Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 28 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 4881 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 57 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 28 Hubei 12 China 9 Wuhan 6 COVID-19 3 SARS 3 Province 3 January 2 February 1 student 1 population 1 patient 1 epidemic 1 city 1 child 1 case 1 Turkey 1 PIBA 1 HCV 1 HBV 1 Fig 1 ECMO 1 CHIKV 1 ACE2 Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 1052 case 864 city 692 number 682 % 531 epidemic 528 population 491 datum 452 model 448 transmission 441 outbreak 431 province 422 rate 407 study 403 day 386 infection 347 time 322 patient 316 virus 276 death 271 effect 257 disease 257 coronavirus 251 spread 221 period 215 measure 215 analysis 207 people 201 level 197 result 189 region 189 lockdown 184 health 178 r 178 control 165 risk 159 preprint 155 country 153 policy 148 author 147 date 143 value 140 variable 139 factor 138 week 135 pneumonia 133 year 132 area 131 parameter 130 individual 124 license Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 924 Hubei 843 Wuhan 651 China 362 COVID-19 301 January 236 February 227 al 199 Province 192 SARS 180 . 177 et 161 Fig 131 Health 118 Table 88 q 78 Coronavirus 71 t 66 National 58 City 55 March 53 B 51 ND 51 CC 51 BY 50 Spring 49 World 47 β 47 j 47 Festival 46 Novel 45 Organization 44 Henan 41 Commission 41 CoV-2 41 C 40 Jiangxi 40 HCV 39 Shanghai 39 NC 39 HBV 38 PIBA 37 Turkey 35 Chinese 34 medRxiv 33 i 33 New 32 J 32 December 31 Zhang 31 Treat Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 795 we 337 it 194 i 143 they 40 them 17 us 10 he 8 you 8 itself 3 themselves 2 she 2 s 2 one 1 yjay3t38 1 -1840 Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 4063 be 823 have 388 use 377 confirm 265 report 244 show 238 estimate 222 include 178 base 169 find 150 indicate 143 increase 138 predict 132 reduce 126 infect 116 cause 113 do 112 make 112 float 105 take 105 follow 104 spread 100 provide 100 consider 93 display 92 collect 89 control 88 compare 83 suggest 80 identify 80 decrease 80 calculate 79 affect 78 grant 78 associate 77 analyze 75 review 73 obtain 72 implement 69 see 69 accord 67 eat 66 import 65 expose 62 exclude 62 become 61 prevent 61 present 59 give 59 assume Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 409 not 371 other 289 more 267 high 250 - 235 also 222 first 199 different 197 new 183 covid-19 169 daily 163 novel 160 public 154 such 147 respectively 134 early 129 large 124 infected 123 local 123 human 117 therefore 115 chinese 114 infectious 114 however 109 second 109 most 107 available 104 significant 102 total 98 severe 92 low 91 only 91 clinical 90 well 90 same 83 medical 83 international 82 epidemiological 81 then 81 many 80 further 79 cumulative 78 significantly 77 respiratory 75 very 75 social 74 potential 74 city 73 important 71 non Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 58 most 21 large 19 high 18 least 15 good 12 Most 5 late 5 bad 4 small 4 great 3 early 2 southernmost 2 short 2 long 2 likeli 2 HBcAb 1 young 1 strict 1 old 1 new 1 low 1 fast 1 cold 1 big 1 Least Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 51 most 7 least 6 hard 5 well 2 worst 1 highest Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 37 doi.org 4 qianxi.baidu.com 3 links.lww.com 3 github.com 2 www.whtv.com.cn 2 www.who.int 2 www.ncbi.nlm.nih.gov 2 www.cdc.gov 2 wjw.hubei.gov.cn 2 swissmodel.expasy.org 2 kns.cnki.net 2 en.nhc.gov 2 blast.ncbi.nlm.nih.gov 1 www.nhc.gov.cn 1 www.mdpi.com 1 www.cnn.com 1 wsjkw.hlj.gov.cn 1 orcid.org 1 news.ifeng.com 1 mc.manuscriptcentral.com 1 dx.doi.org 1 doi 1 creativecommonshorg 1 creat 1 2019ncov.chinacdc.cn Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 14 http://doi.org/10.1101/2020.02 8 http://doi.org/10.1101/2020.04.02.20050781 7 http://doi.org/10 6 http://doi.org/10.1101/2020.04.11.20061952 2 http://www.whtv.com.cn/p/17571.html 2 http://www.who.int/csr/sars/country/en/ 2 http://www.ncbi.nlm.nih.gov/ 2 http://www.cdc.gov/coronavirus/2019-ncov/index.html 2 http://swissmodel.expasy.org 2 http://qianxi.baidu.com/ 2 http://kns.cnki.net/ 2 http://github.com/rrwick/Porechop 2 http://en.nhc.gov 2 http://doi.org/10.1101/2020.03.24.20042424 2 http://blast.ncbi.nlm.nih.gov/Blast.cgi 1 http://www.nhc.gov.cn/yjb/new_index.shtml 1 http://www.mdpi.com/1660-4601/17/15/5627/s1 1 http://www.cnn.com/travel/article/ 1 http://wsjkw.hlj.gov.cn/index 1 http://wjw.hubei.gov.cn/fbjd/dtyw/ 1 http://wjw.hubei.gov.cn/bmdt/ztzl/fkxxgzbdgrfyyq/ 1 http://qianxi.baidu.com/2020/ 1 http://qianxi.baidu.com 1 http://orcid.org/0000-0001-6139-4355 1 http://news.ifeng.com/c/7uHMHXcFHmq 1 http://mc.manuscriptcentral.com/qjm 1 http://links.lww.com/CM9/A210] 1 http://links.lww.com/CM9/ 1 http://links.lww.com/ 1 http://github.com/wybert/openwuhan-ncov-illness-data 