a comparative perspective, summarizes documented spontaneous ocular conditions, discusses experimentally induced disease in general terms, and concludes with a summary of observations regarding the rabbit as a model for broad categories of research. The choroidal thickness varies, being thickest posteriorly and thinning toward the ora ciliaris retinae; it tends to be thicker inferiorly compared to superiorly and is thickest and most heavily pigmented in the region of the visual streak, an area that lies well above the posterior pole of the globe on either side of and below the optic disk. Because the rabbit has a merangiotic retina, it is a less than ideal choice for an experimental model to study retinal vascular diseases of humans. abstract: This chapter reviews the anatomy and physiology of the rabbit eye from a comparative perspective. The anatomy of the rabbit eye reflects its niche as a diurnal herbivore. The rabbit has both photopic and scotopic vision without the benefit of a tapetum. Orbits are laterally situated; the rabbit is one of the few animals in which the orbital axis coincides with the visual axis. The shape of the orbit is circular, compared to the cone shaped human orbit. The orbital walls are of bone, except inferiorly, where the wall is formed partially by the muscles of mastication. The superior orbital wall is formed by the frontal bone. The supraorbital process of the frontal bone contains three supraorbital foramina, which are unique feature of the rabbit orbit; the foramina are incisures formed into apertures by a cartilaginous sheet. The optic foramina share a common canal anteriorly with only a thin boney plate to divide them, which disappears posteriorly to form one canal opening into the cranium as a single foramen. Entropion in young rabbits can occur as a primary or as a secondary condition arising from infection. Viral-induced eyelid proliferations can result from infection by the rabbit myxoma and papilloma viruses. The papilloma virus is a part of the papovaviridae and is a DNA virus transmitted by arthropod vectors. The cottontail rabbit in the mid-western United States is most frequently affected, although domestic rabbits are susceptible. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7149682/ doi: 10.1016/b978-0-12-469235-0.50025-7 id: cord-011400-zyjd9rmp author: Peixoto, Tiago P. title: Network Reconstruction and Community Detection from Dynamics date: 2019-09-18 words: 3327.0 sentences: 191.0 pages: flesch: 50.0 cache: ./cache/cord-011400-zyjd9rmp.txt txt: ./txt/cord-011400-zyjd9rmp.txt summary: Researchers have approached this reconstruction task from a variety of angles, resulting in many different methods, including thresholding the correlation between time series [6] , inversion of deterministic dynamics [7] [8] [9] , statistical inference of graphical models [10] [11] [12] [13] [14] and of models of epidemic spreading [15] [16] [17] [18] [19] [20] , as well as approaches that avoid explicit modeling, such as those based on transfer entropy [21] , Granger causality [22] , compressed sensing [23] [24] [25] , generalized linearization [26] , and matching of pairwise correlations [27, 28] . [32] proposed a method to infer community structure from time-series data that bypasses network reconstruction by employing a direct modeling of the dynamics given the group assignments, instead. We take two empirical networks, the with E ¼ 39430 edges, and a food web from Little Rock Lake [46] , containing N ¼ 183 nodes and E ¼ 2434 edges, and we sample from the SIS (mimicking the spread of a pandemic) and Ising model (representing simplified interspecies interactions) on them, respectively, and evaluate the reconstruction obtained via the joint and separate inference with community detection, with results shown in Fig. 2 . abstract: We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226905/ doi: 10.1103/physrevlett.123.128301 id: cord-354254-89vjfkfd author: Peng, Shanbi title: The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control date: 2020-08-31 words: 7520.0 sentences: 420.0 pages: flesch: 48.0 cache: ./cache/cord-354254-89vjfkfd.txt txt: ./txt/cord-354254-89vjfkfd.txt summary: Inspired by the impact of COVID-19, this review summarizes research works of pathogen transmission based on CFD methods with different models and algorithms. Defining the pathogen as the particle or gaseous in CFD simulation is a common method and epidemic models are used in some investigations to rise the authenticity of calculation. The Re-Normalization Group (RNG) k-ε was used in simulation in order to solve the turbulence with the good performance of accuracy, efficiency and robustness; In Gao and Niu [45] study, RNG k-ε model including the effect of low-Reynolds-number is used to solve the airflow and the diffusion of tracer gas which can represent the contaminant transmission are calculated by the equation below: Gao, et.al [102] combined the use of experiment and CFD method to study airborne transmission in different flats of a high-rise building and to verify their simulation, the data of tracer gas experiment from Denmark Aalborg University [103] is used. abstract: Transmission mechanics of infectious pathogen in various environments are of great complexity and has always been attracting many researchers' attention. As a cost-effective and powerful method, Computational Fluid Dynamics (CFD) plays an important role in numerically solving environmental fluid mechanics. Besides, with the development of computer science, an increasing number of researchers start to analyze pathogen transmission by using CFD methods. Inspired by the impact of COVID-19, this review summarizes research works of pathogen transmission based on CFD methods with different models and algorithms. Defining the pathogen as the particle or gaseous in CFD simulation is a common method and epidemic models are used in some investigations to rise the authenticity of calculation. Although it is not so difficult to describe the physical characteristics of pathogens, how to describe the biological characteristics of it is still a big challenge in the CFD simulation. A series of investigations which analyzed pathogen transmission in different environments (hospital, teaching building, etc) demonstrated the effect of airflow on pathogen transmission and emphasized the importance of reasonable ventilation. Finally, this review presented three advanced methods: LBM method, Porous Media method, and Web-based forecasting method. Although CFD methods mentioned in this review may not alleviate the current pandemic situation, it helps researchers realize the transmission mechanisms of pathogens like viruses and bacteria and provides guidelines for reducing infection risk in epidemic or pandemic situations. url: https://www.ncbi.nlm.nih.gov/pubmed/33027870/ doi: 10.1016/j.scitotenv.2020.142090 id: cord-018746-s9knxdne author: Perra, Nicola title: Modeling and Predicting Human Infectious Diseases date: 2015-04-23 words: 9708.0 sentences: 543.0 pages: flesch: 53.0 cache: ./cache/cord-018746-s9knxdne.txt txt: ./txt/cord-018746-s9knxdne.txt summary: Building on these concepts we present two realistic data-driven epidemiological models able to forecast the spreading of infectious diseases at different geographical granularities. The unprecedented amount of data on human dynamics made available by recent advances technology has allowed the development of realistic epidemic models able to capture and predict the unfolding of infectious disease at different geographical scales [59] . The new approach allows for the early detection of disease outbreaks [62] , the real time monitoring of the evolution of a disease with an incredible geographical granularity [63] [64] [65] , the access to health related behaviors, practices and sentiments at large scales [66, 67] , inform data-driven epidemic models [68, 69] , and development of statistical based models with prediction power [67, [70] [71] [72] [73] [74] [75] [76] [77] [78] . abstract: The spreading of infectious diseases has dramatically shaped our history and society. The quest to understand and prevent their spreading dates more than two centuries. Over the years, advances in Medicine, Biology, Mathematics, Physics, Network Science, Computer Science, and Technology in general contributed to the development of modern epidemiology. In this chapter, we present a summary of different mathematical and computational approaches aimed at describing, modeling, and forecasting the diffusion of viruses. We start from the basic concepts and models in an unstructured population and gradually increase the realism by adding the effects of realistic contact structures within a population as well as the effects of human mobility coupling different subpopulations. Building on these concepts we present two realistic data-driven epidemiological models able to forecast the spreading of infectious diseases at different geographical granularities. We conclude by introducing some recent developments in diseases modeling rooted in the big-data revolution. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123706/ doi: 10.1007/978-3-319-14011-7_4 id: cord-280640-0h3yv2m4 author: Phillips, Christopher J. title: Screening For Colorectal Cancer in the Age of Simulation Models:A Historical Lens date: 2020-07-16 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: nan url: https://www.sciencedirect.com/science/article/pii/S0016508520349313?v=s5 doi: 10.1053/j.gastro.2020.07.010 id: cord-118553-ki6bbuod author: Piccolomini, Elena Loli title: Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model date: 2020-03-22 words: 1842.0 sentences: 107.0 pages: flesch: 55.0 cache: ./cache/cord-118553-ki6bbuod.txt txt: ./txt/cord-118553-ki6bbuod.txt summary: In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, using the data from the Italian Protezione Civile from February 24th 2020. Since several restricting measures have been imposed by the Italian government at different times, starting from March 8th 2020, we propose a modification of SEIRD by introducing a time dependent transmitting rate. The SIR model and its later modifications, such as Susceptible-Exposed-Infected-Removed (SEIR) [2] are commonly used by the epidemic medical community in the study of outbreaks diffusion.In these models, the population is divided into groups. Hopefully, these measures will affect the spread of the COVID-19 virus reducing the number of infected people and the value of the parameter R0. In this paper we propose a SEIRD model accounting for five different groups, Susceptible, Exposed, Infected, Recovery and Dead. abstract: In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, using the data from the Italian Protezione Civile from February 24th 2020. In this study investigate an adaptation of the model. Since several restricting measures have been imposed by the Italian government at different times, starting from March 8th 2020, we propose a modification of SEIRD by introducing a time dependent transmitting rate. In the numerical results we report the maximum infection spread for the three Italian regions firstly affected by the COVID-19 outbreak(Lombardia, Veneto and Emilia Romagna). This approach will be successively extended to other Italian regions, as soon as more data will be available. url: https://arxiv.org/pdf/2003.09909v1.pdf doi: nan id: cord-031143-a1qyadm6 author: Pinto Neto, Osmar title: Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19 date: 2020-08-30 words: 5288.0 sentences: 245.0 pages: flesch: 53.0 cache: ./cache/cord-031143-a1qyadm6.txt txt: ./txt/cord-031143-a1qyadm6.txt summary: RESULTS: The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. The red circles (deaths) and blue circles (active cases) indicate real data up to June 18 Table 3 Inverse of the model optimized coefficients of γ, δ, ζ, and ε representing latent, infectious, hospitalization, and critical cases mean duration in days, as well as the model estimated basic reproductive number (R 0 ) and the death rate (DR) for June 18, 2020, for Germany, Brazil, Spain, Italy, South Korea, Portugal, Switzerland, Thailand, and USA, respectively. abstract: INTRODUCTION: In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. Considering this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in each social, economic, and political scenario. METHODOLOGY: We present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. RESULTS: The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. (c) Using our model, large ranges for each input, and optimization we predict death values for 15, 30, 45, and 60 days ahead with errors in the order of 5, 10, 20, and 80%, respectively; (d) sudden changes in the number of active cases cannot be predicted by the model unless data from outside sources are used. CONCLUSION: The results suggest that the presented model may be used to predict 15 days ahead values of total deaths with errors in the order of 5%. These errors may be minimized if social distance data are inputted into the model. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456444/ doi: 10.1007/s42600-020-00084-6 id: cord-318900-dovu6kha author: Pitschel, T. title: SARS-Cov-2 proliferation: an analytical aggregate-level model date: 2020-08-22 words: 3841.0 sentences: 215.0 pages: flesch: 53.0 cache: ./cache/cord-318900-dovu6kha.txt txt: ./txt/cord-318900-dovu6kha.txt summary: An intuitive mathematical model describing the virus proliferation is presented and its parameters estimated from time series of observed reported CoViD-19 cases in Germany. Approximating the model evolution as continuous process even at small time intervals 1 Caution in the usage of numbers from pure incidence analysis is required: As consequence of the way the raw data is obtained in [HLWea20] , only infectiousness around the moment of symptom onset is in fact fully observed. Therefore, at the present state of this text, such estimation can only serve to determine reasonable bounds on the parameters of the model, rather than to give a reliable forecast of expect number of eventual infections. In this study a novel model for virus proliferation dynamics was developed and with it the SARS-Cov-2 outbreak in Germany retraced on an aggregate level, using CoViD-19 case count data by the Robert-Koch Institute in Berlin. abstract: An intuitive mathematical model describing the virus proliferation is presented and its parameters estimated from time series of observed reported CoViD-19 cases in Germany. The model replicates the main essential characteristics of the proliferation in a stylized form, and thus can support the systematic reasoning about interventional measures (or their lifting) that were discussed during summer and which currently become relevant again in some countries. The model differs in form from elementary SIR models, but is contained in the general Kermack-McKendrick (1927) model. It is maintained that (compared to elementary SIR models) the model is more faithfully representing real proliferation at the instantaneous level, leading to overall more plausible association of model parameters to physical transmission and recovery parameters. The main policy- oriented results are that (1) mitigation measures imposed in March 2020 in Germany were absolutely necessary to avoid health care resource exhaustion, (2) fast response is key to containment in case of renewed outbreaks. A model generalization aiming to better represent the true infectiousness profile is stated. url: https://doi.org/10.1101/2020.08.20.20178301 doi: 10.1101/2020.08.20.20178301 id: cord-331376-l0o1weus author: Pogwizd, Steven M. title: Rabbit models of heart disease date: 2009-03-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Human heart disease is a major cause of death and disability. A variety of animal models of cardiac disease have been developed to better understand the etiology, cellular and molecular mechanisms of cardiac dysfunction and novel therapeutic strategies. The animal models have included large animals (e.g. pig and dog) and small rodents (e.g. mouse and rat) and the advantages of genetic manipulation in mice have appropriately encouraged the development of novel mouse models of cardiac disease. However, there are major differences between rodent and human hearts that raise cautions about the extrapolation of results from mouse to human. The rabbit is a medium-sized animal that has many cellular and molecular characteristics very much like human, and is a practical alternative to larger mammals. Numerous rabbit models of cardiac disease are discussed, including pressure or volume overload, ischemia, rapid-pacing, doxorubicin, drug-induced arrhythmias, transgenesis and infection. These models also lead to the assessment of therapeutic strategies which may become beneficial in human cardiac disease. url: https://api.elsevier.com/content/article/pii/S1740675709000115 doi: 10.1016/j.ddmod.2009.02.001 id: cord-154170-7pnz98o6 author: Ponciano, Jos''e Miguel title: Poverty levels, societal and individual heterogeneities explain the SARS-CoV-2 pandemic growth in Latin America date: 2020-05-22 words: 3253.0 sentences: 175.0 pages: flesch: 46.0 cache: ./cache/cord-154170-7pnz98o6.txt txt: ./txt/cord-154170-7pnz98o6.txt summary: Latin America is experiencing severe impacts of the SARS-CoV-2 pandemic, but poverty and weak public health institutions hamper gathering the kind of refined data needed to inform classical SEIR models of epidemics. Here we show that a multi-model, multi-stages modeling approach helps elucidate i) early epidemic growth in fourteen Latin-American countries ii) the role of poverty in shaping the growth rate of the number of cases and iii) the probability that the number of cases of SARS-CoV-2 exceeds any given amount within arbitrarily defined small windows of time, starting from the present. We draw on prior work in conservation biology, population dynamics and epidemiological theory to complement the current suite of deterministic epidemiological models, characterize the role of urban poverty in shaping the region''s SARS-CoV-2 epidemics, and develop a methodology to generate short (5-15 days), sequentially updatable, process-based forecasts. abstract: Latin America is experiencing severe impacts of the SARS-CoV-2 pandemic, but poverty and weak public health institutions hamper gathering the kind of refined data needed to inform classical SEIR models of epidemics. We present an alternative approach that draws on advances in statistical ecology and conservation biology to enhance the value of sparse data in projecting and ameliorating epidemics. Our approach, leading to what we call a Stochastic Epidemic Gompertz model, with few parameters can flexibly incorporate heterogeneity in transmission within populations and across time. We demonstrate that poverty has a large impact on the course of the pandemic, across fourteen Latin American countries, and show how our approach provides flexible, time-varying projections of disease risk that can be used to refine public health strategies. url: https://arxiv.org/pdf/2005.11201v1.pdf doi: nan id: cord-168862-3tj63eve author: Porter, Mason A. title: Nonlinearity + Networks: A 2020 Vision date: 2019-11-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include"higher-order"interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators. url: https://arxiv.org/pdf/1911.03805v1.pdf doi: nan id: cord-347952-k95wrory author: Prieto, Diana M title: A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels date: 2012-03-30 words: 9202.0 sentences: 433.0 pages: flesch: 38.0 cache: ./cache/cord-347952-k95wrory.txt txt: ./txt/cord-347952-k95wrory.txt summary: Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Conclusions: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. Of the existing computer simulation models addressing PHP, those focused on disease spread and mitigation of pandemic influenza (PI) have been recognized by the public health officials as useful decision support tools for preparedness planning [1] . abstract: BACKGROUND: In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use. METHODS: We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers. RESULTS: While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values. CONCLUSIONS: To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility. url: https://doi.org/10.1186/1471-2458-12-251 doi: 10.1186/1471-2458-12-251 id: cord-007255-jmjolo9p author: Pulliam, Juliet R. C. title: Ability to replicate in the cytoplasm predicts zoonotic transmission of livestock viruses date: 2009-02-15 words: 2458.0 sentences: 117.0 pages: flesch: 42.0 cache: ./cache/cord-007255-jmjolo9p.txt txt: ./txt/cord-007255-jmjolo9p.txt summary: The database contains information on the 3 molecular characteristics hypothesized to influence the potential of a virus to cross host species: site of replication (X SR ; whether replication is completed in the cytoplasm or requires nuclear entry), genomic material (X GM ; RNA or DNA), and segmentation of the viral genome (X Seg ; segmented or nonsegmented). Hypothesis testing allowed us to determine how likely it was that the observed patterns were due to chance, whereas model-based prediction allowed us to determine what trait or set of traits was the best predictor of a livestock virus''s ability to infect humans and to estimate the probability that a particular virus species would be able to jump host species, given knowledge of the traits of interest. To examine the magnitude and relative importance of the effects that the 3 molecular characteristics of interest have on the ability of the viral species in the database to infect humans, we developed a set of logistic regression models. abstract: Understanding viral factors that promote cross-species transmission is important for evaluating the risk of zoonotic emergence. Weconstructed a database of viruses of domestic artiodactyls and examined the correlation between traits linked in the literature to cross-species transmission and the ability of viruses to infect humans. Among these traits-genomic material, genome segmentation, and replication without nuclear entry-the last is the strongest predictor of cross-species transmission. This finding highlights nuclear entry as a barrier to transmission and suggests that the ability to complete replication in the cytoplasm may prove to be a useful indicator of the threat of cross-species transmission. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7110041/ doi: 10.1086/596510 id: cord-017595-v3rllyyu author: Puzyn, Tomasz title: Nanomaterials – the Next Great Challenge for Qsar Modelers date: 2009-06-25 words: 9662.0 sentences: 570.0 pages: flesch: 46.0 cache: ./cache/cord-017595-v3rllyyu.txt txt: ./txt/cord-017595-v3rllyyu.txt summary: However, from the physico-chemical viewpoint, the novel properties of nanoparticles can also be determined by their chemical composition, surface structure, solubility, shape, ratio of particles in relation to agglomerates, and surface area to volume ratio. Analyzing the literature data (Section 14.3) it must be concluded that even if a class of structurally similar nanoparticles is tested with the same laboratory protocol, the number of tested compounds is often insufficient to perform comprehensive internal and external validation of a model and to calculate the appropriate measures of robustness and predictivity in QSAR. [81] have developed two models defining the relationships between basic physico-chemical properties (namely, water solubility, log S, and n-octanol/water partition coefficient, log P) of carbon nanotubes and their chiral vectors (as structural descriptors). Although we strongly believe in the usefulness and appropriateness of QSAR methodology for nanomaterial studies, the number of available models related to activity and toxicity is still very limited. abstract: In this final chapter a new perspective for the application of QSAR in the nanosciences is discussed. The role of nanomaterials is rapidly increasing in many aspects of everyday life. This is promoting a wide range of research needs related to both the design of new materials with required properties and performing a comprehensive risk assessment of the manufactured nanoparticles. The development of nanoscience also opens new areas for QSAR modelers. We have begun this contribution with a detailed discussion on the remarkable physical–chemical properties of nanomaterials and their specific toxicities. Both these factors should be considered as potential endpoints for further nano-QSAR studies. Then, we have highlighted the status and research needs in the area of molecular descriptors applicable to nanomaterials. Finally, we have put together currently available nano-QSAR models related to the physico-chemical endpoints of nanoparticles and their activity. Although we have observed many problems (i.e., a lack of experimental data, insufficient and inadequate descriptors), we do believe that application of QSAR methodology will significantly support nanoscience in the near future. Development of reliable nano-QSARs can be considered as the next challenging task for the QSAR community. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122189/ doi: 10.1007/978-1-4020-9783-6_14 id: cord-269873-4hxwo5kt author: R., Mohammadi title: Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images date: 2020-10-01 words: 3378.0 sentences: 199.0 pages: flesch: 52.0 cache: ./cache/cord-269873-4hxwo5kt.txt txt: ./txt/cord-269873-4hxwo5kt.txt summary: OBJECTIVE: This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. MATERIAL AND METHODS: In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. To this end, the present study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection and diagnosis of COVID-19 infection in chest X-rays. In this study, a CNN-based model was used to detect COVID-19 from the chest X-ray images. In this study, we proposed four pre-trained deep CNN models, including VGG-16, VGG-19, MobileNet, and InceptionResNetV2 for discriminating COVID-19 cases from chest X-ray images. In this study, we presented four pre-trained deep CNN models such as VGG16, VGG19, MobileNet, and InceptionResNetV2 are used for transfer learning to detect and classify COVID-19 from chest radiography. abstract: BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required. OBJECTIVE: This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. MATERIAL AND METHODS: In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. RESULTS: Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. CONCLUSION: The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further. url: https://www.ncbi.nlm.nih.gov/pubmed/33134214/ doi: 10.31661/jbpe.v0i0.2008-1153 id: cord-277128-g90hp8j7 author: Rajendran, Sukumar title: Accessing Covid19 Epidemic Outbreak in Tamilnadu and the Impact of Lockdown through Epidemiological Models and Dynamic systems date: 2020-09-17 words: 2783.0 sentences: 175.0 pages: flesch: 53.0 cache: ./cache/cord-277128-g90hp8j7.txt txt: ./txt/cord-277128-g90hp8j7.txt summary: To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R(0)) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. The model utilizes population dynamics and conditional dependencies such as new cases, deaths, social distancing, and herd immunity over a stipulated timeperiod to simulate probable outcomes. The factors governing the spread are infectious agents, modes of transmission, susceptibility, and immunity (Chowell et al., 2006 The case fatality rate(CFR) is highly variable and increases with severe respiratory symptoms in adults with comorbid conditions . The mapping of transmission of covid19 is done through contact tracing, thereby isolating individuals infected by the epidemic at different epicentres of the society denoted by . abstract: Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the “effective reproductive number” (R(0)) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R(0) value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345=1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions. url: https://www.ncbi.nlm.nih.gov/pubmed/32958973/ doi: 10.1016/j.measurement.2020.108432 id: cord-026336-xdymj4dk author: Ranjan, Rajesh title: Temporal Dynamics of COVID-19 Outbreak and Future Projections: A Data-Driven Approach date: 2020-06-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Long-term predictions for an ongoing epidemic are typically performed using epidemiological models that predict the timing of the peak in infections followed by its decay using non-linear fits from the available data. The curves predicted by these methods typically follow a Gaussian distribution with a decay rate of infections similar to the climbing rate before the peak. However, as seen from the recent COVID-19 data from the US and European countries, the decay in the number of infections is much slower than their increase before the peak. Therefore, the estimates of the final epidemic size from these models are often underpredicted. In this work, we propose two data-driven models to improve the forecasts of the epidemic during its decay. These two models use Gaussian and piecewise-linear fits of the infection rate respectively during the deceleration phase, if available, to project the future course of the pandemic. For countries, which are not yet in the decline phase, these models use the peak predicted by epidemiological models but correct the infection rate to incorporate a realistic slow decline based on the trends from the recent data. Finally, a comparative study of predictions using both epidemiological and data-driven models is presented for a few most affected countries. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275845/ doi: 10.1007/s41403-020-00112-y id: cord-018791-h3bfdr14 author: Rasulev, Bakhtiyor title: Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures date: 2016-12-09 words: 10404.0 sentences: 526.0 pages: flesch: 45.0 cache: ./cache/cord-018791-h3bfdr14.txt txt: ./txt/cord-018791-h3bfdr14.txt summary: In short, QSAR is a method to find correlations and mathematical models for congeneric series of compounds, affinities of ligands to their binding sites, rate constants, inhibition constants, toxicological effect, and many other biological activities, based on structural features, as well as group and molecular properties, such as electronic properties, polarizability, or steric properties (Klebe et al. Later authors improved this approach and by combining the two existing techniques, GRID and PLS, has developed a powerful 3D QSAR methodology, so-called comparative molecular field analysis (CoMFA) (Cramer et al. The main advantage of this combined approach of 3D QSAR and pharmacophore-based docking is to focus on specific key interaction for protein-ligand binding to improve drug candidates. Another group published in 2013 a study that conducted a comprehensive investigation of fullerene analogues by combined computational approach including quantum chemical, molecular docking, and 3D descriptors-based QSAR (Ahmed et al. abstract: The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123761/ doi: 10.1007/978-3-319-27282-5_54 id: cord-289542-u86ujtur author: Razavian, Narges title: A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients date: 2020-10-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows. url: https://doi.org/10.1038/s41746-020-00343-x doi: 10.1038/s41746-020-00343-x id: cord-321715-bkfkmtld author: Redelings, Benjamin D title: Incorporating indel information into phylogeny estimation for rapidly emerging pathogens date: 2007-03-14 words: 9793.0 sentences: 546.0 pages: flesch: 54.0 cache: ./cache/cord-321715-bkfkmtld.txt txt: ./txt/cord-321715-bkfkmtld.txt summary: To see if indel information improves phylogenetic resolution we compare the number of bi-partitions that are supported under the joint model and the traditional sequential approach, in which topology reconstruction assumes a previously determined alignment. These parameters include a multiple alignment A that specifies the positional homology between the sequences Y, an evolutionary tree (τ, T) where τ is an unrooted bifurcating tree topology and T = (t 1 , ..., t 2N -3 ) is a vector of branch lengths along the edges in τ, and vectors Θ and Λ are parameters that characterize the letter substitution and indel processes respectively. We therefore propose a new pairwise alignment prior that maintains a fixed sequence length distribution φ even when the indel probability varies from branch to branch. Since the joint model balances substitution and indel information as well as taking alignment ambiguity into account we assume that these differences represent an improvement in the accuracy of estimation. abstract: BACKGROUND: Phylogenies of rapidly evolving pathogens can be difficult to resolve because of the small number of substitutions that accumulate in the short times since divergence. To improve resolution of such phylogenies we propose using insertion and deletion (indel) information in addition to substitution information. We accomplish this through joint estimation of alignment and phylogeny in a Bayesian framework, drawing inference using Markov chain Monte Carlo. Joint estimation of alignment and phylogeny sidesteps biases that stem from conditioning on a single alignment by taking into account the ensemble of near-optimal alignments. RESULTS: We introduce a novel Markov chain transition kernel that improves computational efficiency by proposing non-local topology rearrangements and by block sampling alignment and topology parameters. In addition, we extend our previous indel model to increase biological realism by placing indels preferentially on longer branches. We demonstrate the ability of indel information to increase phylogenetic resolution in examples drawn from within-host viral sequence samples. We also demonstrate the importance of taking alignment uncertainty into account when using such information. Finally, we show that codon-based substitution models can significantly affect alignment quality and phylogenetic inference by unrealistically forcing indels to begin and end between codons. CONCLUSION: These results indicate that indel information can improve phylogenetic resolution of recently diverged pathogens and that alignment uncertainty should be considered in such analyses. url: https://www.ncbi.nlm.nih.gov/pubmed/17359539/ doi: 10.1186/1471-2148-7-40 id: cord-346309-hveuq2x9 author: Reis, Ben Y title: An Epidemiological Network Model for Disease Outbreak Detection date: 2007-06-26 words: 8419.0 sentences: 382.0 pages: flesch: 46.0 cache: ./cache/cord-346309-hveuq2x9.txt txt: ./txt/cord-346309-hveuq2x9.txt summary: CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events. In order to both improve overall detection performance and reduce vulnerability to baseline shifts, we introduce a general class of epidemiological network models that explicitly capture the relationships among epidemiological data streams. In order to evaluate the practical utility of this approach for surveillance, we constructed epidemiological network models based on real-world historical health-care data and compared their outbreak-detection performance to that of standard historical models. In this study, the researchers developed a new class of surveillance systems called ''''epidemiological network models.'''' These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams). abstract: BACKGROUND: Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most. METHODS AND FINDINGS: To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach. CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events. url: https://www.ncbi.nlm.nih.gov/pubmed/17593895/ doi: 10.1371/journal.pmed.0040210 id: cord-281122-dtgmn9e0 author: Ribeiro, Matheus Henrique Dal Molin title: Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods date: 2020-09-22 words: 4956.0 sentences: 268.0 pages: flesch: 72.0 cache: ./cache/cord-281122-dtgmn9e0.txt txt: ./txt/cord-281122-dtgmn9e0.txt summary: Hence, there is a lack of discussion concerning the predictive 130 capacity of machine learning-based approaches for diseases such as measles, meningitis, 131 and chikungunya on the forecast task; 132 • In the modeling aspect, only four papers focused on ensemble approaches such as bag-133 ging and boosting or models combined by average. Also, σ 2 e and σ 2 θ are the variance of errors, weights and biases, respectively; Considering that QRF uses the quantiles in the prediction process, the α-quantile of CDF 299 is stated as the probability that the number of notifications is lower than Q α if the given p t 300 is equal to α, where the estimate of α is stated as follows: The PLS regression approach is a technique to analyze multivariate data, in which the 306 aim is to relate one or two output variables (Y) with several inputs (X). abstract: Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE) and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, sMAPE criteria and Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modelling developed in this paper is promising and can be used by managers to support decision making. url: https://www.sciencedirect.com/science/article/pii/S1532046420302033?v=s5 doi: 10.1016/j.jbi.2020.103575 id: cord-281543-ivhr2no3 author: Richardson, Eugene T title: Pandemicity, COVID-19 and the limits of public health ‘science’ date: 2020-04-17 words: 1881.0 sentences: 132.0 pages: flesch: 56.0 cache: ./cache/cord-281543-ivhr2no3.txt txt: ./txt/cord-281543-ivhr2no3.txt summary: 17 In the case of Ebola outbreak in West Africa, epidemiologists attributed amplified transmission to local populations'' beliefs in misinformation or their ''strange'' funerary practices-in essence, diverting the public''s gaze from legacies of the transatlantic slave trade (or Maafa), 18 colonialism, 19 indirect rule, 20 structural adjustment 21 and extractive foreign companies as determinants. 40 As they start to sift back through the determinative web of human rights abuses-that is, the pathologies of power 41that set the stage for these health inequalities, they may begin to see that they contribute a great deal to the production and reproduction of structural injustice because of the social position they occupy and the violence that has been committed in their names. Mathematical modeling of the West Africa Ebola epidemic abstract: nan url: https://www.ncbi.nlm.nih.gov/pubmed/32469327/ doi: 10.1136/bmjgh-2020-002571 id: cord-159103-dbgs2ado author: Rieke, Nicola title: The Future of Digital Health with Federated Learning date: 2020-03-18 words: 6703.0 sentences: 326.0 pages: flesch: 46.0 cache: ./cache/cord-159103-dbgs2ado.txt txt: ./txt/cord-159103-dbgs2ado.txt summary: The medical FL use-case is inherently different from other domains, e.g. in terms of number of participants and data diversity, and while recent surveys investigate the research advances and open questions of FL [14, 11, 15] , we focus on what it actually means for digital health and what is needed to enable it. Transfer Learning, for example, is a well-established approach of model-sharing that makes it possible to tackle problems with deep neural networks that have millions of parameters, despite the lack of extensive, local datasets that are required for training from scratch: a model is first trained on a large dataset and then further optimised on the actual target data. To adopt this approach into a form of collaborative learning in a FL setup with continuous learning from different institutions, the participants can share their model with a peer-to-peer architecture in a "round-robin" or parallel fashion and train in turn on their local data. abstract: Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed. url: https://arxiv.org/pdf/2003.08119v1.pdf doi: nan id: cord-327784-xet20fcw author: Rieke, Nicola title: The future of digital health with federated learning date: 2020-09-14 words: 5658.0 sentences: 273.0 pages: flesch: 42.0 cache: ./cache/cord-327784-xet20fcw.txt txt: ./txt/cord-327784-xet20fcw.txt summary: We envision a federated future for digital health and with this perspective paper, we share our consensus view with the aim of providing context and detail for the community regarding the benefits and impact of FL for medical applications (section "Datadriven medicine requires federated efforts"), as well as highlighting key considerations and challenges of implementing FL for digital health (section "Technical considerations"). FL addresses this issue by enabling collaborative learning without centralising data (subsection "The promise of federated efforts") and has already found its way to digital health applications (subsection "Current FL efforts for digital health"). Current FL efforts for digital health Since FL is a general learning paradigm that removes the data pooling requirement for AI model development, the application range of FL spans the whole of AI for healthcare. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation abstract: Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed. url: https://www.ncbi.nlm.nih.gov/pubmed/33015372/ doi: 10.1038/s41746-020-00323-1 id: cord-280722-glcifqyp author: Rios, V. title: Is there a link between temperatures and COVID-19 contagions? Evidence from Italy date: 2020-05-19 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This study analyzes the link between temperatures and COVID-19 contagions in a sample of Italian regions during the period ranging from February 24 to April 15. To that end, Bayesian Model Averaging techniques are used to analyze the relevance of the temperatures together with a set of additional climate, environmental, demographic, social and policy factors. The robustness of individual covariates is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by the temperatures given that it appears as the most relevant determinant of contagions. This finding is robust to (i) the prior distribution elicitation, (ii) the procedure to assign weights to the regressors, (iii) the presence of measurement errors in official data due to under-reporting, (iv) the employment of different metrics of temperatures or (v) the inclusion of additional correlates. In a second step, relative importance metrics that perform an accurate partitioning of the R2 of the model are calculated. The results of this approach support the evidence of the model averaging analysis, given that temperature is the top driver explaining 45% of regional contagion disparities. The set of policy-related factors appear in a second level of importance, whereas factors related to the degree of social connectedness or the demographic characteristics are less relevant. url: https://doi.org/10.1101/2020.05.13.20101261 doi: 10.1101/2020.05.13.20101261 id: cord-269212-oeu48ili author: Rodrigues, Alírio E. title: Chemical engineering and environmental challenges. Cyclic adsorption/reaction technologies: Materials and process together! date: 2020-04-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: I start with a brief survey of paradigms in Chemical Engineering to highlight that in the early 70 s my thesis advisor P. Le Goff already mentioned the strong link of chemical processes with Environment, Energy and Economy (Market). Then I move to my vision of ChE today summarized in ChE = M(2)P(2)E (Molecular, Materials, Process and Product Engineering). I describe how I built a research lab centered around Cyclic Adsorption/Reaction Processes focusing in adsorption technologies to help solving environmental problems. I stress the basic concepts of adsorption processes and the need to use proper diffusion models for intraparticle mass transfer instead of pseudo first order or second order kinetic models. I also consider that adsorbent metrics should be linked to the process where the material is used: materials and processes together! In the last section I review some challenging areas where adsorption technologies are useful. Carbon Capture and Utilization involving Pressure Swing Adsorption to capture CO(2) from flue gas in a pilot plant, 3D printed composite monoliths for Electric Swing Adsorption, and Utilization of CO(2) to be transformed in methanol or Synthetic Natural Gas (SNG) (Power-to-Gas concept). I also address the general topic “Processing of diluted aqueous solutions” with special attention for the development of Simulated Moving Bed coupled with Expanded Bed Adsorption. Finally the integrated process to produce high-added valued compounds (vanillin and syringaldehyde) from Kraft lignin is shown as an example of Lignin valorization in pulp mill biorefinery. url: https://api.elsevier.com/content/article/pii/S2213343720302748 doi: 10.1016/j.jece.2020.103926 id: cord-225429-pz9lsaw6 author: Rodrigues, Helena Sofia title: Optimal Control and Numerical Optimization Applied to Epidemiological Models date: 2014-01-29 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The relationship between epidemiology, mathematical modeling and computational tools allows to build and test theories on the development and battling of a disease. This PhD thesis is motivated by the study of epidemiological models applied to infectious diseases in an Optimal Control perspective, giving particular relevance to Dengue. Dengue is a subtropical and tropical disease transmitted by mosquitoes, that affects about 100 million people per year and is considered by the World Health Organization a major concern for public health. The mathematical models developed and tested in this work, are based on ordinary differential equations that describe the dynamics underlying the disease, including the interaction between humans and mosquitoes. An analytical study is made related to equilibrium points, their stability and basic reproduction number. The spreading of Dengue can be attenuated through measures to control the transmission vector, such as the use of specific insecticides and educational campaigns. Since the development of a potential vaccine has been a recent global bet, models based on the simulation of a hypothetical vaccination process in a population are proposed. Based on Optimal Control theory, we have analyzed the optimal strategies for using these controls, and respective impact on the reduction/eradication of the disease during an outbreak in the population, considering a bioeconomic approach. The formulated problems are numerically solved using direct and indirect methods. The first discretize the problem turning it into a nonlinear optimization problem. Indirect methods use the Pontryagin Maximum Principle as a necessary condition to find the optimal curve for the respective control. In these two strategies several numerical software packages are used. url: https://arxiv.org/pdf/1401.7390v1.pdf doi: nan id: cord-261530-vmsq5hhz author: Rodriguez, Jorge title: A mechanistic population balance model to evaluate the impact of interventions on infectious disease outbreaks: Case for COVID19 date: 2020-04-07 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Infectious diseases, especially when new and highly contagious, could be devastating producing epidemic outbreaks and pandemics. Predicting the outcomes of such events in relation to possible interventions is crucial for societal and healthcare planning and forecasting of resource needs. Deterministic and mechanistic models can capture the main known phenomena of epidemics while also allowing for a meaningful interpretation of results. In this work a deterministic mechanistic population balance model was developed. The model describes individuals in a population by infection stage and age group. The population is treated as in a close well mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The present model provides a steppingstone to build upon and its current low complexity retains accessibility to non experts and policy makers to comprehend the variables and phenomena at play. The impact of specific interventions on the outbreak time course, number of cases and outcome of fatalities were evaluated including that of available critical care. Data available from the COVID19 outbreak as of early April 2020 was used. Key findings in our results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) an increase in the number of critical care beds could save up to eight lives per extra bed in a million population with the current parameters used; (iv) the use of protective equipment (PPE) appears effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) infection recognition through random testing of the population, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities but only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while R0 values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at R0 values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone; (viii) a threshold value, equivalent to that for R0, appears to exist for the daily fatality rate at which to end isolation measures, this is significant as the fatality rate is (unlike R0) very accurately known. Any interpretation of these results for the COVID19 outbreak predictions and interventions should be considered only qualitatively at this stage due to the low confidence (lack of complete and valid data) on the parameter values available at the time of writing. Any quantitative interpretation of the results must be accompanied with a critical discussion in terms of the model limitations and its frame of application. url: https://doi.org/10.1101/2020.04.04.20053017 doi: 10.1101/2020.04.04.20053017 id: cord-275395-w2u7fq1g author: Romero-Severson, Ethan Obie title: Change in global transmission rates of COVID-19 through May 6 2020 date: 2020-08-06 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We analyzed COVID-19 data through May 6th, 2020 using a partially observed Markov process. Our method uses a hybrid deterministic and stochastic formalism that allows for time variable transmission rates and detection probabilities. The model was fit using iterated particle filtering to case count and death count time series from 55 countries. We found evidence for a shrinking epidemic in 30 of the 55 examined countries. Of those 30 countries, 27 have significant evidence for subcritical transmission rates, although the decline in new cases is relatively slow compared to the initial growth rates. Generally, the transmission rates in Europe were lower than in the Americas and Asia. This suggests that global scale social distancing efforts to slow the spread of COVID-19 are effective although they need to be strengthened in many regions and maintained in others to avoid further resurgence of COVID-19. The slow decline also suggests alternative strategies to control the virus are needed before social distancing efforts are partially relaxed. url: https://doi.org/10.1371/journal.pone.0236776 doi: 10.1371/journal.pone.0236776 id: cord-333088-ygdau2px author: Roy, Manojit title: On representing network heterogeneities in the incidence rate of simple epidemic models date: 2006-03-31 words: 7645.0 sentences: 397.0 pages: flesch: 52.0 cache: ./cache/cord-333088-ygdau2px.txt txt: ./txt/cord-333088-ygdau2px.txt summary: We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We also demonstrate the existence of a complex dynamical behavior in the stochastic system within the narrow small-world region, consisting of persistent cycles with enhanced amplitude and a well-defined period that are not predicted by the equivalent homogeneous mean-field model. abstract: Abstract Mean-field ecological models ignore space and other forms of contact structure. At the opposite extreme, high-dimensional models that are both individual-based and stochastic incorporate the distributed nature of ecological interactions. In between, moment approximations have been proposed that represent the effect of correlations on the dynamics of mean quantities. As an alternative closer to the typical temporal models used in ecology, we present here results on “modified mean-field equations” for infectious disease dynamics, in which only mean quantities are followed and the effect of heterogeneous mixing is incorporated implicitly. We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We show that, for two distinct dynamical cases involving a stable equilibrium and a noisy endemic steady state, the modified mean-field model approximates successfully the steady state dynamics as well as the respective short and long transients of decaying cycles. This result demonstrates that early on in the transients an approximate power-law relationship is established between global (mean) quantities and the covariance structure in the network. The approach fails in the more complex case of persistent cycles observed within the narrow range of small-world configurations. url: https://api.elsevier.com/content/article/pii/S1476945X05000814 doi: 10.1016/j.ecocom.2005.09.001 id: cord-264408-vk4lt83x author: Ruiz, Sara I. title: Animal Models of Human Viral Diseases date: 2017-06-23 words: 34464.0 sentences: 1865.0 pages: flesch: 47.0 cache: ./cache/cord-264408-vk4lt83x.txt txt: ./txt/cord-264408-vk4lt83x.txt summary: Well-developed animal models are necessary to understand disease progression, pathogenesis, and immunologic responses to viral infections in humans. NHPs including marmosets, cotton-top tamarins, and rhesus macaques infected with Norwalk virus are monitored for the extent of viral shedding; however, no clinical disease is observed in these models. Intracerebral and IN routes of infection resulted in a fatal disease that was highly dependent on dose while intradermal (ID) and subQ inoculations caused only 50% fatality in mice regardless of the amount of virus (liu et al., 1970) . Ferrets infected with Hendra or Nipah virus display the same clinical disease as seen in the hamster model and human cases (Bossart et al., 2009; Pallister et al., 2011) . Characterization studies with IFNAr −/− mice challenged with different routes (IP, IN, IM, and subQ) showed that CCHFV causes acute disease with high viral loads, pathology in liver and lymphoid tissues, increased proinflammatory response, severe thrombocytopenia, coagulopathy, and death, all of which are characteristics of human disease . abstract: As the threat of exposure to emerging and reemerging viruses within a naïve population increases, it is vital that the basic mechanisms of pathogenesis and immune response be thoroughly investigated. Recent outbreaks of Middle East respiratory syndrome corona virus, Ebola virus, Chikungunya virus, and Zika virus illustrate the emerging threats that are encountered. By utilizing animal models in this endeavor, the host response to viruses can be studied in a more complex and integrated context to identify novel drug targets, and assess the efficacy and safety of new products rapidly. This is especially true in the advent and implementation of the FDA animal rule. Although no one animal model is able to recapitulate all aspects of human disease, understanding the current limitations allows for a more targeted experimental design. Important facets to consider prior to an animal study are route of viral exposure, species of animal, biomarkers of disease, and a humane endpoint. This chapter covers the current animal models for medically important human viruses, and demonstrates where the gaps in knowledge exist. url: https://www.sciencedirect.com/science/article/pii/B9780128094686000334 doi: 10.1016/b978-0-12-809468-6.00033-4 id: cord-319933-yp9ofhi8 author: Ruiz, Sara I. title: Chapter 38 Animal Models of Human Viral Diseases date: 2013-12-31 words: 28834.0 sentences: 1797.0 pages: flesch: 46.0 cache: ./cache/cord-319933-yp9ofhi8.txt txt: ./txt/cord-319933-yp9ofhi8.txt summary: An experimental study with cell culture-adapted hepatitis Avirus in guinea pigs challenged by oral or intraperitoneal routes did not result in clinical disease, increase in liver enzymes, or seroconversion. 32 NHPs including marmosets, cotton-top tamarins, and rhesus macaques infected with Norwalk virus can be monitored for the extent of viral shedding; however, no clinical disease is observed in these models. 66, 67 Intracerebral and intranasal routes of infection resulted in a fatal disease that was highly dependent on dose, while intradermal and subcutaneous inoculations caused only 50% fatality in mice regardless of the amount of virus. A mouse-adapted (MA) strain of Dengue virus 2 introduced into AG129 mice developed vascular leak syndrome similar to the severe disease seen in humans. [138] [139] [140] [141] [142] [143] [144] Inoculation of WNV into NHPs intracerebrally resulted in the development of either encephalitis, febrile disease, or an asymptomatic infection, depending on the virus strain and dose. abstract: Abstract As the threat of exposure to emerging and reemerging viruses within a naive population increases, it is vital that the basic mechanisms of pathogenesis and immune response be thoroughly investigated. By using animal models in this endeavor, the response to viruses can be studied in a more natural context to identify novel drug targets, and assess the efficacy and safety of new products. This is especially true in the advent of the Food and Drug Administration's animal rule. Although no one animal model is able to recapitulate all the aspects of human disease, understanding the current limitations allows for a more targeted experimental design. Important facets to be considered before an animal study are the route of challenge, species of animals, biomarkers of disease, and a humane endpoint. This chapter covers the current animal models for medically important human viruses, and demonstrates where the gaps in knowledge exist. url: https://api.elsevier.com/content/article/pii/B9780124158948000385 doi: 10.1016/b978-0-12-415894-8.00038-5 id: cord-169288-aeyz2t6c author: Runvik, Haakan title: Initialization of a Disease Transmission Model date: 2020-07-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Approaches to the calculation of the full state vector of a larger epidemiological model for the spread of COVID-19 in Sweden at the initial time instant from available data and with a simplified dynamical model are proposed and evaluated. The larger epidemiological model is based on a continuous Markov chain and captures the demographic composition of and the transport flows between the counties of Sweden. Its intended use is to predict the outbreak development in temporal and spatial coordinates as well as across the demographic groups. It can also support evaluating and comparing of prospective intervention strategies in terms of e.g. lockdown in certain areas or isolation of specific age groups. The simplified model is a discrete time-invariant linear system that has cumulative infectious incidence, infected population, asymptomatic population, exposed population, and infectious pressure as the state variables. Since the system matrix of the model depends on a number transition rates, structural properties of the model are investigated for suitable parameter ranges. It is concluded that the model becomes unobservable for some parameter values. Two contrasting approaches to the initial state estimation are considered. One is a version of Rauch-Tung-Striebel smoother and another is based on solving a batch nonlinear optimization problem. The benefits and shortcomings of the considered estimation techniques are analyzed and compared on synthetic data for several Swedish counties. url: https://arxiv.org/pdf/2007.08925v1.pdf doi: nan id: cord-135004-68y19dpg author: Russo, Carlo title: Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction date: 2020-10-27 words: 3206.0 sentences: 161.0 pages: flesch: 48.0 cache: ./cache/cord-135004-68y19dpg.txt txt: ./txt/cord-135004-68y19dpg.txt summary: Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard. Furthermore, we extended the use of lesion features extracted from the latent space of the segmentation models using the LesionEncoder framework, which replaces the classic imaging / radiomic features, such as volumetric parameters, intensity, morphologic, histogram-based and textural features, which showed high predictive power in patient OS prediction. abstract: Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard. url: https://arxiv.org/pdf/2010.13967v1.pdf doi: nan id: cord-007726-bqlf72fe author: Rydell-Törmänen, Kristina title: The Applicability of Mouse Models to the Study of Human Disease date: 2018-11-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The laboratory mouse Mus musculus has long been used as a model organism to test hypotheses and treatments related to understanding the mechanisms of disease in humans; however, for these experiments to be relevant, it is important to know the complex ways in which mice are similar to humans and, crucially, the ways in which they differ. In this chapter, an in-depth analysis of these similarities and differences is provided to allow researchers to use mouse models of human disease and primary cells derived from these animal models under the most appropriate and meaningful conditions. Although there are considerable differences between mice and humans, particularly regarding genetics, physiology, and immunology, a more thorough understanding of these differences and their effects on the function of the whole organism will provide deeper insights into relevant disease mechanisms and potential drug targets for further clinical investigation. Using specific examples of mouse models of human lung disease, i.e., asthma, chronic obstructive pulmonary disease, and pulmonary fibrosis, this chapter explores the most salient features of mouse models of human disease and provides a full assessment of the advantages and limitations of these models, focusing on the relevance of disease induction and their ability to replicate critical features of human disease pathophysiology and response to treatment. The chapter concludes with a discussion on the future of using mice in medical research with regard to ethical and technological considerations. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121329/ doi: 10.1007/978-1-4939-9086-3_1 id: cord-143847-vtwn5mmd author: Ryffel, Th'eo title: ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing date: 2020-06-08 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We propose ARIANN, a low-interaction framework to perform private training and inference of standard deep neural networks on sensitive data. This framework implements semi-honest 2-party computation and leverages function secret sharing, a recent cryptographic protocol that only uses lightweight primitives to achieve an efficient online phase with a single message of the size of the inputs, for operations like comparison and multiplication which are building blocks of neural networks. Built on top of PyTorch, it offers a wide range of functions including ReLU, MaxPool and BatchNorm, and allows to use models like AlexNet or ResNet18. We report experimental results for inference and training over distant servers. Last, we propose an extension to support n-party private federated learning. url: https://arxiv.org/pdf/2006.04593v1.pdf doi: nan id: cord-352348-2wtyk3r5 author: Sabroe, Ian title: Identifying and hurdling obstacles to translational research date: 2007 words: 5307.0 sentences: 229.0 pages: flesch: 39.0 cache: ./cache/cord-352348-2wtyk3r5.txt txt: ./txt/cord-352348-2wtyk3r5.txt summary: The quality of our scientific output (perceived as a change in disease incidence and/or the development of a therapy) is largely dependent on the quality of the input data and the methods for their processing and interpretation, although the process of generating effective translational science is not as linear (that is, from molecules to models to humans) as is often thought. These revolve around our understanding of the nature of the translational process, the integration of the outputs of different technological approaches to disease, the use of models, access to tissues and appropriate materials, and the need for support in increasingly complex areas such as ethics and bioinformatics. Such debates might facilitate the comparison of data between laboratories and between species, and might highlight the components of specific diseases that are ripe for the development of new in vivo models and protocols (for example, there remains a great need to more effectively model the role of the innate immune system in acute and chronic asthma), broadening the number of disease processes or phenotypes that are modelled in pathology. abstract: Although there is overwhelming pressure from funding agencies and the general public for scientists to bridge basic and translational studies, the fact remains that there are significant hurdles to overcome in order to achieve this goal. The purpose of this Opinion article is to examine the nature of these hurdles and to provide food for thought on the main obstacles that impede this process. url: https://www.ncbi.nlm.nih.gov/pubmed/17186032/ doi: 10.1038/nri1999 id: cord-347199-slq70aou author: Safta, Cosmin title: Characterization of partially observed epidemics through Bayesian inference: application to COVID-19 date: 2020-10-07 words: 8406.0 sentences: 455.0 pages: flesch: 54.0 cache: ./cache/cord-347199-slq70aou.txt txt: ./txt/cord-347199-slq70aou.txt summary: The method is cast as one of Bayesian inference of the latent infection rate (number of people infected per day), conditioned on a time-series of Developing a forecasting method that is applicable in the early epoch of a partially-observed outbreak poses some peculiar difficulties. This infection rate curve is convolved with the Probability Density Function (PDF) of the incubation period of the disease to produce an expression for the time-series of newly symptomatic cases, an observable that is widely reported as "daily new cases" by various data sources [2, 5, 6] . 2, with postulated forms for the infection rate curve and the derivation of the prediction for daily new cases; we also discuss a filtering approach that is applied to the data before using it to infer model parameters. abstract: We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico. url: https://doi.org/10.1007/s00466-020-01897-z doi: 10.1007/s00466-020-01897-z id: cord-034181-ji4empe6 author: Saqib, Mohd title: Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model date: 2020-10-23 words: 4637.0 sentences: 277.0 pages: flesch: 55.0 cache: ./cache/cord-034181-ji4empe6.txt txt: ./txt/cord-034181-ji4empe6.txt summary: The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. Furthermore, one issue occurs when working with time-series data (as COVID-19 confirmed cases) is over-fitting particularly when estimating models with large numbers of parameters over relatively short periods and the solution to the over-fitting problem, is to take a Bayesian approach (using Ridge Regularization) which allows us to impose certain priors on depended variables [26] . In the Bayesian regression approach, we can take into account Other models are developed with good accuracy but as well as data become available, those entire algorithms will not able to survive without a few evaluations due to the dynamic nature of pandemic escalation of the COVID-19 but the proposed model corrects the distributions for model parameters and forecasting results using parameters distributions. abstract: In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) Coronavirus, unforeseen pandemic put humanity at big risk and health professionals are facing several kinds of problem due to rapid growth of confirmed cases. That is why some prediction methods are required to estimate the magnitude of infected cases and masses of studies on distinct methods of forecasting are represented so far. In this study, we proposed a hybrid machine learning model that is not only predicted with good accuracy but also takes care of uncertainty of predictions. The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. This is a completely mathematical model in which we have successfully incorporated with prior knowledge and posterior distribution enables us to incorporate more upcoming data without storing previous data. Also, L(2) (Ridge) Regularization is used to overcome the problem of overfitting. To justify our results, we have presented case studies of three countries, −the United States, Italy, and Spain. In each of the cases, we fitted the model and estimate the number of possible causes for the upcoming weeks. Our forecast in this study is based on the public datasets provided by John Hopkins University available until 11th May 2020. We are concluding with further evolution and scope of the proposed model. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581693/ doi: 10.1007/s10489-020-01942-7 id: cord-263571-6i64lee0 author: Sarkar, Rohan title: Inulin from Pachyrhizus erosus root and its production intensification using evolutionary algorithm approach and response surface methodology date: 2020-09-09 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Production of inulin from yam bean tubers by ultrasonic assisted extraction (UAE) was optimized by using response surface methodology (RSM) and genetic algorithms (GA). Yield of inulin was obtained between 11.97%–12.15% for UAE and 11.21%–11.38% for microwave assisted extraction (MAE) using both the methodologies, significantly higher than conventional method (9.9 %) using optimized conditions. Under such optimized condition, SEM image of root tissues before and extraction showed disruption and microfractures over surface. UAE provided a shade better purity of extracted inulin than other two techniques. Degree of polymerization in inulin was also recorded to be better, might be due lesser degradation during extraction. Significant prebiotic activity was recorded while evaluation using Lactobacillus fermentum and it was 36 % more than glucose treatment. Energy density by UAE was few fold lesser than MAE. Carbon emission was far more less in both these methods than the conventional one. url: https://doi.org/10.1016/j.carbpol.2020.117042 doi: 10.1016/j.carbpol.2020.117042 id: cord-297161-ziwfr9dv author: Sauter, T. title: TESTING INFORMED SIR BASED EPIDEMIOLOGICAL MODEL FOR COVID-19 IN LUXEMBOURG date: 2020-07-25 words: 2245.0 sentences: 94.0 pages: flesch: 51.0 cache: ./cache/cord-297161-ziwfr9dv.txt txt: ./txt/cord-297161-ziwfr9dv.txt summary: The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. Such models allow describing the dynamics of mutually exclusive states such as Susceptible (S) which for COVID-19 is assumed to be the entire population of a country, a region or city, the number of Infected (I) and Removed (R) that often combines (deaths and recovered), as well as the number of Exposed (E) for SEIR models. As the number of performed tests strongly influences the dynamic analysis of the COVID-19 pandemic in a country or region, we developed a novel SIR based epidemiological model (SIVRT, Figure 1 ) which allows the integration of this key information. In summary, the novel testing informed SIVRT model structure allows to describe and analyze the COVID-19 pandemic data of Luxembourg in dependency of the number of performed tests. abstract: The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT) which allows the country-specific integration of testing information and other available data. The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. As proof of concept, the novel SIVRT model was used to simulate the first phase of the pandemic in Luxembourg. An overall number of infections of 13.000 and an infection fatality rate of 1,3% was estimated, which is in concordance with data from population-wide testing. Furthermore based on the data as of end of May 2020 and assuming a partial deconfinement, an increase of cases is predicted from mid of July 2020 on. This is consistent with the current observed rise and shows the predictive potential of the novel SIVRT model. url: http://medrxiv.org/cgi/content/short/2020.07.21.20159046v1?rss=1 doi: 10.1101/2020.07.21.20159046 id: cord-268142-lmkfxme5 author: Schafrum Macedo, Aline title: Animal modeling in bone research—Should we follow the White Rabbit? date: 2019-09-26 words: 3706.0 sentences: 296.0 pages: flesch: 49.0 cache: ./cache/cord-268142-lmkfxme5.txt txt: ./txt/cord-268142-lmkfxme5.txt summary: title: Animal modeling in bone research—Should we follow the White Rabbit? Our aim here is to provide a broad overview of animal modeling and its ethical implications, followed by a narrower focus on bone research and the role rabbits are playing in the current scenario. 12 Five key bioethical points are considered when assessing the moral status of animal subjects in research: the presence of life, the ability to feel and perceive stimuli, the level of cognitive behavior, the degree of sociability, and the ability to proliferate. Animal models have taught us much about bone disorders and have been central to developing many treatments throughout history. 8, 17, 51 Rabbits are appealing models for bone research. Rabbits have potential as bone models but conclusive studies are still lacking. Animal models for implant biomaterial research in bone: a review The laboratory rabbit: an animal model of atherosclerosis research Osteoporosis-bone remodeling and animal models abstract: Animal models are live subjects applied to translational research. They provide insights into human diseases and enhance biomedical knowledge. Livestock production has favored the pace of human social development over millennia. Today's society is more aware of animal welfare than past generations. The general public has marked objections to animal research and many species are falling into disuse. The search for an ideal methodology to replace animal use is on, but animal modeling still holds great importance to human health. Bone research, in particular, has unmet requirements that in vitro technologies cannot yet fully address. In that sense, standardizing novel models remains necessary and rabbits are gaining in popularity as potential bone models. Our aim here is to provide a broad overview of animal modeling and its ethical implications, followed by a narrower focus on bone research and the role rabbits are playing in the current scenario. url: https://www.ncbi.nlm.nih.gov/pubmed/31773091/ doi: 10.1002/ame2.12083 id: cord-293333-mqoml9o5 author: Scharbarg, Emeric title: From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves date: 2020-10-03 words: 5785.0 sentences: 330.0 pages: flesch: 59.0 cache: ./cache/cord-293333-mqoml9o5.txt txt: ./txt/cord-293333-mqoml9o5.txt summary: The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. In particular apart the high transmission rate, other two aspects were immediately pointed out by the physicians which did strongly influence the diffusion of the disease and the medical resources: first it was estimated that a large delay of time (10 to 14 days) is present between the moment in which a person becomes infected and can infect, and the instant in which symptoms become evident and the person is isolated and sent to quarantine. The subsystem (2) consisting by I q , R and D q is then further discussed in Section 4: a group of people who are aware of their infection define the flow of admissions in a local hospital and are split into two populations, the patients admitted in conventional hospitalization and the patients admitted in intensive care. abstract: Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first macroscopic model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. Daily (low cost) data are easily collected at this level, and are worked out for a fast evaluation of the local health status thanks to control systems methods. Precisely, the identifiability of the parameters of the hospital model is proven and thanks to the availability of clinical data, essential characteristics of the local health status are identified. Those parameters are meaningful not only to alert on some increase of the infection, but also to assess the efficiency of the therapy and health policy. url: https://www.ncbi.nlm.nih.gov/pubmed/33041632/ doi: 10.1016/j.arcontrol.2020.09.007 id: cord-319378-li77za5e author: Schroeder, Wheaton L. title: Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis date: 2020-09-11 words: 15111.0 sentences: 818.0 pages: flesch: 57.0 cache: ./cache/cord-319378-li77za5e.txt txt: ./txt/cord-319378-li77za5e.txt summary: Even with the addition of exchange and transport reactions, the current Exophiala dermatitidis draft model has relatively few reactions which are capable of holding flux as determined by FVA, see ''''General steps on how to use iEde2091'''' and accompanying code for a description on how to apply FVA). The stoichiometries for the reactions selected by the first CPs solution (taken from the first database file) should be added to a copy of ll OPEN ACCESS STAR Protocols 1, 100105, September 18, 2020 the second draft Exophiala dermatitidis model in order to make the third draft E. As with step 3, the best solutions should be selected from the second application of OptFill, and the stoichiometries of the reactions in the optimal CPs solution should be added to a copy of the third draft Exophiala dermatitidis model to produce the fourth draft E. abstract: Exophiala dermatitidis is a polyextremotolerant fungus with a small genome, thus suitable as a model system for melanogenesis and carotenogensis. A genome-scale model, iEde2091, is reconstructed to increase metabolic understanding and used in a shadow price analysis of pigments, as detailed here. Important to this reconstruction is OptFill, a recently developed alternative gap-filling method useful in the holistic and conservative reconstruction of genome-scale models of metabolism, particularly for understudied organisms like E. dermatitidis where gaps in metabolic knowledge are abundant. For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020). url: https://doi.org/10.1016/j.xpro.2020.100105 doi: 10.1016/j.xpro.2020.100105 id: cord-283092-t3yqsac3 author: Shah, Kamal title: Qualitative Analysis of a Mathematical Model in the Time of COVID-19 date: 2020-05-25 words: 3345.0 sentences: 235.0 pages: flesch: 60.0 cache: ./cache/cord-283092-t3yqsac3.txt txt: ./txt/cord-283092-t3yqsac3.txt summary: In this article, a qualitative analysis of the mathematical model of novel corona virus named COVID-19 under nonsingular derivative of fractional order is considered. Under the new nonsingular derivative, we, first of all, establish some sufficient conditions for existence and uniqueness of solution to the model under consideration. For the semianalytical results, we extend the usual Laplace transform coupled with Adomian decomposition method to obtain the approximate solutions for the corresponding compartments of the considered model. From Figure 1 , we see that at when the rate of healthy immigrants is zero, it means that protection rate is increasing and hence the population of infected class is decreasing while the population of healthy class is increasing at different rates due to fractional order derivative by evaluating the solution up to twenty terms via using MATAB. abstract: In this article, a qualitative analysis of the mathematical model of novel corona virus named COVID-19 under nonsingular derivative of fractional order is considered. The concerned model is composed of two compartments, namely, healthy and infected. Under the new nonsingular derivative, we, first of all, establish some sufficient conditions for existence and uniqueness of solution to the model under consideration. Because of the dynamics of the phenomenon when described by a mathematical model, its existence must be guaranteed. Therefore, via using the classical fixed point theory, we establish the required results. Also, we present the results of stability of Ulam's type by using the tools of nonlinear analysis. For the semianalytical results, we extend the usual Laplace transform coupled with Adomian decomposition method to obtain the approximate solutions for the corresponding compartments of the considered model. Finally, in order to support our study, graphical interpretations are provided to illustrate the results by using some numerical values for the corresponding parameters of the model. url: https://www.ncbi.nlm.nih.gov/pubmed/32596319/ doi: 10.1155/2020/5098598 id: cord-330714-hhvap8ts author: Shah, Kamal title: Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan date: 2020-11-12 words: 4537.0 sentences: 296.0 pages: flesch: 57.0 cache: ./cache/cord-330714-hhvap8ts.txt txt: ./txt/cord-330714-hhvap8ts.txt summary: This work is the consideration of a fractal fractional mathematical model on the transmission and control of corona virus (COVID-19), in which the total population of an infected area is divided into susceptible, infected and recovered classes. For the last few decades, it is noted that arbitrary-order equations of differentiations (FDEs) and integrations (FIDEs) can be use for modeling real world problems by much better way than integer order ODEs, PDEs and IDEs. In the 1750s when "Reimann and Liouvilli", "Euler and Fourier" give interesting analytical results in integer order of differential and integral calculus. [33, 54, 55, 62] Let us take the continuous and differentiable mapping ℧(t) in (a, b) with 0 < r ≤ 1 order, then the fractal-arbitrary order derivative of ℧(t) in ABC form with fractional order 0 < ω ≤ 1 and the law of power is given as abstract: This work is the consideration of a fractal fractional mathematical model on the transmission and control of corona virus (COVID-19), in which the total population of an infected area is divided into susceptible, infected and recovered classes. We consider a fractal-fractional order SIR type model for investigation of Covid-19. To realize the transmission and control of corona virus in a much better way, first we study the stability of the corresponding deterministic model using next generation matrix along with basic reproduction number. After this, we study the qualitative analysis using “fixed point theory” approach. Next, we use fractional Adams-Bashforth approach for investigation of approximate solution to the considered model. At the end numerical simulation are been given by matlab to provide the validity of mathematical system having the arbitrary order and fractal dimension. url: https://www.sciencedirect.com/science/article/pii/S2211379720320052?v=s5 doi: 10.1016/j.rinp.2020.103560 id: cord-332729-f1e334g0 author: Shah, Nirav R. title: An Impact-Oriented Approach to Epidemiological Modeling date: 2020-09-21 words: 1638.0 sentences: 115.0 pages: flesch: 52.0 cache: ./cache/cord-332729-f1e334g0.txt txt: ./txt/cord-332729-f1e334g0.txt summary: 5 The Centers for Disease Control and Prevention (CDC) recently added policy development as a sixth item in its list of the major tasks of epidemiology in public health, but there remains no mention of the impact on the general public. For instance, the Covid Act Now (CAN) model is fully open-source, along with its data inputs (available at https://covidactnow.org). Both the New York Times and Georgetown University''s Center for Global Health, Science, and Security (available at https://covidamp.org/) have begun to collect data on COVID-19 policies by state and effective dates, including shelter-in-place and reopening orders. These eight considerations may enable COVID-19 data and models to become better harbingers of actionable, behavior-changing, and even life-saving information; to bridge the gap between scientific public health expertise and mainstream, layperson Are the data and model''s mechanisms and data sources publicly available for fact-checking and validation? abstract: nan url: https://doi.org/10.1007/s11606-020-06230-1 doi: 10.1007/s11606-020-06230-1 id: cord-336644-kgrdul35 author: Shao, Nian title: Dynamic models for Coronavirus Disease 2019 and data analysis date: 2020-03-24 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this letter, two time delay dynamic models, a Time Delay Dynamical–Novel Coronavirus Pneumonia (TDD‐NCP) model and Fudan‐Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID‐19). The TDD‐NCP model was developed recently by Chengąŕs group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD‐NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan‐CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD‐NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations. url: https://www.ncbi.nlm.nih.gov/pubmed/32327866/ doi: 10.1002/mma.6345 id: cord-031460-nrxtfl3i author: Sharma, Vikas Kumar title: Modeling and Forecasting of COVID-19 Growth Curve in India date: 2020-09-05 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this article, we analyze the growth pattern of COVID-19 pandemic in India from March 4 to July 11 using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt–Winters models. We found that the growth of COVID-19 cases follows a power regime of [Formula: see text] after the exponential growth. We found the optimal change points from where the COVID-19 cases shifted their course of growth from exponential to quadratic and then from quadratic to linear. After that, we saw a sudden spike in the course of the spread of COVID-19 and the growth moved from linear to quadratic and then to quartic, which is alarming. We have also found the best fitted regression models using the various criteria, such as significant p-values, coefficients of determination and ANOVA, etc. Further, we search the best-fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of COVID-19 cases for future days. We also use usual exponential smoothing and Holt–Winters models for forecasting purpose. We further found that the ARIMA (5, 2, 5) model is the best-fitting model for COVID-19 cases in India. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474330/ doi: 10.1007/s41403-020-00165-z id: cord-325738-c800ynvc author: Shi, Pengpeng title: SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration date: 2020-02-20 words: 2669.0 sentences: 158.0 pages: flesch: 53.0 cache: ./cache/cord-325738-c800ynvc.txt txt: ./txt/cord-325738-c800ynvc.txt summary: We established a new SEIR propagation dynamics model, that considered the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and isolate comprehensively. Through the Euler integration algorithm to solve the model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted. In this paper, we established a new SEIR propagation dynamics model, considering the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and quarantine comprehensively. Based on this new SEIR propagation dynamics model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted. abstract: Pneumonia patients of 2019-ncov in latent period are not easy to be effectively quarantined, but there is evidence that they have strong infectious ability. Here, the infectious ability of patients during the latent period is slightly less than that of the infected patients was assumed. We established a new SEIR propagation dynamics model, that considered the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and isolate comprehensively. Based on the raw epidemic data of China from January 23, 2020 to February 10, 2020, the dynamic parameters of the new present SEIR model are fitted. Through the Euler integration algorithm to solve the model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted. url: https://doi.org/10.1101/2020.02.16.20023655 doi: 10.1101/2020.02.16.20023655 id: cord-007129-qjdg46o9 author: Simoes, Joana Margarida title: Spatial Epidemic Modelling in Social Networks date: 2005-06-21 words: 1796.0 sentences: 94.0 pages: flesch: 51.0 cache: ./cache/cord-007129-qjdg46o9.txt txt: ./txt/cord-007129-qjdg46o9.txt summary: In this model, it was considered the social mobility network: the daily movement of individuals, which has been already referred in the literature as a complex network with a Small World behaviour [2] . In this paper, it is described a simulation system using artificial agents integrated with Geographical Information Systems (GIS) that helps to understand the spatial and temporal behaviour of a epidemic phenomena. The present model is inspired by a Site Exchange Cellular Automata [5] , which considers two phases for each time step: movement and infection, assuming there is no virus transmission while the individual is moving. Based on these regions, four ranges of movement were considered: neighbourhood, intra region, inter region and small world. The Small World movement simulation (Fig. 15 ) presents a totally different distribution of population. The movement should always be considered in human epidemics models. abstract: The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108765/ doi: 10.1063/1.1985395 id: cord-034846-05h2no14 author: Singer, Gonen title: Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic date: 2020-08-07 words: 7264.0 sentences: 360.0 pages: flesch: 50.0 cache: ./cache/cord-034846-05h2no14.txt txt: ./txt/cord-034846-05h2no14.txt summary: We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. We use the proposed measure to develop ordinal decision-tree-based ensemble approaches, i.e., ordinal AdaBoost and random forest models, which are known to outperform individual classifiers. The main objectives of this study are fourfold: (i) to extend the weighted information gain measure such that the classification error can be measured from a statistical value that is not necessarily defined by a single class; (ii) to develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used; (iii) to examine the advantage of combining ordinal decision-tree-based ensemble approaches with non-ordinal individual classifiers to leverage the strengths of each type of classifier; and (iv) to examine the ability to carry out multi-class identification of different levels of a daily growth factor using ordinal decision-tree-based ensemble approaches. abstract: In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. In such an application, some of the potential classification errors could have critical consequences. The classification tool will enable the spread of the epidemic to be tracked and controlled by yielding insights regarding the relationship between local containment measures and the daily growth factor. In order to benefit maximally from a variety of ordinal and non-ordinal algorithms, we also propose an ensemble majority voting approach to combine different algorithms into one model, thereby leveraging the strengths of each algorithm. We perform experiments in which the task is to classify the daily COVID-19 growth rate factor based on environmental factors and containment measures for 19 regions of Italy. We demonstrate that the ordinal algorithms outperform their non-ordinal counterparts with improvements in the range of 6–25% for a variety of common performance indices. The majority voting approach that combines ordinal and non-ordinal models yields a further improvement of between 3% and 10%. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517475/ doi: 10.3390/e22080871 id: cord-289917-2mxd7zxf author: Singh, Brijesh P. title: Modeling tempo of COVID‐19 pandemic in India and significance of lockdown date: 2020-08-04 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: A very special type of pneumonic disease that generated the COVID‐19 pandemic was first identified in Wuhan, China in December 2019 and is spreading all over the world. The ongoing outbreak presents a challenge for data scientists to model COVID‐19, when the epidemiological characteristics of the COVID‐19 are yet to be fully explained. The uncertainty around the COVID‐19 with no vaccine and effective medicine available until today create additional pressure on the epidemiologists and policy makers. In such a crucial situation, it is very important to predict infected cases to support prevention of the disease and aid in the preparation of healthcare service. In this paper, we have tried to understand the spreading capability of COVID‐19 in India taking into account of the lockdown period. The numbers of confirmed cases are increased in India and states in the past few weeks. A differential equation based simple model has been used to understand the pattern of COVID‐19 in India and some states. Our findings suggest that the physical distancing and lockdown strategies implemented in India are successfully reducing the spread and that the tempo of pandemic growth has slowed in recent days. url: https://doi.org/10.1002/pa.2257 doi: 10.1002/pa.2257 id: cord-338466-7uvta990 author: Singh, Brijesh P. title: Modeling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlook date: 2020-10-31 words: 9001.0 sentences: 478.0 pages: flesch: 57.0 cache: ./cache/cord-338466-7uvta990.txt txt: ./txt/cord-338466-7uvta990.txt summary: For the spread of COVID-19, when disease dynamics are still unclear, mathematical modeling helps us to estimate the cumulative number of positive cases in the present scenarios. There are already various measures such as social distancing, lockdown masking and washing hand regularly has been implemented to prevent the spread of COVID-19, but in absence of particular medicine and vaccine it is very important to predict how the infection is likely to develop among the population that support prevention of the disease and aid in the preparation of healthcare service. The logistic growth regression model is used for the estimation of the final size and its peak time of the COVID-19 pandemic in many countries of the World and found similar result obtained by SIR model (Batista, 2020) . abstract: A very special type of pneumonic disease that generated the COVID-19 was first identified in Wuhan, China in December 2019 and is spreading all over the world. The ongoing outbreak presents a challenge for data scientists to model COVID-19, when the epidemiological characteristics of the COVID-19 are yet to be fully explained. The uncertainty around the COVID-19 with no vaccine and effective medicine available till today create additional pressure on the epidemiologists and policy makers. In such a crucial situation, it is very important to predict infected cases to support prevention of the disease and aid in the preparation of healthcare service. India is fighting efficiently against COVID-19 and facing greater challenges because of its large population and high population density. Though the government of India is taking all needful steps to prevent its spread but it is not enough to control and stop spread of the disease so far, perhaps due to defiant nature of people living in India. Effective measure to control this disease, medical professionals needs to know the estimated size of this pandemic and pace. In this study, an attempt has been made to understand the spreading capability of COVID-19 in India through some simple models. Findings suggest that the lockdown strategies implemented in India are not successfully reducing the pace of the pandemic significantly after first lockdown. url: https://www.sciencedirect.com/science/article/pii/S0169716120300493 doi: 10.1016/bs.host.2020.10.005 id: cord-258316-uiusqr59 author: Spil, Ton A.M. title: Are serious games too serious? Diffusion of wearable technologies and the creation of a diffusion of serious games model date: 2020-08-18 words: 7512.0 sentences: 401.0 pages: flesch: 53.0 cache: ./cache/cord-258316-uiusqr59.txt txt: ./txt/cord-258316-uiusqr59.txt summary: A key theoretical contribution of this research is the identification of habit as a potential dependent variable for the intention to use wearables and the development of a diffusion model for serious games. We question the actual adoption and effectiveness of wearables and serious games -the principle of revealing and challenge prevailing beliefs and social practices -by making use of the IT adoption model as discussed in the previous section based on insights from innovation and adoption researchers like Davis, Bagozzi, and Warshaw (1989) , DeLone and McLean (1993) , Rogers (1983) and Venkatesh et al. We study how the adoption of serious wearable games can be improved -the principle of taking a value position -in order to help improve health on both an individual and societal level -the principles of individual emancipation and improvements in society -and try to improve diffusion models for serious games by identifying habit as a potential dependent variable for the intention to use wearables -the principle of improvements in social theories. abstract: Today globally, more people die from chronic diseases than from war and terrorism. This is not due to aging alone but also because we lead unhealthy lifestyles with little or no exercise and typically consume food with poor nutritional content. This paper proffers the design science research method to create an artefact that can help people study the diffusion of serious games. The ultimate goal of the study is to create a serious game that can help people to improve their balance in physical exercise, nutrition and well-being. To do this, first we conducted 97 interviews to study if wearables can be used for gathering health data. Analysis indicates that designers, manufacturers, and developers of wearables and associated software and apps should make their devices reliable, relevant, and user friendly. To increase the diffusion, adoption, and habitual usage of wearables key issues such as privacy and security need to be addressed as well. Then, we created a paper prototype and conducted a further 32 interviews to validate the first prototype of the game, especially with respect to the diffusion possibilities of the game. Results are positive from a formal technology acceptance point of view showing relevance and usefulness. But informally in the open questions some limitations also became visible. In particular, ease of use is extremely important for acceptance and calling it a game can in fact be an obstruction. Moreover, the artefact should not be patronizing and age differences can also pose problems, hence the title not to make the serious game too serious. Future research plans to address these problems in the next iteration while the future implementation plan seeks for big platforms or companies to diffuse the serious game. A key theoretical contribution of this research is the identification of habit as a potential dependent variable for the intention to use wearables and the development of a diffusion model for serious games. The hedonic perspective is added to the model as well as trust and perceived risks. This model ends the cycle of critical design with an improvement of theory as result contributing to the societal goal of decreasing Obesities and Diabetes. url: https://www.ncbi.nlm.nih.gov/pubmed/32836650/ doi: 10.1016/j.ijinfomgt.2020.102202 id: cord-229393-t3cpzmwj author: Srivastava, Ajitesh title: Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic date: 2020-04-23 words: 3671.0 sentences: 228.0 pages: flesch: 62.0 cache: ./cache/cord-229393-t3cpzmwj.txt txt: ./txt/cord-229393-t3cpzmwj.txt summary: By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends and provide extremely accurate predictions of confirmed cases at the level of countries and states of the United States. We do so by proposing two measures: (i) Contact Reduction Score that measure how much a region has reduced transmission; (ii) and Epidemic Reduction Score that measures how much reduction in confirmed cases a region has achieved compared to a hypothetical scenario where the trends had remained the same as a reference day in the past. Applying such machine learning-based models to a finer level (from countries to states/cities) and larger scale (more ''regions'' of the world) brings unique challenges in terms of unreported/noisy data and large number of model parameters, which will be explored in a future work. To incorporate the fast evolving trend of COVID-19 due to changing policies, we use weighted least squared to learn parameters β p i and δ p i from available reported data. abstract: Accurate forecasts of COVID-19 is central to resource management and building strategies to deal with the epidemic. We propose a heterogeneous infection rate model with human mobility for epidemic modeling, a preliminary version of which we have successfully used during DARPA Grand Challenge 2014. By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends and provide extremely accurate predictions of confirmed cases at the level of countries and states of the United States. We show that during the earlier part of the epidemic, using travel data increases the predictions. Training the model to forecast also enables learning characteristics of the epidemic. In particular, we show that changes in model parameters over time can help us quantify how well a state or a country has responded to the epidemic. The variations in parameters also allow us to forecast different scenarios such as what would happen if we were to disregard social distancing suggestions. url: https://arxiv.org/pdf/2004.11372v3.pdf doi: nan id: cord-214774-yro1iw80 author: Srivastava, Anuj title: Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown Measures date: 2020-04-25 words: 4987.0 sentences: 321.0 pages: flesch: 60.0 cache: ./cache/cord-214774-yro1iw80.txt txt: ./txt/cord-214774-yro1iw80.txt summary: This paper develops an agent-level simulation model, termed ALPS, for simulating the spread of an infectious disease in a confined community. From an epidemiological perspective, as large amount of infection, containment, and recovery data from the this pandemic becomes available over time, the community is currently relying essentially on simulation models to help assess situations and to evaluate options [1] . In this paper we develop a mathematical simulation model, termed ALPS, to replicate the spread of an infectious disease, such as COVID-19, in a confined community and to study the influence of some governmental interventions on final outcomes. [10] construct a detailed agent-based model for spread of infectious diseases, taking into account population demographics and other social conditions, but they do not consider countermeasures such as lockdowns in their simulations. In this section we develop our simulation model for agent-level interactions and spread of the infections across a population in a well-defined geographical domain. abstract: This paper develops an agent-level simulation model, termed ALPS, for simulating the spread of an infectious disease in a confined community. The mechanism of transmission is agent-to-agent contact, using parameters reported for Corona COVID-19 pandemic. The main goal of the ALPS simulation is analyze effects of preventive measures -- imposition and lifting of lockdown norms -- on the rates of infections, fatalities and recoveries. The model assumptions and choices represent a balance between competing demands of being realistic and being efficient for real-time inferences. The model provides quantification of gains in reducing casualties by imposition and maintenance of restrictive measures in place. url: https://arxiv.org/pdf/2004.12250v1.pdf doi: nan id: cord-322806-g01wmmbx author: Sturniolo, S. title: Testing, tracing and isolation in compartmental models date: 2020-05-19 words: 9749.0 sentences: 531.0 pages: flesch: 53.0 cache: ./cache/cord-322806-g01wmmbx.txt txt: ./txt/cord-322806-g01wmmbx.txt summary: This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. It provides a logical framework for understanding the propagation of an May 14, 2020 1/23 infectious disease through a population and allows different interventions to be explored, including testing and contact tracing of infected individuals as possible strategies to ease social distancing restrictions. In this paper we develop an extension to the classic Susceptible-Exposed-Infectious-Removed 1 (SEIR) model [16, 52, 53] simulated with ODEs to include testing, contacttracing, and isolation (TTI) strategies. To answer this we adapt the standard Susceptible-Exposed-Infectious-Removed (SEIR) compartmental model [16, 52] to incorporate contact tracing as well as testing and isolation of cohorts of people. Overlapping compartments represent model states that are not mutually exclusive, so that it is possible for an individual to belong in more than one of them e.g. be infected and contact-traced, or exposed and tested. abstract: Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of testing, contact tracing and isolation. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computationally efficiency is such that it be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks. url: https://doi.org/10.1101/2020.05.14.20101808 doi: 10.1101/2020.05.14.20101808 id: cord-027228-s32v6bmd author: Subramanian, Vigneshwar title: Editorial: Why is modeling COVID-19 so difficult? date: 2020-06-19 words: 1117.0 sentences: 62.0 pages: flesch: 51.0 cache: ./cache/cord-027228-s32v6bmd.txt txt: ./txt/cord-027228-s32v6bmd.txt summary: Disease spread depends heavily on the prevalence of COVID-19, which is not precisely known, and on policy interventions such as social distancing, which are a moving target and not intrinsically measurable. For example, the University of Texas model uses phone geolocation data as a proxy for social distancing and assumes the intervention remains constant across the forecasted time period 5 . Assumptions may also change over time as information emerges and their performance is reassessed; for example, the Columbia model updated contact tracing assumptions to the current parameters to model loosening social distancing restrictions as states reopen 6 . The general workflow involved in developing such a model is as follows: first, the outcome of interest is defined; second, relevant predictors or risk factors are identified; third, the effects of each predictor variable are estimated, for example in a regression analysis; and finally, the model is validated 7 . abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303028/ doi: 10.1016/j.chest.2020.06.014 id: cord-182586-xdph25ld author: Sun, Fei title: Dynamics of an imprecise stochastic multimolecular biochemical reaction model with L'{e}vy jumps date: 2020-04-26 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Population dynamics are often affected by sudden environmental perturbations. Parameters of stochastic models are often imprecise due to various uncertainties. In this paper, we formulate a stochastic multimolecular biochemical reaction model that includes L'{e}vy jumps and interval parameters. Firstly, we prove the existence and uniqueness of the positive solution. Moreover, the threshold between extinction and persistence of the reaction is obtained. Finally, some simulations are carried out to demonstrate our theoretical results. url: https://arxiv.org/pdf/2004.14163v1.pdf doi: nan id: cord-331646-j5mkparg author: Sze To, G. N. title: Review and comparison between the Wells–Riley and dose‐response approaches to risk assessment of infectious respiratory diseases date: 2009-07-31 words: 11058.0 sentences: 551.0 pages: flesch: 45.0 cache: ./cache/cord-331646-j5mkparg.txt txt: ./txt/cord-331646-j5mkparg.txt summary: Dose‐response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Dose-response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Some newer studies have proposed to use dose-response models for assessing the infection risk of airborne-transmissible pathogens (e.g., Armstrong and Haas, 2007a; Nicas, 1996; Sze To et al., 2008) . Some Review of the Wells-Riley and dose-response models studies also suggested that the deposition loss of infectious particles and the viability loss of pathogens while airborne can also be considered by adding these sink terms in the denominator, similar to Equation 11 (Fisk et al., 2005; Franchimon et al., 2008) : abstract: Abstract Infection risk assessment is very useful in understanding the transmission dynamics of infectious diseases and in predicting the risk of these diseases to the public. Quantitative infection risk assessment can provide quantitative analysis of disease transmission and the effectiveness of infection control measures. The Wells–Riley model has been extensively used for quantitative infection risk assessment of respiratory infectious diseases in indoor premises. Some newer studies have also proposed the use of dose‐response models for such purpose. This study reviews and compares these two approaches to infection risk assessment of respiratory infectious diseases. The Wells–Riley model allows quick assessment and does not require interspecies extrapolation of infectivity. Dose‐response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Spatial distribution of airborne pathogens is one of the most important factors in infection risk assessment of respiratory disease. Respiratory deposition of aerosol induces heterogeneous infectivity of intake pathogens and randomness on the intake dose, which are not being well accounted for in current risk models. Some suggestions for further development of the risk assessment models are proposed. PRACTICAL IMPLICATIONS: This review article summarizes the strengths and limitations of the Wells–Riley and the dose‐response models for risk assessment of respiratory diseases. Even with many efforts by various investigators to develop and modify the risk assessment models, some limitations still persist. This review serves as a reference for further development of infection risk assessment models of respiratory diseases. The Wells–Riley model and dose‐response model offer specific advantages. Risk assessors can select the approach that is suitable to their particular conditions to perform risk assessment. url: https://www.ncbi.nlm.nih.gov/pubmed/19874402/ doi: 10.1111/j.1600-0668.2009.00621.x id: cord-283678-xdma6vyo author: Séférian, Roland title: Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6 date: 2020-08-18 words: 10700.0 sentences: 570.0 pages: flesch: 44.0 cache: ./cache/cord-283678-xdma6vyo.txt txt: ./txt/cord-283678-xdma6vyo.txt summary: PURPOSE OF REVIEW: The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). SUMMARY: Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP). Our review of available ESMs suggests that the current generation of marine biogeochemical models has not much evolved toward comprehensive couplings between Earth system components and ocean biogeochemistry or toward improved treatment of biophysical and biogeochemical feedback with respect to their predecessors (F1 and F4 in Fig. 1 ). abstract: PURPOSE OF REVIEW: The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). RECENT FINDINGS: The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. SUMMARY: Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40641-020-00160-0) contains supplementary material, which is available to authorized users. url: https://doi.org/10.1007/s40641-020-00160-0 doi: 10.1007/s40641-020-00160-0 id: cord-259426-qbolo3k3 author: Tadesse, Trhas title: Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020 date: 2020-10-22 words: 5279.0 sentences: 264.0 pages: flesch: 54.0 cache: ./cache/cord-259426-qbolo3k3.txt txt: ./txt/cord-259426-qbolo3k3.txt summary: title: Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020 Therefore, this study investigated the predictors of COVID-19 prevention practice using the Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. Three hundred ninety-one (62.3%), 337 (53.7%), 312 (49.7), 497 (79.1%), 303 (48.2%) and 299 (52.4%) of the respondents had high perceived susceptibility, severity, benefit, barrier, cues to action and self-efficacy to COVID-19 prevention practice, respectively. Therefore, this study was aimed at assessing predictors of COVID-19 prevention practice among Higher Education employees in Addis Ababa Ethiopia using a Health Belief Model. A multicentered cross-sectional study design was used to assess predictors of COVID-19 prevention practices using a Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. The questionnaire was used to gather employees'' demographic data, knowledge about COVID-19 and its prevention, Health Belief Model constructs (perceived susceptibility, perceived severity, perceived benefit, perceived barrier, and cues to action self-efficacy), and practice of COVID-19 prevention. abstract: BACKGROUND: Ethiopia has taken strict preventive measures against COVID-19 to control its spread, to protect citizens, and ensure their wellbeing. Employee’s adherence to preventive measures is influenced by their knowledge, perceived susceptibility, severity, benefit, barrier, cues to action, and self-efficacy. Therefore, this study investigated the predictors of COVID-19 prevention practice using the Health Belief Model among employees in Addis Ababa, Ethiopia, 2020. METHODS: Multicentre cross-sectional study design was used. A total of 628 employees selected by systematic sampling method were included in this study. Data were collected using a pretested self-administered questionnaire. Summary statistics of a given data for each variable were calculated. Logistic regression model was used to measure the association between the outcome and the predictor variable. Statistical significance was declared at p-value<0.05. Direction and strength of association were expressed using OR and 95% CI. RESULTS: From a total of 628 respondents, 432 (68.8%) of them had poor COVID-19 prevention practice. Three hundred ninety-one (62.3%), 337 (53.7%), 312 (49.7), 497 (79.1%), 303 (48.2%) and 299 (52.4%) of the respondents had high perceived susceptibility, severity, benefit, barrier, cues to action and self-efficacy to COVID-19 prevention practice, respectively. Employees with a low level of perceived barriers were less likely to have a poor practice of COVID-19 prevention compared to employees with a high level of perceived barrier [AOR = 0.03, 95% CI (0.01,0.05)]. Similarly, employees with low cues to action and employees with a low level of self-efficacy were practiced COVID prevention measures to a lesser extent compared those with high cues to action and high level of self-efficacy [AOR = 0.05, 95% CI (0.026,0.10)] and [AOR = 0.08, 95% CI (0.04,0.14)], respectively. CONCLUSION: The proportion of employees with poor COVID-19 prevention was high. Income, perceived barrier, cues to action, and self-efficacy were significantly associated with COVID-19 prevention practice. url: https://www.ncbi.nlm.nih.gov/pubmed/33122922/ doi: 10.2147/idr.s275933 id: cord-296826-870mxd1t author: Taghikhah, Firouzeh title: Integrated modeling of extended agro-food supply chains: A systems approach date: 2020-06-27 words: 10522.0 sentences: 534.0 pages: flesch: 43.0 cache: ./cache/cord-296826-870mxd1t.txt txt: ./txt/cord-296826-870mxd1t.txt summary: Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. In developing the proposed ESSC model considering the heterogeneity of consumers, we take an integrated modeling approach combining agent-based modeling (ABM), discrete event simulation (DES), and system dynamics (SD) to simulate both production and consumption side of the operation and the feedbacks between them. In response to this call, our study presents the development of an extended food SC model that incorporates the dynamics of farmers, processors, retailers, and consumers behavior as well as sustainability aspects. A growing number of studies focuses on improving the productivity of organic agriculture from sustainability perspectives; yet, the relationships between the behavior of final consumers and the decisions of upstream supply chain actors, in this case, farmers, have been poorly analyzed (Naik & Suresh, 2018; Taghikhah et al., 2019) . abstract: The current intense food production-consumption is one of the main sources of environmental pollution and contributes to anthropogenic greenhouse gas emissions. Organic farming is a potential way to reduce environmental impacts by excluding synthetic pesticides and fertilizers from the process. Despite ecological benefits, it is unlikely that conversion to organic can be financially viable for farmers, without additional support and incentives from consumers. This study models the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain. Findings demonstrate the feasibility and superiority of the proposed model over the traditional sustainable supply chain models in incorporating the feedback between consumers and producers and analyzing management scenarios that can urge farmers to expand organic agriculture. Results further indicate that demand-side participation in transition pathways towards sustainable agriculture can become a time-consuming effort if not accompanied by the middle actors between consumers and farmers. In practice, our proposed model may serve as a decision-support tool to guide evidence-based policymaking in the food and agriculture sector. url: https://www.sciencedirect.com/science/article/pii/S0377221720305798?v=s5 doi: 10.1016/j.ejor.2020.06.036 id: cord-313046-3g2us5zh author: Taghizadeh, L. title: Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic date: 2020-06-03 words: 5180.0 sentences: 355.0 pages: flesch: 55.0 cache: ./cache/cord-313046-3g2us5zh.txt txt: ./txt/cord-313046-3g2us5zh.txt summary: We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. In this work, we propose Bayesian inference for the analysis of the COVID-19 data in order to estimate the crucial unknown quantities of the pandemic models. We use an adaptive MCMC method to find the probability distributions and confidence intervals of the epidemiological models parameters using the Austrian infection data. In this section, we present simulation results of Bayesian inversion and the adaptive MCMC method (see Algorithm 1) for the two epidemic models, namely the logistic and the SIR models, using the data of the COVID-19 outbreak in Austria. According to Bayesian analysis, the unknown parameters of the logistic and SIR models using the data of COVID-19 outbreak in Austria were found and summarized in Table 1 and Table 3 , respectively. abstract: The main goal of this paper is to develop the forward and inverse modeling of the Coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental protective decision making. We perform our analyses on the publicly available data for Austria to estimate the main epidemiological model parameters and to study the effectiveness of the protective measures by the Austrian government. The estimated parameters and the analysis of fatalities provide useful information for decision makers and makes it possible to perform more realistic forecasts of future outbreaks. url: https://doi.org/10.1101/2020.05.30.20117754 doi: 10.1101/2020.05.30.20117754 id: cord-027119-zazr8uj5 author: Taif, Khasrouf title: Cast Shadow Generation Using Generative Adversarial Networks date: 2020-05-25 words: 4029.0 sentences: 221.0 pages: flesch: 56.0 cache: ./cache/cord-027119-zazr8uj5.txt txt: ./txt/cord-027119-zazr8uj5.txt summary: Generative Adversarial Networks have been implemented widely to perform graphical tasks, as it requires minimum to no human interaction, which gives GANs a great advantage over conventional deep learning methods, such as image-to-image translation with single D, G semi-supervised model [7] or unsupervised dual learning [26] . We apply image-to-image translation to our own image set to generate correct cast shadows for 3D rendered images in a semi-supervised manner using colour labels. We start with the assumption that GANs can generate both soft and hard shadows on demand, using colour labels and given a relatively small training image set. This paper explored a framework based on conditional GANs using a pix2pix Tensorflow port to perform computer graphic functions, by instructing the network to successfully generate shadows for 3D rendered images given training images paired with conditional colour labels. abstract: We propose a computer graphics pipeline for 3D rendered cast shadow generation using generative adversarial networks (GANs). This work is inspired by the existing regression models as well as other convolutional neural networks such as the U-Net architectures which can be geared to produce believable global illumination effects. Here, we use a semi-supervised GANs model comprising of a PatchGAN and a conditional GAN which is then complemented by a U-Net structure. We have adopted this structure because of its training ability and the quality of the results that come forth. Unlike other forms of GANs, the chosen implementation utilises colour labels to generate believable visual coherence. We carried forth a series of experiments, through laboratory generated image sets, to explore the extent at which colour can create the correct shadows for a variety of 3D shadowed and un-shadowed images. Once an optimised model is achieved, we then apply high resolution image mappings to enhance the quality of the final render. As a result, we have established that the chosen GANs model can produce believable outputs with the correct cast shadows with plausible scores on PSNR and SSIM similarity index metrices. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302543/ doi: 10.1007/978-3-030-50426-7_36 id: cord-024501-nl0gsr0c author: Tan, Chunyang title: MSGE: A Multi-step Gated Model for Knowledge Graph Completion date: 2020-04-17 words: 3236.0 sentences: 206.0 pages: flesch: 55.0 cache: ./cache/cord-024501-nl0gsr0c.txt txt: ./txt/cord-024501-nl0gsr0c.txt summary: In this paper, we first integrate iterative mechanism into knowledge graph embedding and propose a multi-step gated model which utilizes relations as queries to extract useful information from coarse to fine in multiple steps. First gate mechanism is adopted to control information flow by the interaction between entity and relation with multiple steps. In this paper, we propose a Multi-Step Gated Embedding (MSGE) model for link prediction in KGs. During every step, gate mechanism is applied several times, which is used to decide what features are retained and what are excluded at the dimension level, corresponding to the multi-step reasoning procedure. All results demonstrate our motivation that controlling information flow in a multi-step way is beneficial for link prediction task in knowledge graphs. In this paper, we propose a multi-step gated model MSGE for link prediction task in knowledge graph completion. abstract: Knowledge graph embedding models aim to represent entities and relations in continuous low-dimensional vector space, benefiting many research areas such as knowledge graph completion and web searching. However, previous works do not consider controlling information flow, which makes them hard to obtain useful latent information and limits model performance. Specifically, as human beings, predictions are usually made in multiple steps with every step filtering out irrelevant information and targeting at helpful information. In this paper, we first integrate iterative mechanism into knowledge graph embedding and propose a multi-step gated model which utilizes relations as queries to extract useful information from coarse to fine in multiple steps. First gate mechanism is adopted to control information flow by the interaction between entity and relation with multiple steps. Then we repeat the gate cell for several times to refine the information incrementally. Our model achieves state-of-the-art performance on most benchmark datasets compared to strong baselines. Further analyses demonstrate the effectiveness of our model and its scalability on large knowledge graphs. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206261/ doi: 10.1007/978-3-030-47426-3_33 id: cord-260407-jf1dnllj author: Tang, Catherine So-kum title: Factors influencing the wearing of facemasks to prevent the severe acute respiratory syndrome among adult Chinese in Hong Kong date: 2004-06-11 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Background. The global outbreak of the severe acute respiratory syndrome (SARS) in 2003 has been an international public health threat. Quick diagnostic tests and specific treatments for SARS are not yet available; thus, prevention is of paramount importance to contain its global spread. This study aimed to determine factors associating with individuals' practice of the target SARS preventive behavior (facemask wearing). Methods. A total of 1329 adult Chinese residing in Hong Kong were surveyed. The survey instrument included demographic data, measures on the five components of the Health Belief Model, and the practice of the target SARS preventive behavior. Logistic regression analyses were conducted to determine rates and predictors of facemask wearing. Results. Overall, 61.2% of the respondents reported consistent use of facemasks to prevent SARS. Women, the 50–59 age group, and married respondents were more likely to wear facemasks. Three of the five components of the Health Belief Model, namely, perceived susceptibility, cues to action, and perceived benefits, were significant predictors of facemask-wearing even after considering effects of demographic characteristics. Conclusions. The Health Belief Model is useful in identifying determinants of facemask wearing. Findings have significant implications in enhancing the effectiveness of SARS prevention programs. url: https://www.ncbi.nlm.nih.gov/pubmed/15539054/ doi: 10.1016/j.ypmed.2004.04.032 id: cord-321984-qjfkvu6n author: Tang, Lu title: A Review of Multi‐Compartment Infectious Disease Models date: 2020-08-03 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper. url: https://www.ncbi.nlm.nih.gov/pubmed/32834402/ doi: 10.1111/insr.12402 id: cord-013784-zhgjmt2j author: Tang, Min title: Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions date: 2020-06-04 words: 13704.0 sentences: 794.0 pages: flesch: 45.0 cache: ./cache/cord-013784-zhgjmt2j.txt txt: ./txt/cord-013784-zhgjmt2j.txt summary: To move beyond serum-free sphere culture-based models, we utilized a DLP-based rapid 3D bioprinting system to generate 3D tri-culture or tetra-culture glioblastoma tissue models, with a background "normal brain" made up of NPCs and astrocytes and a tumor mass generated by GSCs, with or without macrophage, using brain-specific extracellular matrix (ECM) materials (Fig. 1a ). 35 While patient-derived glioblastoma cells grown under serum-free conditions enrich for stem-like tumor cells (GSCs) that form spheres and more closely replicate transcriptional profiles and invasive potential than standard culture conditions, we previously demonstrated that spheres display differential transcriptional profiles and cellular dependencies in an RNA interference screen compared to in vivo xenografts. [49] [50] [51] g Therapeutic efficacy prediction of drugs in all cancer cells in the CTRP dataset based on differentially expressed genes between the 3D tetra-culture model and GSCs grown in sphere culture as defined by RNA-seq. abstract: Brain tumors are dynamic complex ecosystems with multiple cell types. To model the brain tumor microenvironment in a reproducible and scalable system, we developed a rapid three-dimensional (3D) bioprinting method to construct clinically relevant biomimetic tissue models. In recurrent glioblastoma, macrophages/microglia prominently contribute to the tumor mass. To parse the function of macrophages in 3D, we compared the growth of glioblastoma stem cells (GSCs) alone or with astrocytes and neural precursor cells in a hyaluronic acid-rich hydrogel, with or without macrophage. Bioprinted constructs integrating macrophage recapitulate patient-derived transcriptional profiles predictive of patient survival, maintenance of stemness, invasion, and drug resistance. Whole-genome CRISPR screening with bioprinted complex systems identified unique molecular dependencies in GSCs, relative to sphere culture. Multicellular bioprinted models serve as a scalable and physiologic platform to interrogate drug sensitivity, cellular crosstalk, invasion, context-specific functional dependencies, as well as immunologic interactions in a species-matched neural environment. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608409/ doi: 10.1038/s41422-020-0338-1 id: cord-346265-jx4kspen author: Tatapudi, Hanisha title: Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region date: 2020-10-19 words: 5638.0 sentences: 315.0 pages: flesch: 52.0 cache: ./cache/cord-346265-jx4kspen.txt txt: ./txt/cord-346265-jx4kspen.txt summary: In this paper, we investigate a few ''what-if'' scenarios for social intervention policies including if the stay-at-home order were not lifted, if the Phase II order continues unaltered, what impact will the universal face mask usage have on the infections and deaths, and finally, how do the benefits of contact tracing vary with various target levels for identifying asymptomatic and pre-symptomatic. We conduct our investigation by first developing a comprehensive agent-based simulation model for COVID-19, and then using a major urban outbreak region (Miami-Dade County hospitalization (if infected with acute illness); and 10) recovery or death (if infected). The model also considers: varying levels of compliances for isolation and quarantine, lower on-site staffing levels of essential work and community places during stay-at-home order, restricted daily schedule of people during various social intervention periods, phased lifting of interventions, use of face masks in workplaces, schools and community places with varying compliance levels, and contact tracing with different target levels to identify asymptomatic and presymptomatic cases. abstract: PURPOSE: Social intervention strategies to mitigate COVID-19 are examined using an agent-based simulation model. Outbreak in a large urban region, Miami-Dade County, Florida, USA is used as a case study. Results are intended to serve as a planning guide for decision makers. METHODS: The simulation model mimics daily social mixing behavior of the susceptible and infected generating the spread. Data representing demographics of the region, virus epidemiology, and social interventions shapes model behavior. Results include daily values of infected, reported, hospitalized, and dead. RESULTS: Results show that early implementation of complete stay-at-home order is effective in flattening and reversing the infection growth curve in a short period of time. Whereas, using Florida's Phase II plan alone could result in 75% infected and end of pandemic via herd immunity. Universal use of face masks reduced infected by 20%. A further reduction of 66% was achieved by adding contact tracing with a target of identifying 50% of the asymptomatic and pre-symptomatic. CONCLUSIONS: In the absence of a vaccine, the strict stay-at-home order, though effective in curbing a pandemic outbreak, leaves a large proportion of the population susceptible. Hence, there should be a strong follow up plan of social distancing, use of face mask, contact tracing, testing, and isolation of infected to minimize the chances of large-scale resurgence of the disease. However, as the economic cost of the complete stay-at-home-order is very high, it can perhaps be used only as an emergency first response, and the authorities should be prepared to activate a strong follow up plan as soon as possible. The target level for contact tracing was shown to have a nonlinear impact on the reduction of the percentage of population infected. Increase in contact tracing target from 20% to 30% appeared to provide the largest incremental benefit. url: https://doi.org/10.1016/j.gloepi.2020.100036 doi: 10.1016/j.gloepi.2020.100036 id: cord-174036-b3frnfr7 author: Thomas, Loring J. title: Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity date: 2020-05-20 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities. url: https://arxiv.org/pdf/2005.09850v1.pdf doi: nan id: cord-303651-fkdep6cp author: Thompson, Robin N. title: Key questions for modelling COVID-19 exit strategies date: 2020-08-12 words: 11567.0 sentences: 587.0 pages: flesch: 40.0 cache: ./cache/cord-303651-fkdep6cp.txt txt: ./txt/cord-303651-fkdep6cp.txt summary: This leads to a roadmap for future research (figure 1) made up of three key steps: (i) improve estimation of epidemiological parameters using outbreak data from different countries; (ii) understand heterogeneities within and between populations that affect virus transmission and interventions; and (iii) focus on data needs, particularly data collection and methods for planning exit strategies in low-to-middle-income countries (LMICs) where data are often lacking. Three key steps are required: (i) improve estimates of epidemiological parameters (such as the reproduction number and herd immunity fraction) using data from different countries ( §2a-d); (ii) understand heterogeneities within and between populations that affect virus transmission and interventions ( §3a-d); and (iii) focus on data requirements for predicting the effects of individual interventions, particularly-but not exclusively-in data-limited settings such as LMICs ( §4a-c). abstract: Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health. url: https://arxiv.org/pdf/2006.13012v4.pdf doi: 10.1098/rspb.2020.1405 id: cord-020610-hsw7dk4d author: Thys, Séverine title: Contesting the (Super)Natural Origins of Ebola in Macenta, Guinea: Biomedical and Popular Approaches date: 2019-10-12 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In December 2013, a two-year-old child died from viral haemorrhagic fever in Méliandou village in the South-East of Guinea, and constituted the likely index case of a major epidemic. When the virus was formally identified as Ebola, epidemiologists started to investigate the chains of transmission, while local people were trying to make sense out of these deaths. The epidemic control measures taken by national and international health agencies were soon faced by strong reluctance and a sometimes aggressive attitude of the affected communities. Based on ethnographic work in Macenta (Forest region) in the autumn of 2014 for the Global Outbreak and Alert Response Network (GOARN) of the World Health Organization, this chapter shows that while epidemiologists involved in the outbreak response attributed the first Ebola deaths in the Forest region to the transmission of a virus from an unknown animal reservoir, local citizens believed these deaths were caused by the breach of a taboo. Epidemiological and popular explanations, mainly evolving in parallel, but sometimes overlapping, were driven by different explanatory models: a biomedical model embodying nature in the guise of an animal disease reservoir, which in turn poses as threat to humanity, and a traditional-religious model wherein nature and culture are not dichotomized. The chapter will argue that epidemic responses must be flexible and need to systematically document popular discourse(s), rumours, codes, practices, knowledge and opinions related to the outbreak event. This precious information must be used not only to shape and adapt control interventions and health promotion messages, but also to trace the complex biosocial dynamics of such zoonotic disease beyond the usual narrow focus on wild animals as the sources of infection. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141173/ doi: 10.1007/978-3-030-26795-7_7 id: cord-027201-owzhv0xy author: Tkacz, Magdalena A. title: Advantage of Using Spherical over Cartesian Coordinates in the Chromosome Territories 3D Modeling date: 2020-06-15 words: 3143.0 sentences: 191.0 pages: flesch: 55.0 cache: ./cache/cord-027201-owzhv0xy.txt txt: ./txt/cord-027201-owzhv0xy.txt summary: This paper shows results of chromosome territory modeling in two cases: when the implementation of the algorithm was based on Cartesian coordinates and when implementation was made with Spherical coordinates. In the article, the summary of measurements of computational times of simulation of chromatin decondensation process (which led to constitute the chromosome territory) was presented. Initially, when implementation was made with the use of Cartesian Coordinates, simulation takes a lot of time to create a model (mean 746.7[sec] with the median 569.1[sec]) and additionally requires restarts of the algorithm, also often exceeds acceptable (given a priori) time for the computational experiment. This paper shows some new knowledge that we discover while trying to model chromosome territories (CT''s) being a final result of modeling and simulation chromatin decondensation (CD) process and documents some problems (and the way we took to solve them) to make the working model. abstract: This paper shows results of chromosome territory modeling in two cases: when the implementation of the algorithm was based on Cartesian coordinates and when implementation was made with Spherical coordinates. In the article, the summary of measurements of computational times of simulation of chromatin decondensation process (which led to constitute the chromosome territory) was presented. Initially, when implementation was made with the use of Cartesian Coordinates, simulation takes a lot of time to create a model (mean 746.7[sec] with the median 569.1[sec]) and additionally requires restarts of the algorithm, also often exceeds acceptable (given a priori) time for the computational experiment. Because of that, authors attempted changing the coordinate system to Spherical Coordinates (in a few previous projects it leads to improving the efficiency of implementation). After changing the way that 3D point is represented in 3D space the time required to make a successful model reduced to the mean 25.3[sec] with a median 18.5[s] (alongside with lowering the number of necessary algorithm restarts) which gives a significant difference in the efficiency of model’s creation. Therefore we showed, that a more efficient way for implementation was the usage of spherical coordinates. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302845/ doi: 10.1007/978-3-030-50417-5_49 id: cord-296565-apqm0i58 author: Togati, Teodoro Dario title: General Theorizing and Historical Specificity in the ‘Keynes Versus the Classics’ Dispute’ date: 2020-10-06 words: 10625.0 sentences: 397.0 pages: flesch: 44.0 cache: ./cache/cord-296565-apqm0i58.txt txt: ./txt/cord-296565-apqm0i58.txt summary: For example, both Keynes''s theory and standard macro share a common feature: while accepting a universalistic approach to theorizing-stressing behavioural hardcore ''drivers'' such as agents'' optimizing choices or psychological laws-they are not truly universal in their scope or object of analysis: both theories also include 1 One major question which arises today, for example, is whether the current ''consensus'' macro is general enough to accommodate the post-Covid-19 scenario. 17 Based on the ADM and its sophisticated institutional setting, this hard core plays a key role in modern economics because it turns ''choice theory'' into the only possible ''natural laws''-i.e. those forms of agents'' behaviour that are true whatever the context-generating the basic rules of ''grammar'' of economics, from which serious theorists cannot simply depart if they want to be understood by their peers. abstract: This paper addresses the issues of general theorizing and historical specificity in the ‘Keynes versus the Classics’ dispute and puts forward two main arguments. First, the current macroeconomic orthodoxy wins the ‘relative’ generality contest because it implies that institutions influence outcomes, such as the natural rate of unemployment, in contrast with Keynes’s ‘internalist’ approach, which neglects historical specificity. Secondly, mainstream macro is not truly general in an ‘absolute’ sense since it only makes sense under very special real-world institutional conditions. url: https://www.ncbi.nlm.nih.gov/pubmed/33041383/ doi: 10.1057/s41302-020-00177-1 id: cord-297517-w8cvq0m5 author: Toğaçar, Mesut title: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches date: 2020-05-06 words: 4678.0 sentences: 320.0 pages: flesch: 58.0 cache: ./cache/cord-297517-w8cvq0m5.txt txt: ./txt/cord-297517-w8cvq0m5.txt summary: title: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. [9] performed a classification algorithm using pneumonia data, SVM as a classification method, and InceptionV3, VGG-16 models as a deep learning approach. Using pneumonia and normal chest X-ray images, they set 30% of the dataset as test data and compared the proposed approach with the existing CNNs. They achieved 89.57% classification success. The second dataset is important in this study to compare COVID-19 chest images using deep learning models. abstract: Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease. url: https://www.sciencedirect.com/science/article/pii/S0010482520301736?v=s5 doi: 10.1016/j.compbiomed.2020.103805 id: cord-313279-15wii9nn author: Trevijano-Contador, Nuria title: Expanding the use of alternative models to investigate novel aspects of immunity to microbial pathogens date: 2014-05-15 words: 2122.0 sentences: 107.0 pages: flesch: 38.0 cache: ./cache/cord-313279-15wii9nn.txt txt: ./txt/cord-313279-15wii9nn.txt summary: In the present issue of Virulence, an article entitled "The maternal transfer of bacteria can mediate trans-generational immune priming in insects" 1 describes an elegant study that illustrates the use of the lepidopteran Galleria mellonella to investigate a specific aspect of immunity to microbes. But in addition, this study opens the scope on the use of non-conventional models and illustrates how they can be used to investigate aspects of immunity against pathogenic microorganisms. These models have been a useful tool for centuries, and the development of their genetic manipulation offers new alternatives to investigate the role of specific factors of the immune system in the defense against pathogens. Among insects, there are two species largely used as model hosts to study microbial virulence, Drosophila melanogaster and Galleria mellonella. melanogaster also a suitable model to investigate the role of host elements in the response against microbial pathogens. abstract: nan url: https://doi.org/10.4161/viru.28775 doi: 10.4161/viru.28775 id: cord-161039-qh9hz4wz author: Tripathy, Shrabani S. title: Flood Evacuation During Pandemic: A multi-objective Framework to Handle Compound Hazard date: 2020-10-03 words: 2923.0 sentences: 164.0 pages: flesch: 48.0 cache: ./cache/cord-161039-qh9hz4wz.txt txt: ./txt/cord-161039-qh9hz4wz.txt summary: This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter''s stay. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. Various studies have used optimization models for flood evacuation to minimize losses considering factors like travel time and distance, cost of evacuation, and usage of infrastructure (5, 6, 14, (17) (18) (19) . Jagatsinghpur is a coastal (east coast) district in the state Odisha, India (Figure 2 The first step in designing any evacuation strategy is to identify the villages with high flood hazard. Shelter location-allocation model for flood evacuation planning A spatiotemporal optimization model for the evacuation of the population exposed to flood hazard abstract: The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees for a temporary period. Floods during pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max-min approach. The simulation model consists of an extended Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model.We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards. url: https://arxiv.org/pdf/2010.01386v1.pdf doi: nan id: cord-001603-vlv8x8l8 author: Ul-Haq, Zaheer title: 3D Structure Prediction of Human β1-Adrenergic Receptor via Threading-Based Homology Modeling for Implications in Structure-Based Drug Designing date: 2015-04-10 words: 5592.0 sentences: 298.0 pages: flesch: 51.0 cache: ./cache/cord-001603-vlv8x8l8.txt txt: ./txt/cord-001603-vlv8x8l8.txt summary: ORCHESTRAR is specifically designed for homology or comparative protein modeling that identifies structurally conserved regions (SCRs), models loops using model-based and ab-initio methods, models side chains, and combine them all to prepare a final model. Initially, a homology model was generated by ORCHESTRAR that lacks a region of 45 amino acid residues (209-254) of the cytoplasmic loop of TM5 located within the target sequence but absent in the template structure. Two conserved disulfide bridges which are important for cell surface expression, ligand binding, receptor activation and maintenance of the secondary structure are located in EL-2 and EL-3 regions at positions Cys81-Cys166 and Cys159-Cys165, respectively (Table 5 ). The docking results reveals that Ser178 and Phe168 are crucial residues in ligand binding by providing H-bonding, and π-π interactions, respectively, thus helps in the activation of hsβADR1. abstract: Dilated cardiomyopathy is a disease of left ventricular dysfunction accompanied by impairment of the β(1)-adrenergic receptor (β(1)-AR) signal cascade. The disturbed β(1)-AR function may be based on an elevated sympathetic tone observed in patients with heart failure. Prolonged adrenergic stimulation may induce metabolic and electrophysiological disturbances in the myocardium, resulting in tachyarrhythmia that leads to the development of heart failure in human and sudden death. Hence, β(1)-AR is considered as a promising drug target but attempts to develop effective and specific drug against this tempting pharmaceutical target is slowed down due to the lack of 3D structure of Homo sapiens β(1)-AR (hsβADR1). This study encompasses elucidation of 3D structural and physicochemical properties of hsβADR1 via threading-based homology modeling. Furthermore, the docking performance of several docking programs including Surflex-Dock, FRED, and GOLD were validated by re-docking and cross-docking experiments. GOLD and Surflex-Dock performed best in re-docking and cross docking experiments, respectively. Consequently, Surflex-Dock was used to predict the binding modes of four hsβADR1 agonists. This study provides clear understanding of hsβADR1 structure and its binding mechanism, thus help in providing the remedial solutions of cardiovascular, effective treatment of asthma and other diseases caused by malfunctioning of the target protein. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393300/ doi: 10.1371/journal.pone.0122223 id: cord-290952-tbsccwgx author: Ullah, Saif title: Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study date: 2020-07-03 words: 6464.0 sentences: 357.0 pages: flesch: 51.0 cache: ./cache/cord-290952-tbsccwgx.txt txt: ./txt/cord-290952-tbsccwgx.txt summary: In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. abstract: Coronavirus disease (COVID-19) is the biggest public health challenge the world is facing in recent days. Since there is no effective vaccine and treatment for this virus, therefore, the only way to mitigate this infection is the implementation of non-pharmaceutical interventions such as social-distancing, community lockdown, quarantine, hospitalization or self-isolation and contact-tracing. In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. Initially, a mathematical model without optimal control is formulated and some of the basic necessary analysis of the model, including stability results of the disease-free equilibrium is presented. It is found that the model is stable around the disease-free equilibrium both locally and globally when the basic reproduction number is less than unity. Despite the basic analysis of the model, we further consider the confirmed infected COVID-19 cases documented in Pakistan from March 1 till May 28, 2020 and estimate the model parameters using the least square fitting tools from statistics and probability theory. The results show that the model output is in good agreement with the reported COVID-19 infected cases. The approximate value of the basic reproductive number based on the estimated parameters is [Formula: see text]. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. It is observed that the most effective strategy to minimize the disease burden is the implementation of maintaining a strict social-distancing and contact-tracing to quarantine the exposed people. Furthermore, we carried out the global sensitivity analysis of the most crucial parameter known as the basic reproduction number using the Latin Hypercube Sampling (LHS) and the partial rank correlation coefficient (PRCC) techniques. The proposed model is then reformulated by adding the time-dependent control variables u(1)(t) for quarantine and u(2)(t) for the hospitalization interventions and present the necessary optimality conditions using the optimal control theory and Pontryagin’s maximum principle. Finally, the impact of constant and optimal control interventions on infected individuals is compared graphically. url: https://api.elsevier.com/content/article/pii/S0960077920304720 doi: 10.1016/j.chaos.2020.110075 id: cord-151871-228t4ymc author: Unceta, Irene title: Differential Replication in Machine Learning date: 2020-07-15 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates. To survive in such an ecosystem, machine learning models need to adapt to new conditions by evolving over time. The idea of model adaptability has been studied from different perspectives. In this paper, we propose a solution based on reusing the knowledge acquired by the already deployed machine learning models and leveraging it to train future generations. This is the idea behind differential replication of machine learning models. url: https://arxiv.org/pdf/2007.07981v1.pdf doi: nan id: cord-000759-36dhfptw author: Uribe-Sánchez, Andrés title: Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks date: 2011-06-05 words: 6945.0 sentences: 385.0 pages: flesch: 43.0 cache: ./cache/cord-000759-36dhfptw.txt txt: ./txt/cord-000759-36dhfptw.txt summary: The existing models on pandemic influenza (PI) containment and mitigation aims to address various complex aspects of the pandemic evolution process including: (i) the mechanism of disease progression, from the initial contact and infection transmission to the asymptomatic phase, manifestation of symptoms, and the final health outcome [10] [11] [12] , (ii) the population dynamics, including individual susceptibility [13, 14] and transmissibility [10, [15] [16] [17] , and behavioral factors affecting infection generation and effectiveness of interventions [18] [19] [20] , (iii) the impact of pharmaceutical and nonpharmaceutical measures, including vaccination [21] [22] [23] , antiviral therapy [24] [25] [26] , social distancing [27] [28] [29] [30] [31] and travel restrictions, and the use of low-cost measures, such as face masks and hand washing [26, [32] [33] [34] . The single-region model subsumes the following components (see Figure 3 ): (i) population dynamics (mixing groups and schedules), (ii) contact and infection process, (iii) disease natural history, and (iv) mitigation strategies, including social distancing, vaccination, and antiviral application. abstract: As recently pointed out by the Institute of Medicine, the existing pandemic mitigation models lack the dynamic decision support capability. We develop a large-scale simulation-driven optimization model for generating dynamic predictive distribution of vaccines and antivirals over a network of regional pandemic outbreaks. The model incorporates measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The performance of the strategy is compared to that of the reactive myopic policy, using a sample outbreak in Fla, USA, with an affected population of over four millions. The comparison is implemented at different levels of vaccine and antiviral availability and administration capacity. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The model is intended to support public health policy making for effective distribution of limited mitigation resources. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3447295/ doi: 10.1155/2011/579597 id: cord-026949-nu46ok9w author: Varshney, Deeksha title: Natural Language Generation Using Transformer Network in an Open-Domain Setting date: 2020-05-26 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Prior works on dialog generation focus on task-oriented setting and utilize multi-turn conversational utterance-response pairs. However, natural language generation (NLG) in the open-domain environment is more challenging. The conversations in an open-domain chit-chat model are mostly single-turn in nature. Current methods used for modeling single-turn conversations often fail to generate contextually relevant responses for a large dataset. In our work, we develop a transformer-based method for natural language generation (NLG) in an open-domain setting. Experiments on the utterance-response pairs show improvement over the baselines, both in terms of quantitative measures like BLEU and ROUGE and human evaluation metrics like fluency and adequacy. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298179/ doi: 10.1007/978-3-030-51310-8_8 id: cord-027438-ovhzult0 author: Veen, Lourens E. title: Easing Multiscale Model Design and Coupling with MUSCLE 3 date: 2020-05-25 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Multiscale modelling and simulation typically entails coupling multiple simulation codes into a single program. Doing this in an ad-hoc fashion tends to result in a tightly coupled, difficult-to-change computer program. This makes it difficult to experiment with different submodels, or to implement advanced techniques such as surrogate modelling. Furthermore, building the coupling itself is time-consuming. The MUltiScale Coupling Library and Environment version 3 (MUSCLE 3) aims to alleviate these problems. It allows the coupling to be specified in a simple configuration file, which specifies the components of the simulation and how they should be connected together. At runtime a simulation manager takes care of coordination of submodels, while data is exchanged over the network in a peer-to-peer fashion via the MUSCLE library. Submodels need to be linked to this library, but this is minimally invasive and restructuring simulation codes is usually not needed. Once operational, the model may be rewired or augmented by changing the configuration, without further changes to the submodels. MUSCLE 3 is developed openly on GitHub, and is available as Open Source software under the Apache 2.0 license. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304760/ doi: 10.1007/978-3-030-50433-5_33 id: cord-025404-rk2fuovf author: Venero, Sheila Katherine title: Automated Planning for Supporting Knowledge-Intensive Processes date: 2020-05-05 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Knowledge-intensive Processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain which reveals the feasibility of our approach. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254556/ doi: 10.1007/978-3-030-49418-6_7 id: cord-020764-5tq9cr7o author: Vertrees, Roger A. title: Tissue Culture Models date: 2010-05-21 words: 11293.0 sentences: 580.0 pages: flesch: 39.0 cache: ./cache/cord-020764-5tq9cr7o.txt txt: ./txt/cord-020764-5tq9cr7o.txt summary: Scientists have developed diverse and unique tissue culture systems that contain air-liquid barriers of lung epithelium and subjected these cells to various gaseous toxicants to determine what occurs following inhalation of various chemicals. In addition to the characterization of responses to inhaled agents, epithelial cell cultures, notably alveolar epithelium obtained from fetal lung tissue, have allowed investigators to characterize the liquid transport phenotype that occurs in the developing lung. Primary cell cultures of human airway smooth muscle tissue can be obtained utilizing a method described by Halayko et al. Additionally, if investigators do not wish to use currently established lung cancer cell lines, obtaining clinical samples for use in tissue culture models is relatively easy. This model is composed of a coculture of in vitro threedimensional human bronchoepithelial TLAs engineered using a rotating-wall vessel to mimic the characteristics of in vivo tissue and to provide a tool to study human respiratory viruses and host-pathogen cell interactions. abstract: The use of tissue cultures as a research tool to investigate the pathophysiologic bases of diseases has become essential in the current age of molecular biomedical research. Although it will always be necessary to translate and validate the observations seen in vitro to the patient or animal, the ability to investigate the role(s) of individual variables free from confounders is paramount toward increasing our understanding of the physiology of the lung and the role of its cellular components in disease. Additionally, it is not feasible to conduct certain research in humans because of ethical constraints, yet investigators may still be interested in the physiologic response in human tissues; in vitro characterization of human tissue is an acceptable choice. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147448/ doi: 10.1007/978-0-387-72430-0_15 id: cord-162772-5jgqgoet author: Viguerie, Alex title: Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion date: 2020-05-11 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources. url: https://arxiv.org/pdf/2005.05320v1.pdf doi: nan id: cord-337915-usi3crfl author: Vo, Khuong title: An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram date: 2020-07-04 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F(1) score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method. url: https://doi.org/10.3390/s20133757 doi: 10.3390/s20133757 id: cord-248050-apjwnwky author: Vrugt, Michael te title: Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model date: 2020-03-31 words: 5112.0 sentences: 331.0 pages: flesch: 53.0 cache: ./cache/cord-248050-apjwnwky.txt txt: ./txt/cord-248050-apjwnwky.txt summary: title: Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model We present an extended model for disease spread based on combining an SIR model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. In this article, we present a dynamical density functional theory (DDFT) [18] [19] [20] [21] for epidemic spreading that allows to model the effect of social distancing and isolation on infection numbers. While DDFT is not an exact theory (it is based on the assumption that the density is the only slow variable in the system [50, 51] ), it is nevertheless a significant improvement compared to the standard diffusion equation as it allows to incor-porate the effects of particle interactions and generally shows excellent agreement with microscopic simulations. abstract: For preventing the spread of epidemics such as the coronavirus disease COVID-19, social distancing and the isolation of infected persons are crucial. However, existing reaction-diffusion equations for epidemic spreading are incapable of describing these effects. We present an extended model for disease spread based on combining an SIR model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. The model shows interesting nonequilibrium phase separation associated with a reduction of the number of infections, and allows for new insights into the control of pandemics. url: https://arxiv.org/pdf/2003.13967v2.pdf doi: nan id: cord-158494-dww63e9f author: Wakefield, Jon title: Small Area Estimation of Health Outcomes date: 2020-06-18 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed. url: https://arxiv.org/pdf/2006.10266v1.pdf doi: nan id: cord-320666-cmqj8get author: Walach, H. title: What association do political interventions, environmental and health variables have with the number of Covid-19 cases and deaths? A linear modeling approach date: 2020-06-22 words: 7117.0 sentences: 444.0 pages: flesch: 56.0 cache: ./cache/cord-320666-cmqj8get.txt txt: ./txt/cord-320666-cmqj8get.txt summary: Results: We fitted two models with log-linearly linked variables on gamma-distributed outome variables (CoV2 cases and Covid-19 related deaths, standardized on population). Population standardized cases were best predicted by number of tests, life-expectancy in a country, and border closure (negative predictor, i.e. preventive). Population standardized deaths were best predicted by time, the virus had been in the country, life expectancy, smoking (negative predictor, i.e. preventive), and school closures (positive predictor, i.e. accelerating). The model predicting Covid-19 related deaths is presented in Table 3 : Here the duration the infection had been in the country is a significant positive predictor, and so is life expectancy. The major findings of this modeling study using population data for 40 countries are clear, if surprising: Life-expectancy emerges as a stable positive predictor both for standardized cases of CoV2 infections, as well as for Covid-19 related deaths. abstract: Background: It is unclear which variables contribute to the variance in Covid-19 related deaths and Covid-19 cases. Method: We modelled the relationship of various predictors (health systems variables, population and population health indicators) together with variables indicating public health measures (school closures, border closures, country lockdown) in 40 European and other countries, using Generalized Linear Models and minimized information criteria to select the best fitting and most parsimonious models. Results: We fitted two models with log-linearly linked variables on gamma-distributed outome variables (CoV2 cases and Covid-19 related deaths, standardized on population). Population standardized cases were best predicted by number of tests, life-expectancy in a country, and border closure (negative predictor, i.e. preventive). Population standardized deaths were best predicted by time, the virus had been in the country, life expectancy, smoking (negative predictor, i.e. preventive), and school closures (positive predictor, i.e. accelerating). Model fit statistics and model adequacy were good. Discussion and Interpretation: Interestingly, none of the variables that code for the preparedness of the medical system, for health status or other population parameters were predictive. Of the public health variables only border closure had the potential of preventing cases and none were predictors for preventing deaths. School closures, likely as a proxy for social distancing, was associated with increased deaths. Conclusion: The pandemic seems to run its autonomous course and only border closure has the potential to prevent cases. None of the contributes to preventing deaths. url: http://medrxiv.org/cgi/content/short/2020.06.18.20135012v1?rss=1 doi: 10.1101/2020.06.18.20135012 id: cord-254729-hoa39sx2 author: Wan, Wai-Yin title: Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions date: 2011-01-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two heavy-tailed distributions namely Student’s [Formula: see text] and exponential power distributions with different tailednesses and kurtoses are used and they are represented in scale mixture of normal and scale mixture of uniform respectively. The proposed model is capable of describing the trend and meanwhile the mixing parameters in the scale mixture representations can detect the outlying observations. Simulations and real data analysis are performed to investigate the properties of the models. url: https://doi.org/10.1016/j.csda.2010.06.011 doi: 10.1016/j.csda.2010.06.011 id: cord-321852-e7369brf author: Wang, Bo title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system date: 2020-11-10 words: 6468.0 sentences: 373.0 pages: flesch: 50.0 cache: ./cache/cord-321852-e7369brf.txt txt: ./txt/cord-321852-e7369brf.txt summary: In this paper, we introduce a automatically AI system that can provide the probability of infection and the ranked IDs. Specifically, the proposed system which consists of classification and segmentation will save about 30-40% of the detection time for physicians and promote the performance of COVID-19 detection. Using the dataset, we train and evaluate several deep learning based models to detect and segment the COVID-19 regions. [34] also build a U-Net based segmentation model to separate lung lesions and extract the radiologic characteristics in order to predict the hospital stay of a patient. [42] develop three widelyused models, i.e., ResNet-50 [43] , Inception-V3 [44] , and Inception-ResNet-V2 [45] , to detect COVID-19 lesion in X-ray images and among them ResNet-50 achieves the best classification performance. The positive data for the segmentation models were those images with arbitrary lung lesion regions, regardless of whether the lesions were COVID-19 or not. abstract: The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. url: https://api.elsevier.com/content/article/pii/S1568494620308358 doi: 10.1016/j.asoc.2020.106897 id: cord-278693-r55g26qw author: Wang, Lianwen title: New global dynamical results and application of several SVEIS epidemic models with temporary immunity date: 2021-02-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: This work applies a novel geometric criterion for global stability of nonlinear autonomous differential equations generalized by Lu and Lu (2017) to establish global threshold dynamics for several SVEIS epidemic models with temporary immunity, incorporating saturated incidence and nonmonotone incidence with psychological effect, and an SVEIS model with saturated incidence and partial temporary immunity. Incidentally, global stability for the SVEIS models with saturated incidence in Cai and Li (2009), Sahu and Dhar (2012) is completely solved. Furthermore, employing the DEDiscover simulation tool, the parameters in Sahu and Dhar’model are estimated with the 2009–2010 pandemic H1N1 case data in Hong Kong China, and it is validated that the vaccination programme indeed avoided subsequent potential outbreak waves of the pandemic. Finally, global sensitivity analysis reveals that multiple control measures should be utilized jointly to cut down the peak of the waves dramatically and delay the arrival of the second wave, thereinto timely vaccination is particularly effective. url: https://doi.org/10.1016/j.amc.2020.125648 doi: 10.1016/j.amc.2020.125648 id: cord-017423-cxua1o5t author: Wang, Rui title: A Review of Microblogging Marketing Based on the Complex Network Theory date: 2011-11-12 words: 2682.0 sentences: 121.0 pages: flesch: 36.0 cache: ./cache/cord-017423-cxua1o5t.txt txt: ./txt/cord-017423-cxua1o5t.txt summary: Microblogging marketing which is based on the online social network with both small-world and scale-free properties can be explained by the complex network theory. In brief, the complex network theory pioneered by the small-world and scalefree network model overcomes the constraints of the network size and structure of regular network and random network, describes the basic structural features of high clustering coefficient, short average path length, power-law degree distribution, and scale-free characteristics. Generally speaking, microblog has characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, and node degree distribution of positive and negative correlation. The complex network characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, node degree distribution of positive and negative correlation and its application in various industries provide theoretical and practical methods to conduct and implement microblogging marketing. abstract: Microblogging marketing which is based on the online social network with both small-world and scale-free properties can be explained by the complex network theory. Through systematically looking back at the complex network theory in different development stages, this chapter reviews literature from the microblogging marketing angle, then, extracts the analytical method and operational guide of microblogging marketing, finds the differences between microblog and other social network, and points out what the complex network theory cannot explain. In short, it provides a theoretical basis to effectively analyze microblogging marketing by the complex network theory. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121981/ doi: 10.1007/978-1-4419-8849-2_134 id: cord-246317-wz7epr3n author: Wang, Wei-Yao title: EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods date: 2020-07-05 words: 3270.0 sentences: 229.0 pages: flesch: 61.0 cache: ./cache/cord-246317-wz7epr3n.txt txt: ./txt/cord-246317-wz7epr3n.txt summary: We preprocess original tweet data to pre-trained language model, then fine-tune to multi-label classification model. Our study can be mainly divided into three topics, including multi-label classification, pre-trained models, and ensemble methods. Also, deep learning models are introduced to solve the multi-label classification problem, and have been proved that such models are able to extract high-level features from raw data. Secondly, a strength pre-trained language model can generate deep contextual word representation which means a word token can have several representation in different sentences. (2) Our goal aims to get better performance instead of efficiency, we use RoBERTa-base, BERT-basecased, and BERT-base-uncased to individually train language model and fine-tune to multi-label classification model. Since RoBERTa and BERT use different input formats, and our dataset has pair of sequences text and reply in each tweet, we convert input sentences based on corresponding models. abstract: This paper provides a method to classify sentiment with robust model based ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To reduce domain bias, we first train tweet dataset for pre-trained language model. Besides, each classifier has its strengths and weakness, we leverage different types of models with ensemble methods: average and power weighted sum. From the experiments, we show that our approach has achieved positive effect for sentiment classification. Our system reached third place among 26 teams from the evaluation in SocialNLP 2020 EmotionGIF competition. url: https://arxiv.org/pdf/2007.02259v1.pdf doi: nan id: cord-030683-xe9bn1cc author: Wang, Wenxi title: A Study of Symmetry Breaking Predicates and Model Counting date: 2020-03-13 words: 6667.0 sentences: 345.0 pages: flesch: 55.0 cache: ./cache/cord-030683-xe9bn1cc.txt txt: ./txt/cord-030683-xe9bn1cc.txt summary: We study the use of CNF-level and domain-level symmetry breaking predicates in the context of the state-of-the-art in model counting, specifically the leading approximate model counter ApproxMC and the recently introduced exact model counter ProjMC. Domain-specific predicates are particularly useful, and in many cases can provide full symmetry breaking to enable highly efficient model counting up to isomorphism. The other option is to ensure the formula that is input to the model counter includes symmetry breaking predicates [20, 21] , i.e., additional constraints that only allow canonical solutions from each isomorphism class, so the model counter can report the desired count. A key lesson of our study (in the context of the model counting problems considered) is: if non-isomorphic solution counts are desired, use full symmetry breaking predicates at the domain-level whenever feasible -even if it is straightforward to compute the number of non-isomorphic solutions from the number of all solutions, or even if the symmetry breaking constraints have to be written manually. abstract: Propositional model counting is a classic problem that has recently witnessed many technical advances and novel applications. While the basic model counting problem requires computing the number of all solutions to the given formula, in some important application scenarios, the desired count is not of all solutions, but instead, of all unique solutions up to isomorphism. In such a scenario, the user herself must try to either use the full count that the model counter returns to compute the count up to isomorphism, or ensure that the input formula to the model counter adequately captures the symmetry breaking predicates so it can directly report the count she desires. We study the use of CNF-level and domain-level symmetry breaking predicates in the context of the state-of-the-art in model counting, specifically the leading approximate model counter ApproxMC and the recently introduced exact model counter ProjMC. As benchmarks, we use a range of problems, including structurally complex specifications of software systems and constraint satisfaction problems. The results show that while it is sometimes feasible to compute the model counts up to isomorphism using the full counts that are computed by the model counters, doing so suffers from poor scalability. The addition of symmetry breaking predicates substantially assists model counters. Domain-specific predicates are particularly useful, and in many cases can provide full symmetry breaking to enable highly efficient model counting up to isomorphism. We hope our study motivates new research on designing model counters that directly account for symmetries to facilitate further applications of model counting. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439727/ doi: 10.1007/978-3-030-45190-5_7 id: cord-330978-f3uednt5 author: Wang, Yi title: Effect of time delay on pattern dynamics in a spatial epidemic model date: 2014-10-15 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Time delay, accounting for constant incubation period or sojourn times in an infective state, widely exists in most biological systems like epidemiological models. However, the effect of time delay on spatial epidemic models is not well understood. In this paper, spatial pattern of an epidemic model with both nonlinear incidence rate and time delay is investigated. In particular, we mainly focus on the effect of time delay on the formation of spatial pattern. Through mathematical analysis, we gain the conditions for Hopf bifurcation and Turing bifurcation, and find exact Turing space in parameter space. Furthermore, numerical results show that time delay has a significant effect on pattern formation. The simulation results may enrich the finding of patterns and may well capture some key features in the epidemic models. url: https://doi.org/10.1016/j.physa.2014.06.038 doi: 10.1016/j.physa.2014.06.038 id: cord-344417-1seb8b09 author: Wang, Yuhang title: SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection date: 2020-10-03 words: 8230.0 sentences: 540.0 pages: flesch: 57.0 cache: ./cache/cord-344417-1seb8b09.txt txt: ./txt/cord-344417-1seb8b09.txt summary: In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. To obtain enhanced text representations for fake news detection, we especially take into account the content structure-both global semantic relationship and local sequential order among sentences in a news document. Finally, we feed the enhanced sentence representations into the LSTM-based network sequentially, and obtain the informative document representation by max-pooling, which is further used for fake news detection. RQ3 What is the effect of LSTM, which is used to model the global sequential order information in the process of learning entire document-level representations for improving the fake news detection performance? abstract: The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagatorsâô profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods. url: https://www.sciencedirect.com/science/article/pii/S0957417420308460?v=s5 doi: 10.1016/j.eswa.2020.114090 id: cord-288342-i37v602u author: Wang, Zhen title: Coupled disease–behavior dynamics on complex networks: A review date: 2015-07-08 words: 15810.0 sentences: 776.0 pages: flesch: 38.0 cache: ./cache/cord-288342-i37v602u.txt txt: ./txt/cord-288342-i37v602u.txt summary: Incorporating adaptive behavior into a model of disease spread can provide important insight into population health outcomes, as the activation of social distancing and other nonpharmaceutical interventions (NPIs) have been observed to have the ability to alter the course of an epidemic [50] [51] [52] . The authors studied their coupled "disease-behavior" model in well-mixed populations, in square lattice populations, in random network populations, and in SF network populations, and found that population structure acts as a "double-edged sword" for public health: it can promote high levels of voluntary vaccination and herd immunity given that the cost for vaccination is not too large, but small increases in the cost beyond a certain threshold would cause vaccination to plummet, and infections to rise, more dramatically than in well-mixed populations. The first mathematical models studied the adaptive dynamics of disease-behavior responses in the homogeneously mixed population, assuming that individuals interact with each other at the same contact rate, without restrictions on selecting potential partners. abstract: It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. url: https://www.sciencedirect.com/science/article/pii/S1571064515001372 doi: 10.1016/j.plrev.2015.07.006 id: cord-022494-d66rz6dc author: Webb, B. title: Comparative Modeling of Drug Target Proteins date: 2014-10-01 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7157477/ doi: 10.1016/b978-0-12-409547-2.11133-3 id: cord-191876-03a757gf author: Weinert, Andrew title: Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment date: 2020-08-03 words: 3981.0 sentences: 223.0 pages: flesch: 54.0 cache: ./cache/cord-191876-03a757gf.txt txt: ./txt/cord-191876-03a757gf.txt summary: We''ve previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. In response, we previously identified and determined that the OpenSky Network [4] , a community network of ground-based sensors that observe aircraft equipped with Automatic Dependent Surveillance-Broadcast (ADS-B) out, would provide sufficient and appropriate data to develop new models [5] . Additionally to address that the four aircraft registries do not contain all registered aircraft globally, a second level directory titled "Unknown" was created and populated with directories corresponding to each hour of data. This hierarchy ensures that there are no more than 1000 directories per level, as recommended by the LLSC, while organizing the data to easily enable comparative analysis between years or different types of aircraft. abstract: As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain. url: https://arxiv.org/pdf/2008.00861v1.pdf doi: nan id: cord-290421-9v841ose author: Weston, Dale title: Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews date: 2020-10-01 words: 9917.0 sentences: 402.0 pages: flesch: 34.0 cache: ./cache/cord-290421-9v841ose.txt txt: ./txt/cord-290421-9v841ose.txt summary: The current paper presents a synthesis of review literature discussing the application of behaviour change theories within an infectious disease and emergency response context, with a view to informing infectious disease modelling, research and public health practice. Papers were included if they presented a review of theoretical models as applied to understanding preventative health behaviours in the context of emergency preparedness and response, and/or infectious disease outbreaks. Although this is based on key outcomes/ conclusions and not an exhaustive list of all successful theories reported within/ across reviews, the commonly applied behaviour change theories do seem to be identified as relevant for understanding and explaining human behaviour within an infectious disease and emergency response context. Based on these identified theories and our synthesis of review outcomes, and in conjunction with a recent review by Weston and colleagues [26] , we make recommendations to assist researchers, intervention designers, and mathematical modellers to incorporate psychological behaviour change theories within infectious disease and emergency response contexts. abstract: BACKGROUND: Behavioural science can play a critical role in combatting the effects of an infectious disease outbreak or public health emergency, such as the COVID-19 pandemic. The current paper presents a synthesis of review literature discussing the application of behaviour change theories within an infectious disease and emergency response context, with a view to informing infectious disease modelling, research and public health practice. METHODS: A scoping review procedure was adopted for the searches. Searches were run on PubMed, PsychInfo and Medline with search terms covering four major categories: behaviour, emergency response (e.g., infectious disease, preparedness, mass emergency), theoretical models, and reviews. Three further top-up reviews was also conducted using Google Scholar. Papers were included if they presented a review of theoretical models as applied to understanding preventative health behaviours in the context of emergency preparedness and response, and/or infectious disease outbreaks. RESULTS: Thirteen papers were included in the final synthesis. Across the reviews, several theories of behaviour change were identified as more commonly cited within this context, specifically, Health Belief Model, Theory of Planned Behaviour, and Protection Motivation Theory, with support (although not universal) for their effectiveness in this context. Furthermore, the application of these theories in previous primary research within this context was found to be patchy, and so further work is required to systematically incorporate and test behaviour change models within public health emergency research and interventions. CONCLUSION: Overall, this review identifies a range of more commonly applied theories with broad support for their use within an infectious disease and emergency response context. The Discussion section details several key recommendations to help researchers, practitioners, and infectious disease modellers to incorporate these theories into their work. Specifically, researchers and practitioners should base future research and practice on a systematic application of theories, beginning with those reported herein. Furthermore, infectious disease modellers should consult the theories reported herein to ensure that the full range of relevant constructs (cognitive, emotional and social) are incorporated into their models. In all cases, consultation with behavioural scientists throughout these processes is strongly recommended to ensure the appropriate application of theory. url: https://doi.org/10.1186/s12889-020-09519-2 doi: 10.1186/s12889-020-09519-2 id: cord-025843-5gpasqtr author: Wild, Karoline title: Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models date: 2020-05-09 words: 5032.0 sentences: 378.0 pages: flesch: 47.0 cache: ./cache/cord-025843-5gpasqtr.txt txt: ./txt/cord-025843-5gpasqtr.txt summary: title: Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models Although most of them are not limited to a specific infrastructure and able to manage multi-cloud applications, they all require a central orchestrator that processes the deployment model and executes all necessary tasks to deploy and orchestrate the application components on the respective infrastructure. We introduce a global declarative deployment model that describes a composite cross-organizational application, which is split to local parts for each participant. Based on the split declarative deployment models, workflows are generated which form the deployment choreography and coordinate the local deployment and cross-organizational data exchange. For the deployment execution we use an hybrid approach: Based on the LDM a local deployment workflow model is generated in step four that orchestrates the local deployment and cross-organizational information exchange activities. abstract: Various technologies have been developed to automate the deployment of applications. Although most of them are not limited to a specific infrastructure and able to manage multi-cloud applications, they all require a central orchestrator that processes the deployment model and executes all necessary tasks to deploy and orchestrate the application components on the respective infrastructure. However, there are applications in which several organizations, such as different departments or even different companies, participate. Due to security concerns, organizations typically do not expose their internal APIs to the outside or leave control over application deployments to others. As a result, centralized deployment technologies are not suitable to deploy cross-organizational applications. In this paper, we present a concept for the decentralized cross-organizational application deployment automation. We introduce a global declarative deployment model that describes a composite cross-organizational application, which is split to local parts for each participant. Based on the split declarative deployment models, workflows are generated which form the deployment choreography and coordinate the local deployment and cross-organizational data exchange. To validate the practical feasibility, we prototypical implemented a standard-based end-to-end toolchain for the proposed method using TOSCA and BPEL. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266461/ doi: 10.1007/978-3-030-49435-3_2 id: cord-230430-38fkbjq0 author: Williams, Tom title: Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures date: 2020-07-16 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as"Referring Expression Generation."As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the listener, and in part due to cognitive availability of those properties in working memory (WM). Because different theories of working memory"forgetting"necessarily lead to differences in cognitive availability, we hypothesize that they will similarly result in generation of different referring expressions. To design effective intelligent agents, it is thus necessary to determine how different models of forgetting may be differentially effective at producing natural human-like referring expressions. In this work, we computationalize two candidate models of working memory forgetting within a robot cognitive architecture, and demonstrate how they lead to cognitive availability-based differences in generated referring expressions. url: https://arxiv.org/pdf/2007.08672v1.pdf doi: nan id: cord-348010-m3a3utvz author: Wolff, Michael title: On build‐up of epidemiologic models—Development of a SEI(3)RSD model for the spread of SARS‐CoV‐2 date: 2020-10-13 words: 13018.0 sentences: 991.0 pages: flesch: 61.0 cache: ./cache/cord-348010-m3a3utvz.txt txt: ./txt/cord-348010-m3a3utvz.txt summary: (Adequate contacts, reproduction and contact numbers) (i) A contact is called adequate (also effective), if it leads to a transmission of the pathogen from an infectious person to another one, and, if the affected individual is susceptible, then an infection is provoked. In the case of concrete models one uses generally contact and replacement numbers, and , which reflect the current infection behaviour. (i) (Closed-population model) An assumed constant number of community members (see Remark 2.2) seems to be justified, if the infection spreads quickly, approximately within a year, and/or, if there is a balance between births, migration and non-disease-related deaths. (Using , there arise difficulties with the dot indicating the time derivation.) If the model is to be to take a latent period into account, the class of infected is divided into subclasses in the following way. abstract: The present study investigates essential steps in build‐up of models for description of the spread of infectious diseases. Combining these modules, a SEI(3)RSD model will be developed, which can take into account a possible passive immunisation by vaccination as well as different durations of latent and incubation periods. Besides, infectious persons with and without symptoms can be distinguished. Due to the current world‐wide SARS‐CoV‐2 pandemic (COVID‐19 pandemic) models for description of the spread of infectious diseases and their application for forecasts have become into the focus of the scientific community as well as of broad public more than usual. Currently, many papers and studies have appeared and appear dealing with the virus SARS‐CoV‐2 and the COVID‐19 disease caused by it. This occurs under medical, virological, economic, sociological and further aspects as well as under mathematical points of view. Concerning the last‐mentioned point, the main focus lies on the application of existing models and their adaptation to data about the course of infection available at the current time. Clearly, the aim is to predict the possible further development, for instance in Germany. It is of particular interest to investigate how will be the influence of political and administrative measures like contact restrictions, closing or rather re‐opening of schools, restaurants, hotels etc. on the course of infection. The steps considered here for building up suitable models are well‐known for long time. However, understandably they will not be dealt with in an extended way in current application‐oriented works. Therefore, it is the aim of this study to present some existing steps of modelling without any pretension of completeness. Thus, on the one hand we give assistance and, on the other hand, we develop a model capable to take already known properties of COVID‐19 as well as a later possible passive immunisation by vaccination and a possible loss of immunity of recovered persons into account. url: https://doi.org/10.1002/zamm.202000230 doi: 10.1002/zamm.202000230 id: cord-308302-5yns1hg9 author: Wu, Gang title: A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings date: 2020-08-20 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings. url: https://www.ncbi.nlm.nih.gov/pubmed/32820210/ doi: 10.1038/s41598-020-71114-7 id: cord-285897-ahysay2l author: Wu, Guangyao title: Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study date: 2020-07-02 words: 3803.0 sentences: 178.0 pages: flesch: 42.0 cache: ./cache/cord-285897-ahysay2l.txt txt: ./txt/cord-285897-ahysay2l.txt summary: OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Therefore, our objective is to develop and validate a prognostic machine-learning model based on clinical, laboratory, and radiological variables of COVID-19 patients at hospital admission for severity risk assessment during hospitalization, and compare the performance with that of PSI as a representative clinical assessment method. This international multicenter study analyzed individually and in combination, clinical, laboratory and radiological characteristics for COVID-19 patients at hospital admission, to retrospectively develop and prospectively validate a prognostic model and tool to assess the severity of the illness, and its progression, and to compare these with PSI scoring. abstract: BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion-matrix. RESULTS: The median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all of which performed better than the pneumonia severity index. The cut-off values of the low, medium, and high-risk probabilities were 0.21 and 0.80. The online-calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission. url: https://www.ncbi.nlm.nih.gov/pubmed/32616597/ doi: 10.1183/13993003.01104-2020 id: cord-333693-z2ni79al author: Wu, Lin title: Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion date: 2020-08-06 words: 723.0 sentences: 43.0 pages: flesch: 40.0 cache: ./cache/cord-333693-z2ni79al.txt txt: ./txt/cord-333693-z2ni79al.txt summary: title: Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. We addressed these issues by presenting an interactive individual-based simulator, which is capable of modeling an epidemic through multi-source information fusion. However, oversimplified models are not capable of incorporating multi-type uncertain information like clinical courses, viral shedding, subclinical transmission, infections, confirmations, deaths, or interventions, so they cannot reduce uncertainty by multi-source information fusion. To tackle the three challenges of modelling epidemic dynamics, we have developed an interactive simulator for individual-based models in this paper. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia Estimates of the severity of coronavirus disease 2019: a model-based analysis abstract: Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. However, there are three issues that make this modeling a challenging task: uncertainty in data, roughness in models, and complexity in programming. We addressed these issues by presenting an interactive individual-based simulator, which is capable of modeling an epidemic through multi-source information fusion. url: https://api.elsevier.com/content/article/pii/S2666675820300333 doi: 10.1016/j.xinn.2020.100033 id: cord-002169-7kwlteyr author: Wu, Nicholas C title: Adaptation in protein fitness landscapes is facilitated by indirect paths date: 2016-07-08 words: 9303.0 sentences: 504.0 pages: flesch: 54.0 cache: ./cache/cord-002169-7kwlteyr.txt txt: ./txt/cord-002169-7kwlteyr.txt summary: Previous empirical studies on combinatorially complete fitness landscapes have been limited to subgraphs of the sequence space consisting of only two amino acids at each site (2 L genotypes) (Weinreich et al., 2006; Lunzer et al., 2005; O''Maille et al., 2008; Lozovsky et al., 2009; Franke et al., 2011; Tan et al., 2011) . Our findings support the view that direct paths of protein adaptation are often constrained by pairwise epistasis on a rugged fitness landscape (Weinreich et al., 2005; Kondrashov and Kondrashov, 2015) . With our experimental data, we observed two distinct mechanisms of bypass, either using an extra amino acid at the same site or using an additional site, that allow proteins to continue adaptation when no direct paths were accessible due to reciprocal sign epistasis ( Figure 2 ). Our results suggest that higher-order epistasis can either increase or decrease the ruggedness induced by pairwise epistasis, which in turn determines the accessibility of direct paths in a rugged fitness landscape (Figure 3-figure supplement 6). abstract: The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20(L)) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 20(4) = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. DOI: http://dx.doi.org/10.7554/eLife.16965.001 url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4985287/ doi: 10.7554/elife.16965 id: cord-003377-9vkhptas author: Wu, Tong title: The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia date: 2018-12-19 words: 4969.0 sentences: 267.0 pages: flesch: 49.0 cache: ./cache/cord-003377-9vkhptas.txt txt: ./txt/cord-003377-9vkhptas.txt summary: title: The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia We focus on the role played by the live poultry trade in the spread of H5N1 across three regions widely infected by the disease, which also correspond to three major trade blocs: the European Union (EU), the Economic Community of West African States (ECOWAS), and the Association of Southeast Asian Nations (ASEAN). The indicator for wild bird habitat used in this study was the set of "Important Bird and Biodiversity Areas" (IBAs) for "migratory and congregatory waterbirds" identified by BirdLife The live poultry trade poses different avian influenza risks in different regions of the world Table 1 . Our first specification (Model 1) included a number of factors related to disease risk but excluded both live poultry imports and biosecurity measures. abstract: In the past two decades, avian influenzas have posed an increasing international threat to human and livestock health. In particular, highly pathogenic avian influenza H5N1 has spread across Asia, Africa, and Europe, leading to the deaths of millions of poultry and hundreds of people. The two main means of international spread are through migratory birds and the live poultry trade. We focus on the role played by the live poultry trade in the spread of H5N1 across three regions widely infected by the disease, which also correspond to three major trade blocs: the European Union (EU), the Economic Community of West African States (ECOWAS), and the Association of Southeast Asian Nations (ASEAN). Across all three regions, we found per-capita GDP (a proxy for modernization, general biosecurity, and value-at-risk) to be risk reducing. A more specific biosecurity measure–general surveillance–was also found to be mitigating at the all-regions level. However, there were important inter-regional differences. For the EU and ASEAN, intra-bloc live poultry imports were risk reducing while extra-bloc imports were risk increasing; for ECOWAS the reverse was true. This is likely due to the fact that while the EU and ASEAN have long-standing biosecurity standards and stringent enforcement (pursuant to the World Trade Organization’s Agreement on the Application of Sanitary and Phytosanitary Measures), ECOWAS suffered from a lack of uniform standards and lax enforcement. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300203/ doi: 10.1371/journal.pone.0208197 id: cord-293893-ibca88xu author: Xie, Tian title: Parallel Evolution and Response Decision Method for Public Sentiment based on System Dynamics date: 2020-05-23 words: 9806.0 sentences: 441.0 pages: flesch: 42.0 cache: ./cache/cord-293893-ibca88xu.txt txt: ./txt/cord-293893-ibca88xu.txt summary: This method is structure-dependent rather than data-dependent and can be implemented in real-time, which makes it helpful to simulate, analyze and guide the evolution processes of dynamic public sentiment in the case of lack of historical knowledge on less-frequently occurring original events. The rationality of the cultivated SD model and the consistency between its simulation results and the real evolution trends of the public sentiment are essential to achieve scenario rehearsal and response effectively in the decision-making processes (Thompson et al., 2016) . In a decision-making process for a non-duplicated public sentiment triggered by a major public health incident or a large-scale project, because the decision makers lack prior data and knowledge, the parameters of the initial equations of the 1-general SD model can be referenced from the developed models of historical cases which are similar with the current event in type, system structure and situation. abstract: Abstract Governments face difficulties in policy making in many areas such as health, food safety, and large-scale projects where public perceptions can be misplaced. For example, the adoption of the MMR vaccine has been opposed due to the publicity indicating an erroneous link between the vaccine and autism. This research proposes the “Parallel Evolution and Response Decision Framework for Public Sentiments” as a real-time decision-making method to simulate and control the public sentiment evolution mechanisms. This framework is based on the theories of Parallel Control and Management (PCM) and System Dynamics (SD) and includes four iterative steps: namely, SD modelling, simulating, optimizing, and controlling. A concrete case of an anti-nuclear mass incident that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. In addition, the results indicate the effects by adjusting the key control variables of response strategies. These variables include response time, response capacity, and transparency of the government regarding public sentiment. Furthermore, the advantages and disadvantages of the proposed method will be analyzed to determine how it can be used by policy makers in predicting public opinion and offering effective response strategies. url: https://www.ncbi.nlm.nih.gov/pubmed/32834432/ doi: 10.1016/j.ejor.2020.05.025 id: cord-035388-n9hza6vm author: Xu, Jie title: Federated Learning for Healthcare Informatics date: 2020-11-12 words: 6143.0 sentences: 352.0 pages: flesch: 43.0 cache: ./cache/cord-035388-n9hza6vm.txt txt: ./txt/cord-035388-n9hza6vm.txt summary: This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. For both provider (e.g., building a model for predicting the hospital readmission risk with patient Electronic Health Records (EHR) [71] ) and consumer (patient)-based applications (e.g., screening atrial fibrillation with electrocardiograms captured by smartwatch [79] ), the sensitive patient data can stay either in local institutions or with individual consumers without going out during the federated model learning process, which effectively protects the patient privacy. Federated learning is a problem of training a high-quality shared global model with a central server from decentralized data scattered among large number of different clients (Fig. 1) . abstract: With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659898/ doi: 10.1007/s41666-020-00082-4 id: cord-005033-voi9gu0l author: Xuan, Huiyu title: A CA-based epidemic model for HIV/AIDS transmission with heterogeneity date: 2008-06-07 words: 6567.0 sentences: 395.0 pages: flesch: 57.0 cache: ./cache/cord-005033-voi9gu0l.txt txt: ./txt/cord-005033-voi9gu0l.txt summary: In this paper, we develop an extended CA simulation model to study the dynamical behaviors of HIV/AIDS transmission. Additional, we divide the post-infection process of AIDS disease into several sub-stages in order to facilitate the study of the dynamics in different development stages of epidemics. Higher population density, higher mobility, higher number of infection source, and greater neighborhood are more likely to result in high levels of infections and in persistence. Ahmed and Agiza (1998) develop a CA model that takes into consideration the latency and incubation period of epidemics and allow each individual (agent) to have distinctive susceptibility. We also define four types of agents that are characterized by different infectivity (and susceptibility) and various forms of neighborhood to represent four types of people in real life. To capture this, we extend classical CA models by allowing each agent to have its own attributes such as mobility, infectivity, resistibility (susceptibility) 2 and different extent of neighborhood. abstract: The complex dynamics of HIV transmission and subsequent progression to AIDS make the mathematical analysis untraceable and problematic. In this paper, we develop an extended CA simulation model to study the dynamical behaviors of HIV/AIDS transmission. The model incorporates heterogeneity into agents’ behaviors. Agents have various attributes such as infectivity and susceptibility, varying degrees of influence on their neighbors and different mobilities. Additional, we divide the post-infection process of AIDS disease into several sub-stages in order to facilitate the study of the dynamics in different development stages of epidemics. These features make the dynamics more complicated. We find that the epidemic in our model can generally end up in one of the two states: extinction and persistence, which is consistent with other researchers’ work. Higher population density, higher mobility, higher number of infection source, and greater neighborhood are more likely to result in high levels of infections and in persistence. Finally, we show in four-class agent scenario, variation in susceptibility (or infectivity) and various fractions of four classes also complicates the dynamics, and some of the results are contradictory and needed for further research. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088085/ doi: 10.1007/s10479-008-0369-3 id: cord-160382-8n3s5j8w author: Yamagata, Yoriyuki title: Simultaneous estimation of the effective reproducing number and the detection rate of COVID-19 date: 2020-05-02 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: A major difficulty to estimate $R$ (the effective reproducing number) of COVID-19 is that most cases of COVID-19 infection are mild or asymptomatic, therefore true number of infection is difficult to determine. This paper estimates the daily change of $R$ and the detection rate simultaneously using a Bayesian model. The analysis using synthesized data shows that our model correctly estimates $R$ and detects a short-term shock of the detection rate. Then, we apply our model to data from several countries to evaluate the effectiveness of public healthcare measures. Our analysis focuses Japan, which employs a moderate measure to keep"social distance". The result indicates a downward trend and now $R$ becomes below $1$. Although our analysis is preliminary, this may suggest that a moderate policy still can prevent epidemic of COVID-19. url: https://arxiv.org/pdf/2005.02766v2.pdf doi: nan id: cord-250288-obsl0nbf author: Yan, Bingjie title: An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM date: 2020-05-05 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional epidemic, the epidemic can be controlled using the development policy. The common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compared the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. And this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data; 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage. url: https://arxiv.org/pdf/2005.03446v2.pdf doi: nan id: cord-337897-hkvll3xh author: Yang, Zheng Rong title: Peptide Bioinformatics- Peptide Classification Using Peptide Machines date: 2009 words: 7631.0 sentences: 495.0 pages: flesch: 54.0 cache: ./cache/cord-337897-hkvll3xh.txt txt: ./txt/cord-337897-hkvll3xh.txt summary: The earlier work was to investigate a set of experimentally determined (synthesized) functional peptides to find some conserved amino acids, referred In protease cleavage site prediction, we commonly use peptides with a fixed length. The bio-basis function method has been successfully applied to various peptide classification tasks, for instance, the prediction of trypsin cleavage sites [ 9 ] , the prediction of HIV cleavage sites [ 10 ] , the prediction of hepatitis C virus protease cleavage sites [ 16 ] , the prediction of the disorder segments in proteins [ 7 , 17 ] , the prediction of protein phosphorylation sites [ 18 , 19 ] , the prediction of the O-linkage sites in glycoproteins [ 20 ] , the prediction of signal peptides [ 21 ] , the prediction of factor Xa protease cleavage sites [ 22 ] , the analysis of mutation patterns of HIV-1 Fig. 9 . abstract: Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, such as functional site prediction, protein structure identification, and protein function recognition. In these applications, we normally need to assign a peptide to one of the given categories using a computer model. They are therefore referred to as peptide classification applications. Among various machine learning approaches, including neural networks, peptide machines have demonstrated excellent performance compared with various conventional machine learning approaches in many applications. This chapter discusses the basic concepts of peptide classification, commonly used feature extraction methods, three peptide machines, and some important issues in peptide classification. url: https://www.ncbi.nlm.nih.gov/pubmed/19065810/ doi: 10.1007/978-1-60327-101-1_9 id: cord-274209-n0aast22 author: Yaro, David title: Analysis and Optimal Control of Fractional-Order Transmission of a Respiratory Epidemic Model date: 2019-07-15 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: The World Health Organization is yet to realise the global aim of achieving future-free and eliminating the transmission of respiratory diseases such as H1N1, SARS and Ebola since the recent reemergence of Ebola in the Democratic Republic of Congo. In this paper, a Caputo fractional-order derivative is applied to a system of non-integer order differential equation to model the transmission dynamics of respiratory diseases. The nonnegative solutions of the system are obtained by using the Generalized Mean Value Theorem. The next generation matrix approach is used to obtain the basic reproduction number [Formula: see text] . We discuss the stability of the disease-free equilibrium when [Formula: see text] , and the necessary conditions for the stability of the endemic equilibrium when [Formula: see text] . A sensitivity analysis shows that [Formula: see text] is most sensitive to the probability of the disease transmission rate. The results from the numerical simulations of optimal control strategies disclose that the utmost way of controlling or probably eradicating the transmission of respiratory diseases should be quarantining the exposed individuals, monitoring and treating infected people for a substantial period. url: https://www.ncbi.nlm.nih.gov/pubmed/32289049/ doi: 10.1007/s40819-019-0699-7 id: cord-016954-l3b6n7ej author: Young, Colin R. title: Animal Models of Multiple Sclerosis date: 2008 words: 9705.0 sentences: 495.0 pages: flesch: 44.0 cache: ./cache/cord-016954-l3b6n7ej.txt txt: ./txt/cord-016954-l3b6n7ej.txt summary: The relative inaccessibility and sensitivity of the central nervous system (CNS) in humans preclude studies on disease pathogenesis, and so much of our understanding of infections and immune responses has been derived from experimental animal models. Viral models are immensely relevant since epidemiological studies suggest an environmental factor, and almost all naturally occurring CNS demyelinating diseases of humans and animals of known etiology are caused by a virus. The most widely studied models of MS are the experimental infections of rodents resulting in an inflammatory demyelinating disease in the CNS, such as Theiler''s virus, mouse hepatitis virus, and Semliki Forest virus. Theiler''s virus-induced demyelination, a model for human MS, bears several similarities to the human disease: an immune-mediated demyelination, involvement of CD4 + helper T cells and CD8 + cytotoxic T cells, delayed type hypersensitivity responses to viral antigens and autoantigens, and pathology. abstract: To determine whether an immunological or pharmaceutical product has potential for therapy in treating multiple sclerosis (MS), detailed animal models are required. To date many animal models for human MS have been described in mice, rats, rabbits, guinea pigs, marmosets, and rhesus monkeys. The most comprehensive studies have involved murine experimental allergic (or autoimmune) encephalomyelitis (EAE), Semliki Forest virus (SFV), mouse hepatitis virus (MHV), and Theiler’s murine encephalomyelitis virus (TMEV). Here, we describe in detail multispecies animal models of human MS, namely EAE, SFV, MHV, and TMEV, in addition to chemically induced demyelination. The validity and applicability of each of these models are critically evaluated. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121393/ doi: 10.1007/978-1-59745-285-4_69 id: cord-027318-hinho0mh author: Zak, Matthew title: Classification of Lung Diseases Using Deep Learning Models date: 2020-05-22 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classification tasks using transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is in smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset despite being computationally less expensive. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304013/ doi: 10.1007/978-3-030-50420-5_47 id: cord-205559-q50vog59 author: Zhang, Lelin title: Detecting Transaction-based Tax Evasion Activities on Social Media Platforms Using Multi-modal Deep Neural Networks date: 2020-07-27 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale. url: https://arxiv.org/pdf/2007.13525v1.pdf doi: nan id: cord-178783-894gkrsk author: Zhang, Rui title: Drug Repurposing for COVID-19 via Knowledge Graph Completion date: 2020-10-19 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. url: https://arxiv.org/pdf/2010.09600v1.pdf doi: nan id: cord-024552-hgowgq41 author: Zhang, Ruixi title: Hydrological Process Surrogate Modelling and Simulation with Neural Networks date: 2020-04-17 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Environmental sustainability is a major concern for urban and rural development. Actors and stakeholders need economic, effective and efficient simulations in order to predict and evaluate the impact of development on the environment and the constraints that the environment imposes on development. Numerical simulation models are usually computation expensive and require expert knowledge. We consider the problem of hydrological modelling and simulation. With a training set consisting of pairs of inputs and outputs from an off-the-shelves simulator, We show that a neural network can learn a surrogate model effectively and efficiently and thus can be used as a surrogate simulation model. Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206312/ doi: 10.1007/978-3-030-47436-2_34 id: cord-325862-rohhvq4h author: Zhang, Yong title: Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19 date: 2020-06-04 words: 5899.0 sentences: 259.0 pages: flesch: 47.0 cache: ./cache/cord-325862-rohhvq4h.txt txt: ./txt/cord-325862-rohhvq4h.txt summary: The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The main contributions of this work, therefore, include 1) the first application of FDEs in modeling the evolution of the COVID-19 death toll, 2) an updated SEIR model with a transient recovery rate to better capture the dynamics of COVID-19 pandemic within China and for other countries, and 3) a particle-tracking approach based on stochastic bimolecular reaction theory to evaluate the mitigation of the spread of the COVID-19 outbreak. abstract: Fractional calculus provides a promising tool for modeling fractional dynamics in computational biology, and this study tests the applicability of fractional-derivative equations (FDEs) for modeling the dynamics and mitigation scenarios of the novel coronavirus for the first time. The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the evolution dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed over the last three months (up to 3/22/2020) in China. The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The validated SEIR model was then applied to predict COVID-19 evolution in the United States, Italy, Japan, and South Korea. In addition, a time FDE model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the FDE model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread. Therefore, caution is needed when applying FDEs to model the coronavirus outbreak, since specific COVID-19 kinetics may not exhibit nonlocal behavior. Particularly, the spread of COVID-19 may be affected by the rapid improvement of health care systems which may remove the memory impact in COVID-19 dynamics (resulting in a short-tailed recovery curve), while the death toll and mitigation of COVID-19 can be captured by the time FDEs due to the nonlocal, memory impact in fatality and human activities. url: https://www.sciencedirect.com/science/article/pii/S0960077920303581?v=s5 doi: 10.1016/j.chaos.2020.109959 id: cord-347906-3ehsg8oi author: Zhang, Zizhen title: Dynamics of COVID-19 mathematical model with stochastic perturbation date: 2020-08-28 words: 1774.0 sentences: 176.0 pages: flesch: 58.0 cache: ./cache/cord-347906-3ehsg8oi.txt txt: ./txt/cord-347906-3ehsg8oi.txt summary: title: Dynamics of COVID-19 mathematical model with stochastic perturbation Thirdly, we examine the threshold of the proposed stochastic COVID-19 model, when noise is small or large. The same set of parameter values and initial conditions for deterministic models will lead to an ensemble of different outputs. They obtained the condition of the disease extinction and persistence according to noise and threshold of the deterministic system. Similarly, several authors discussed the same conditions for stochastic models; see [32] [33] [34] [35] [36] [37] [38] [39] . To study the effects of the environment on spreading of COVID-19 and make the research more realistic, first we formulate a stochastic mathematical COVID-19 model. In this section, a COVID-19 mathematical model with random perturbation is formulated as follows: The extinction and persistence of the stochastic SIS epidemic model with vaccination A stochastic differential equation SIS epidemic model abstract: Acknowledging many effects on humans, which are ignored in deterministic models for COVID-19, in this paper, we consider stochastic mathematical model for COVID-19. Firstly, the formulation of a stochastic susceptible–infected–recovered model is presented. Secondly, we devote with full strength our concentrated attention to sufficient conditions for extinction and persistence. Thirdly, we examine the threshold of the proposed stochastic COVID-19 model, when noise is small or large. Finally, we show the numerical simulations graphically using MATLAB. url: https://doi.org/10.1186/s13662-020-02909-1 doi: 10.1186/s13662-020-02909-1 id: cord-132843-ilxt4b6g author: Zhao, Liang title: Event Prediction in the Big Data Era: A Systematic Survey date: 2020-07-19 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed. url: https://arxiv.org/pdf/2007.09815v3.pdf doi: nan id: cord-004332-99lxmq4u author: Zhao, Shi title: Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall date: 2020-01-10 words: 4195.0 sentences: 218.0 pages: flesch: 53.0 cache: ./cache/cord-004332-99lxmq4u.txt txt: ./txt/cord-004332-99lxmq4u.txt summary: title: Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall This work aims to study the epidemiological features of epidemics in different Nigerian regions and quantify the association between reproduction number (R) and state rainfall. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points (τ) in different regions at different time periods. As illustrated in Figure 3 , the reproduction numbers, R j s, are estimated for different epidemics from the selected growth models. To quantify the impacts of state rainfall, we calculate the percentage changing rate with different cumulative lags (t) from 4 to 9 months and estimate their significant levels. The estimated changing rate in R under a one-unit (mm) increase in the average monthly rainfall is summarised with different cumulative lag terms from 4 to 9 months (the t in Eqn (3)). abstract: Lassa fever (LF) is increasingly recognised as an important rodent-borne viral haemorrhagic fever presenting a severe public health threat to sub-Saharan West Africa. In 2017–18, LF caused an unprecedented epidemic in Nigeria and the situation was worsening in 2018–19. This work aims to study the epidemiological features of epidemics in different Nigerian regions and quantify the association between reproduction number (R) and state rainfall. We quantify the infectivity of LF by the reproduction numbers estimated from four different growth models: the Richards, three-parameter logistic, Gompertz and Weibull growth models. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points (τ) in different regions at different time periods. Cochran's Q test is further applied to test the spatial heterogeneity of the LF epidemics. A linear random-effect regression model is adopted to quantify the association between R and state rainfall with various lag terms. Our estimated Rs for 2017–18 (1.33 with 95% CI 1.29–1.37) was significantly higher than those for 2016–17 (1.23 with 95% CI: (1.22, 1.24)) and 2018–19 (ranged from 1.08 to 1.36). We report spatial heterogeneity in the Rs for epidemics in different Nigerian regions. We find that a one-unit (mm) increase in average monthly rainfall over the past 7 months could cause a 0.62% (95% CI 0.20%–1.05%)) rise in R. There is significant spatial heterogeneity in the LF epidemics in different Nigerian regions. We report clear evidence of rainfall impacts on LF epidemics in Nigeria and quantify the impact. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019145/ doi: 10.1017/s0950268819002267 id: cord-343701-x5rghsbs author: Zhao, Yu-Feng title: Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models date: 2020-06-25 words: 5030.0 sentences: 241.0 pages: flesch: 53.0 cache: ./cache/cord-343701-x5rghsbs.txt txt: ./txt/cord-343701-x5rghsbs.txt summary: Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. On this basis, a rolling grey Verhulst model and its derived models were established to predict the change trend of the number of cases of COVID-19 infection in China. Based on a rolling mechanism, the rolling grey Verhulst model and its derived models for predicting the number of patients infected with COVID-19 in China were constructed by adding the latest data and removing the earliest data. The results showed that the rolling grey Verhulst model and its derived models could accurately predict the changes in the number of confirmed patients in China. abstract: The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780. url: https://www.ncbi.nlm.nih.gov/pubmed/32630565/ doi: 10.3390/ijerph17124582 id: cord-024515-iioqkydg author: Zhong, Qi title: Protecting IP of Deep Neural Networks with Watermarking: A New Label Helps date: 2020-04-17 words: 4588.0 sentences: 233.0 pages: flesch: 57.0 cache: ./cache/cord-024515-iioqkydg.txt txt: ./txt/cord-024515-iioqkydg.txt summary: To mitigate this threat, in this paper, we propose an innovative framework to protect the intellectual property of deep learning models, that is, watermarking the model by adding a new label to crafted key samples during training. The intuition comes from the fact that, compared with existing DNN watermarking methods, adding a new label will not twist the original decision boundary but can help the model learn the features of key samples better. Extensive experimental results show that, compared with the existing schemes, the proposed method performs better under small perturbation strength or short key samples'' length in terms of classification accuracy and ownership verification efficiency. -Effectiveness and efficiency: the false positive rate for key samples should be minimized, and a reliable ownership verification result needs to be obtained with few queries to the remote DNN API; -Robustness: the watermarked model can resist several known attacks, for example, pruning attack and fine-tuning attack. abstract: Deep neural network (DNN) models have shown great success in almost every artificial area. It is a non-trivial task to build a good DNN model. Nowadays, various MLaaS providers have launched their cloud services, which trains DNN models for users. Once they are released, driven by potential monetary profit, the models may be duplicated, resold, or redistributed by adversaries, including greedy service providers themselves. To mitigate this threat, in this paper, we propose an innovative framework to protect the intellectual property of deep learning models, that is, watermarking the model by adding a new label to crafted key samples during training. The intuition comes from the fact that, compared with existing DNN watermarking methods, adding a new label will not twist the original decision boundary but can help the model learn the features of key samples better. We implement a prototype of our framework and evaluate the performance under three different benchmark datasets, and investigate the relationship between model accuracy, perturbation strength, and key samples’ length. Extensive experimental results show that, compared with the existing schemes, the proposed method performs better under small perturbation strength or short key samples’ length in terms of classification accuracy and ownership verification efficiency. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206275/ doi: 10.1007/978-3-030-47436-2_35 id: cord-193856-6vs16mq3 author: Zhou, Tongxin title: Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach date: 2020-09-13 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Online healthcare communities provide users with various healthcare interventions to promote healthy behavior and improve adherence. When faced with too many intervention choices, however, individuals may find it difficult to decide which option to take, especially when they lack the experience or knowledge to evaluate different options. The choice overload issue may negatively affect users' engagement in health management. In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions. Taking into account that users' health behaviors can be highly dynamic and diverse, we propose a multi-armed bandit (MAB)-driven recommendation framework, which enables us to adaptively learn users' preference variations while promoting recommendation diversity in the meantime. To better adapt an MAB to the healthcare context, we synthesize two innovative model components based on prominent health theories. The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. The second component is a diversity constraint, which structurally diversifies recommendations in different dimensions to provide users with well-rounded support. We apply our approach to an online weight management context and evaluate it rigorously through a series of experiments. Our results demonstrate that each of the design components is effective and that our recommendation design outperforms a wide range of state-of-the-art recommendation systems. Our study contributes to the research on the application of business intelligence and has implications for multiple stakeholders, including online healthcare platforms, policymakers, and users. url: https://arxiv.org/pdf/2009.06108v1.pdf doi: nan id: cord-121200-2qys8j4u author: Zogan, Hamad title: Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media date: 2020-07-03 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. url: https://arxiv.org/pdf/2007.02847v1.pdf doi: nan id: cord-312911-nqq87d0m author: Zou, D. title: Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States date: 2020-05-25 words: 5032.0 sentences: 283.0 pages: flesch: 62.0 cache: ./cache/cord-312911-nqq87d0m.txt txt: ./txt/cord-312911-nqq87d0m.txt summary: We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (R0). Based on the proposed model, we are able to make accurate predictions on the numbers of confirmed cases and fatality cases for nation, states and and counties. Moreover, our model can also predict the peak dates of active cases and estimate the basic reproduction number (R 0 ) of different states in the US. abstract: We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (R0). In particular, the forecasts based on our model suggest that the peak date of the US, New York state, and California state are 06/01/2020, 05/10/2020, and 07/01/2020 respectively. In addition, the estimated R0 of the US, New York state, and California state are 2.5, 3.6 and 2.2 respectively. The prediction results for all states in the US can be found on our project website: https://covid19.uclaml.org, which are updated on a weekly basis, and have been adopted by the Centers for Disease Control and Prevention (CDC) for COVID-19 death forecasts (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html). url: https://doi.org/10.1101/2020.05.24.20111989 doi: 10.1101/2020.05.24.20111989 id: cord-031232-6cv8n2bf author: de Weck, Olivier title: Handling the COVID‐19 crisis: Toward an agile model‐based systems approach date: 2020-08-27 words: 7906.0 sentences: 343.0 pages: flesch: 52.0 cache: ./cache/cord-031232-6cv8n2bf.txt txt: ./txt/cord-031232-6cv8n2bf.txt summary: In this paper, authors from several of the key countries involved in COVID‐19 propose a holistic systems model that views the problem from a perspective of human society including the natural environment, human population, health system, and economic system. 34 In order to take into account and to avoid such paradoxical consequences, one must choose a systems approach to analyze the COVID-19 crisis, integrating all existing domains of knowledge into a common understanding of the crisis, in order to obtain a global vision, both in space and time and at different possible observation scales, and thus giving a chance to find the global optimum for human society as a whole. • The lifecycle of the social system can be analyzed to first order in terms of wealth and health, where these features can be, respectively, In a systems approach, we will thus have to construct the different possible global lifecycle scenarios that can be achieved in this way (see Figure 4 for an illustration of this classical process), to evaluate their probabilities and to define means to mitigate the worst consequences. abstract: The COVID‐19 pandemic has caught many nations by surprise and has already caused millions of infections and hundreds of thousands of deaths worldwide. It has also exposed a deep crisis in modeling and exposed a lack of systems thinking by focusing mainly on only the short term and thinking of this event as only a health crisis. In this paper, authors from several of the key countries involved in COVID‐19 propose a holistic systems model that views the problem from a perspective of human society including the natural environment, human population, health system, and economic system. We model the crisis theoretically as a feedback control problem with delay, and partial controllability and observability. Using a quantitative model of the human population allows us to test different assumptions such as detection threshold, delay to take action, fraction of the population infected, effectiveness and length of confinement strategies, and impact of earlier lifting of social distancing restrictions. Each conceptual scenario is subject to 1000+ Monte‐Carlo simulations and yields both expected and surprising results. For example, we demonstrate through computational experiments that maintaining strict confinement policies for longer than 60 days may indeed be able to suppress lethality below 1% and yield the best health outcomes, but cause economic damages due to lost work that could turn out to be counterproductive in the long term. We conclude by proposing a hierarchical Computerized, Command, Control, and Communications (C4) information system and enterprise architecture for COVID‐19 with real‐time measurements and control actions taken at each level. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461451/ doi: 10.1002/sys.21557 id: cord-031957-df4luh5v author: dos Santos-Silva, Carlos André title: Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era date: 2020-09-02 words: 16609.0 sentences: 954.0 pages: flesch: 43.0 cache: ./cache/cord-031957-df4luh5v.txt txt: ./txt/cord-031957-df4luh5v.txt summary: 19 Plant AMPs are the central focus of the present review, comprising information on their structural features (at genomic, gene, and protein levels), resources, and bioinformatic tools available, besides the proposition of an annotation routine. 26 Plant AMPs are also classified into families considering protein sequence similarity, cysteine motifs, and distinctive patterns of disulfide bonds, which determine the folding of the tertiary structure. 27, 31 These AMP categories will be detailed in the next sections, together with other groups here considered (Impatienlike, Macadamia [β-barrelins], Puroindoline (PIN), and Thaumatin-like protein [TLP]) and the recently described αhairpinin AMPs. The description includes comments on their structure, pattern for regular expression (REGEX) analysis (when available), functions, tissue-specificity, and scientific data availability. 179 As to the TLP structure, this protein presents characteristic thaumatin signature (PS00316): 180, 181 Most of the TLPs have molecular mass ranging from 21 to 26 kDa, 163 possessing 16 conserved cysteine residues (Supplementary Figure S8) involved in the formation of 8 disulfide bonds, 182 which help in the stability of the molecule, allowing a correct folding even under extreme conditions of temperature and pH. abstract: Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476358/ doi: 10.1177/1177932220952739 id: cord-004584-bcw90f5b author: nan title: Abstracts: 8th EBSA European Biophysics Congress, August 23rd–27th 2011, Budapest, Hungary date: 2011-08-06 words: 106850.0 sentences: 5038.0 pages: flesch: 41.0 cache: ./cache/cord-004584-bcw90f5b.txt txt: ./txt/cord-004584-bcw90f5b.txt summary: Our goals are two-fold: (1) to monitor conformational changes in each domain upon its binding to specific ligands and then to correlate the observed changes with structural differences between the CRDs and (2) to investigate the interaction between the CRDs and lipid model membranes. Cholesterol-assisted lipid and protein interactions such as the integration into lipid nanodomains are considered to play a functional part in a whole range of membrane-associated processes, but their direct and non-invasive observation in living cells is impeded by the resolution limit of [200nm of a conventional far-field optical microscope. Therefore, to investigate the dynamic and complex membrane lateral organization in living cells, we have developed an original approach based on molecule diffusion measurements performed by fluorescence correlation spectroscopy at different spatial scales (spot variable FCS, svFCS) (1). abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080017/ doi: 10.1007/s00249-011-0734-z id: cord-006229-7yoilsho author: nan title: Abstracts of the 82(nd) Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) and the 18(th) Annual Meeting of the Network Clinical Pharmacology Germany (VKliPha) in cooperation with the Arbeitsgemeinschaft für Angewandte Humanpharmakologie e.V. (AGAH) date: 2016-02-06 words: 133493.0 sentences: 6804.0 pages: flesch: 42.0 cache: ./cache/cord-006229-7yoilsho.txt txt: ./txt/cord-006229-7yoilsho.txt summary: It directly activates Protein Kinase A (PKA) or the Exchange protein directly activated by cAMP (Epac) which is a guanine exchange factor (GEF) for the small monomeric GTPase Rap. As Human umbilical vein endothelial cells (HUVEC) express both cAMP effectors (Epac1 and PKA), we investigated the role of cAMP-signaling using a spheroid based sprouting assay as an in vitro model for angiogenesis. After activation, S1P receptors regulate important processes in the progression of renal diseases, such as mesangial cell migration Methods and Results: Here we demonstrate that dexamethasone treatment lowered S1P 1 mRNA and protein expression levels in rat mesangial cells measured by TaqMan® and Western blot analyses. The aim of this study was to investigate the relevance of IGFBP5 in cardiogenesis and cardiac remodeling and its role as a potential target for ameliorating stress-induced cardiac remodeling Methods and Results: We investigated the expression of Igfbp5 in murine cardiac tissue at different developmental stages by qPCR normalized to Tpt1 (Tumor Protein, Translationally-Controlled 1). abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100641/ doi: 10.1007/s00210-016-1213-y id: cord-015147-h0o0yqv8 author: nan title: Oral Communications and Posters date: 2014-09-12 words: 73711.0 sentences: 3862.0 pages: flesch: 43.0 cache: ./cache/cord-015147-h0o0yqv8.txt txt: ./txt/cord-015147-h0o0yqv8.txt summary: Cyclooxygenases (COX) catalyze the first step in the synthesis of prostaglandins (PG) from arachidonic acid.COX-1 is constitutively expressed.The COX-2 gene is an immediate early-response gene that is induced by variety of mitogenic and inflammatory stimuli.Levels of COX-2 are increased in both inflamed and malignant tissues.In inflamed tissues, there is both pharmacological and genetic evidence that targeting COX-2 can either improve (e.g., osteoarthritis) or exacerbate symptoms (e.g., inflammatory bowel disease).Multiple lines of evidence suggest that COX-2 plays a significant role in carcinogenesis.The most specific data that support a cause-and effect relationship between COX-2 and tumorigenesis come from genetic studies.Overexpression of COX-2 has been observed to drive tumor formation whereas COX-2 deficiency protects against several tumor types.Selective COX-2 inhibitors protect against the formation and growth of experimental tumors.Moreover, selective COX-2 inhibitors are active in preventing colorectal adenomas in humans.Increased amounts of COX-2-derived PGE2 are found in both inflamed and neoplastic tissues.The fact that PGE2 can stimulate cell proliferation, inhibit apoptosis and induce angiogenesis fits with evidence that induction of COX-2 contributes to both wound healing and tumor growth.Taken together, it seems likely that COX-2 induction contributes to wound healing in response to injury but reduces the threshold for carcinogenesis. abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7095932/ doi: 10.1007/bf03353884 id: cord-023284-i0ecxgus author: nan title: Abstracts of publications related to QASR date: 2006-09-19 words: nan sentences: nan pages: flesch: nan cache: txt: summary: abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168536/ doi: 10.1002/qsar.19900090309 id: cord-354627-y07w2f43 author: pinter, g. title: COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach date: 2020-05-06 words: 5478.0 sentences: 337.0 pages: flesch: 50.0 cache: ./cache/cord-354627-y07w2f43.txt txt: ./txt/cord-354627-y07w2f43.txt summary: As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. Due to the complex nature of the COVID-19 outbreak and its irregularity in different countries, the standard epidemiological models, i.e., susceptible-infected-resistant (SIR)-based models, had been challenged for delivering higher performance in individual nations. In this study the hybrid machine learning model of MLP-ICA and ANFIS are used to predict the COVID-19 outbreak in Hungary. Both machine learning models, as an alternative to epidemiological models, showed potential in predicting COVID-19 outbreak as well as estimating total mortality. abstract: Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. url: http://medrxiv.org/cgi/content/short/2020.05.02.20088427v1?rss=1 doi: 10.1101/2020.05.02.20088427 id: cord-103435-yufvt44t author: van Aalst, Marvin title: Constructing and analysing dynamic models with modelbase v1.0 - a software update date: 2020-10-02 words: 4085.0 sentences: 208.0 pages: flesch: 37.0 cache: ./cache/cord-103435-yufvt44t.txt txt: ./txt/cord-103435-yufvt44t.txt summary: Background Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Results and Discussion We provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. Most recently, deterministic models simulating the dynamics of infectious diseases gained the interest of the general public during our combat of the Covid-19 pandemic, when a large number of ODE based mathematical models has been developed and discussed even in nonscientific journals (see for example [3] [4] [5] ). Implementation modelbase is a Python package to facilitate construction and analysis of ODE based mathematical models of biological systems. We are presenting here updates of our modelling software that has been developed to simplify the building process of mathematical models based on ODEs. modelbase is fully embedded in the Python programming language. abstract: Background Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired. Results and Discussion We provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps, modelbase v1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format using modelbase. Conclusion With the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes. url: https://doi.org/10.1101/2020.09.30.321380 doi: 10.1101/2020.09.30.321380 id: cord-298646-wurzy88k author: van der Merwe, René title: Challenge models to assess new therapies in chronic obstructive pulmonary disease date: 2012-09-13 words: 4775.0 sentences: 240.0 pages: flesch: 40.0 cache: ./cache/cord-298646-wurzy88k.txt txt: ./txt/cord-298646-wurzy88k.txt summary: This review focuses on human challenge models with lipopolysaccharide endotoxin, ozone, and rhinovirus, in the early clinical development phases of novel therapeutic agents for the treatment and reduction of exacerbations in COPD. One of the main challenges in developing new therapeutic agents for the treatment or prevention of acute exacerbations of COPD is that their potential success cannot be entirely known until the investigational therapies enter relatively large Phase II studies, assessing clinical outcome over a 3-to 6-month period or longer. 20 In the first reported study of the inflammatory effects of low-level O 3 exposure (80 ppb O 3 for 6.6 hours) in healthy volunteers, 21 there were statistically significant increases in polymorphononuclear neutrophils, prostaglandin E 2 , lactate dehydrogenase, IL-6, α1-antitrypsin, and decreased phagocytosis via the complement receptor. The O 3 -challenge model potentially provides critical decision-making data in understanding whether new compounds have the desired biological effect in healthy volunteers and patients with COPD; hence it can de-risk decisions to move forwards into large Phase II safety and efficacy trials. abstract: Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality. Current therapies confer partial benefits either by incompletely improving airflow limitation or by reducing acute exacerbations, hence new therapies are desirable. In the absence of robust early predictors of clinical efficacy, the potential success of novel therapeutic agents in COPD will not entirely be known until the drugs enter relatively large and costly clinical trials. New predictive models in humans, and new study designs are being sought to allow for confirmation of pharmacodynamic and potentially clinically meaningful effects in early development. This review focuses on human challenge models with lipopolysaccharide endotoxin, ozone, and rhinovirus, in the early clinical development phases of novel therapeutic agents for the treatment and reduction of exacerbations in COPD. url: https://doi.org/10.2147/copd.s30664 doi: 10.2147/copd.s30664 ==== make-pages.sh questions [ERIC WAS HERE] ==== make-pages.sh search /data-disk/reader-compute/reader-cord/bin/make-pages.sh: line 77: /data-disk/reader-compute/reader-cord/tmp/search.htm: No such file or directory Traceback (most recent call last): File "/data-disk/reader-compute/reader-cord/bin/tsv2htm-search.py", line 51, in with open( TEMPLATE, 'r' ) as handle : htm = handle.read() FileNotFoundError: [Errno 2] No such file or directory: '/data-disk/reader-compute/reader-cord/tmp/search.htm' ==== make-pages.sh topic modeling corpus Zipping study carrel