1 http://dx.doi.org/10.7717/ 1 http://doi 1 http://creativecommonshorg/licenses/by/4.0/ 1 http://creat 1 http://2019nCoV.chinacdc.cn/2019-nCoV/ Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 317342267@qq.com Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 7 coronavirus indicating person 5 wuhan were not 4 cases increased rapidly 4 transmission control measures 3 cases did not 3 cases is still 3 hubei are calmer 3 hubei is much 3 hubei is not 3 hubei were more 3 study are available 3 wuhan reduced inflow 2 cases had more 2 cases is less 2 cases were mainly 2 cases were not 2 cities are not 2 covid-19 is less 2 data were available 2 effect is not 2 effects including panic 2 epidemic is still 2 hubei takes actions 2 hubei were not 2 model are smaller 2 model does not 2 patients had normal 2 patients were also 2 population are worth 2 province is effective 2 province was not 2 province were similar 2 rate is larger 2 rates are lower 2 transmission is still 2 virus spread very 2 wuhan are significantly 1 % did not 1 % floating populations 1 % had college 1 % had likely 1 % had senior 1 % reported here 1 case reports statistical 1 cases are available 1 cases are main 1 cases are very 1 cases had symptoms 1 cases have also 1 cases have not Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 effect is not statistically 1 % had no underlying 1 cases have not yet 1 cases was not very 1 china is not large 1 cities are not as 1 cities were not yet 1 days are not significantly 1 disease is not serious 1 epidemic has not yet 1 epidemic was not over 1 hubei are no longer 1 hubei is not only 1 hubei was not significant 1 hubei were no longer 1 model was not sensitive 1 numbers are not large 1 rate is not very 1 spread are not well 1 study had no role 1 wuhan are not just 1 wuhan does not necessarily 1 wuhan have no new 1 wuhan is not significant A rudimentary bibliography -------------------------- id = cord-313675-fsjze3t2 author = Aslan, ibrahim Halil title = Modeling COVID-19: Forecasting and analyzing the dynamics of the outbreak in Hubei and Turkey date = 2020-04-15 keywords = Hubei; Turkey summary = We provide forecasts for the peak of the outbreak and the total number of cases/deaths in Turkey, for varying levels of social distancing, quarantine, and COVID-19 testing. In addition, we also provide 15-day forecasts of the fatality rate of the outbreak, the number of cases, and the number of deaths depending on the data (Chinese physicians, 2020; Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE, 2020; World Health Organization, 2020b) and outputs of our SEIQR model. The rate of reported cases i q denotes the number of individuals who transition from the infected class I to the reported class I q per day; it is also directly related to the daily number of COVID-19 tests carried out during the outbreak. In this part, we estimate the parameters in the system (1), so we fit our model with the daily reported cumulative number of cases and deaths, which are provided by (World Health Organization, 2020b) and (Chinese physicians, 2020). doi = 10.1101/2020.04.11.20061952 id = cord-325012-yjay3t38 author = Chen, Ze-Liang title = Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China date = 2020-02-28 keywords = Hubei; January; Wuhan summary = Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. The relative risk according to time increased steadily from January 20 onwards and the upward trend continued as of January 30 [ Figure 2C ], indicating that the number of cases nationwide is on the rise. From January 1 to 23, 2020, the population that migrated out of Wuhan city and Hubei province increased steadily, peaking on January 21 and 22 [ Figure 4A ]. To analyze the correlation between the number of cases and the emigration in Wuhan city and Hubei province, population migration data were collected from Baidu Qianxi. The correlation coefficient between the provincial number of cases and emigration from Wuhan increased to 0.943, with the highest coefficient of 0.996 observed between Wuhan and other cities of Hubei provinces [ Figure 4E and 4F; Supplementary Tables 3 and 4 , http://links.lww.com/ CM9/A210]. doi = 10.1097/cm9.0000000000000782 id = cord-265680-ztk6l2n2 author = Deng, J title = High COVID-19 mortality in the UK: Lessons to be learnt from Hubei Province – Are under-detected “silent hypoxia” and subsequently low admission rate to blame? date = 2020-08-31 keywords = Hubei summary = title: High COVID-19 mortality in the UK: Lessons to be learnt from Hubei Province – Are under-detected "silent hypoxia" and subsequently low admission rate to blame? With centralised isolation and timely treatment to prevent transmission and deterioration of the infection, and with occasional transfers of patients with worsening symptoms to ICU, this drastically decreased the mortality over the entire epidemic in Hubei [ Table 1 ]. As a result, no new cases were found, with only https://mc.manuscriptcentral.com/qjm the 68,135 confirmed cases in Table 1 is a highly reliable reflection of the epidemic in Hubei after the initial chaotic statistics in January. For example, by the end of July, New York had most Covid deaths in the US with 32,683 fatalities, yet a "Nightingale" hospital costing $52 million treated only 79 virus patients. New diagnosis and treatment scheme for novel coronavirus infected pneumonia doi = 10.1093/qjmed/hcaa262 id = cord-317465-ucwuptgg author = FANG, H. title = Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China date = 2020-03-26 keywords = Hubei; January; Wuhan; city summary = In this paper, we exploit the exogenous variations in human mobility created by lockdowns of Chinese cities during the outbreak of the Novel Coronavirus (2019-nCoV), and utilize a variety of high-quality data sets, to study the effectiveness of an unprecedented cordon sanitaire of the epicenter of COVID-19, and provide a comprehensive analysis on the role of human mobility restrictions in the delaying and the halting of the spread of the COVID-19 pandemic. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities'' new infection cases ( Figure 4 ). In this paper, we quantify the causal impact of human mobility restrictions, particularly the lockdown of the city of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus, and estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities'' new infection cases. doi = 10.1101/2020.03.24.20042424 id = cord-351880-iqr419fp author = Fan, Changyu title = Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study date = 2020-03-04 keywords = Hubei; Province; Wuhan; population summary = Total 1999 2000 2000 2000 2000 2000 11,999 Hubei 1514 1508 1487 1465 1477 1547 8998 Henan 113 134 109 159 170 125 810 Anhui 59 58 55 53 56 46 327 Hunan 57 46 68 54 41 36 302 Jiangxi 58 40 53 57 49 34 291 Chongqing 34 29 34 33 33 35 198 Zhejiang 22 29 25 33 25 33 167 Sichuan 22 30 45 21 22 27 167 Fujian 14 17 16 15 39 19 120 Jiangsu 38 13 16 19 13 11 110 Shandong 12 18 11 13 8 12 74 Guangdong 7 8 18 18 14 8 73 Hebei 0 1 5 Tianjin 1 0 0 1 0 1 3 Shanghai 0 1 0 1 0 1 3 Inner Mongolia 1 0 0 0 1 0 2 Xizang 0 0 1 0 0 1 2 Ningxia 0 0 1 0 0 0 1 According to the current infectious features of 2019-nCoV, which are that middle-aged and elderly people have a high risk of infection, and transmission can occur between individuals, families and communities, we assessed several main variables. doi = 10.3390/ijerph17051679 id = cord-283891-m36un1y2 author = Hu, Bisong title = First, second and potential third generation spreads of the COVID-19 epidemic in mainland China: an early exploratory study incorporating location-based service data of mobile devices date = 2020-05-17 keywords = China; Hubei; Wuhan summary = Methods We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics. Here, using location-based service (LBS) data of mobile devices, we analyzed the spatiotemporal association of the confirmed COVID-19 cases and human movements from the sources of the epidemic outbreak, and revealed the first, second and potential third generation spreads of the COVID-19 epidemic in mainland China. Based on the above datasets of COVID-19 cases in mainland China and two categories of location-based service data of mobile devices from the epidemic sources, we calculated their Pearson correlation coefficient, r, and spatial stratified heterogeneity (SSH), q, statistics. doi = 10.1016/j.ijid.2020.05.048 id = cord-351659-ujbxsus4 author = Jiang, Xiandeng title = A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China date = 2020-08-19 keywords = China; February; Hubei summary = We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mainland China over January 31–February 19, 2020. Our results demonstrate that the influential inter-location routes from Hubei have become unidentifiable since February 4, 2020, whereas the self-transmission in each provincial-level administrative region (location, hereafter) was accelerating over February 4–15, 2020. Implications of our results suggest that in addition to the origin of the outbreak, virus preventions are of crucial importance in locations with the largest migrant workers percentages (e.g., Jiangxi, Henan and Anhui) to controlling the spread of COVID-19. This enables the detection and visualization of time-varying inter-location and self-transmission routes of the COVID-19 on the daily basis. On the fifth day (February 4, 2020), no influential transmission routes were found from Hubei to directly affect other locations, and there were only three influential routes identified nationally, including Zhejiang-Shaanxi, www.nature.com/scientificreports/ Zhejiang-Jiangxi and Jiangxi-Shanghai. doi = 10.1038/s41598-020-71023-9 id = cord-326599-n0vmb946 author = Leung, Char title = The difference in the incubation period of 2019 novel coronavirus (SARS-CoV-2) infection between travelers to Hubei and non-travelers: The need of a longer quarantine period date = 2020-03-18 keywords = Hubei summary = title: The difference in the incubation period of 2019 novel coronavirus (SARS-CoV-2) infection between travelers to Hubei and non-travelers: The need of a longer quarantine period Data collected from the individual cases reported by the media were used to estimate the distribution of the incubation period of travelers to Hubei and non-travelers. Against this background, the present work estimated the distribution of incubation periods of patients infected in and outside Hubei. The very first observation of the incubation period of SARS-CoV-2 came from the National Health Such difference might be due to the difference in infectious dose since travelers to Hubei might be exposed to different sources of infection multiple times during their stay in Hubei. Incubation period of 2019 novel coronavirus (COVID-19) infections among travellers from Wuhan doi = 10.1017/ice.2020.81 id = cord-286334-d9v5xtx7 author = Li, Rui title = Analysis of angiotensin-converting enzyme 2 (ACE2) from different species sheds some light on cross-species receptor usage of a novel coronavirus 2019-nCoV date = 2020-04-30 keywords = ACE2; CHIKV; China; ECMO; Fig; HBV; HCV; Hubei; patient summary = More detailed monitoring on how these physiological parameters change over time (perhaps including more complex cytokine studies), in these severely ill, influenza A(H1N1)pdm09-infected patients admitted to ICU-ECMO units, may eventually yield data to improve their management and clinical outcomes. 5 In the current study, we characterized a new HCV subtypes among chronic hepatitis C patients in Yunnan, China, initially designated as 6xi, further analyzed its evolutionary history and investigated its baseline RAS by next generation sequencing (NGS) method. The samples met the following inclusion criteria: (1) hepatitis C antibody-positive for 6 months with normal serum alanine aminotransferase (ALT) levels; (2) subject was residing in Yunnan province and was over 18 years old; (3) complete demographic information and clinical data were available; (4) consented to the use of patient information in studies on HCV epidemics; and (5) were treatment-naïve during sampling. doi = 10.1016/j.jinf.2020.02.013 id = cord-296669-1md8j11e author = Li, Xin title = Factors Associated with Mental Health Results among Workers with Income Losses Exposed to COVID-19 in China date = 2020-08-04 keywords = COVID-19; China; Hubei summary = The degrees of the depression, anxiety, insomnia, and distress symptoms of our participants were assessed using the Chinese versions of the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the Insomnia Severity Index-7 (ISI-7), and the revised 7-item Impact of Event Scale (IES-7) scales, respectively, which found that the prevalence rates of depression, anxiety, insomnia, and distress caused by COVID-19 were 45.5%, 49.5%, 30.9%, and 68.1%, respectively. Mental disorders, including depression, anxiety, insomnia, and distress, caused by COVID-19 were assessed in our study by Chinese versions of validated measurement tools [24] [25] [26] [27] : the Patient Health Questionnaire-9 (PHQ-9; the total score ranged from 0 to 27) [24] , the Generalized Anxiety Disorder-7 (GAD-7; the total score ranged from 0 to 21) [25] , the Insomnia Severity Index-7 (ISI-7; the total score ranged from 0 to 28) [26] , and the revised 7-item Impact of Event Scale (IES-7; the total score ranged from 0 to 28) [27] . doi = 10.3390/ijerph17155627 id = cord-273531-q9ah287w author = Li, Yang title = Characteristics of COVID-19 Near China''s Epidemic Center date = 2020-06-26 keywords = China; Hubei summary = Background: This study described and analyzed the age, gender, infection sources, and timing characteristics of the 416 confirmed cases in two cities near the center of China''s COVID-19 outbreak. Methods: This study used publicly available data to examine gender, age, source of infection, date returned from Hubei, date of disease onset, date of first medical visit, date of final diagnosis, and date of recovery of COVID-19 cases. Results: Public-use data revealed similar risks of infection by age and that the numbers of new and final diagnoses of confirmed cases first increased, peaked at about two weeks, and then gradually decreased. The first novel coronavirus pneumonia (COVID19) case was identified in Wuhan, Hubei Province, China, on December 12, 2019, after which the disease gradually spread. The variables used in the analysis were: gender, age, source of infection, date returned from Hubei, date of disease onset, date of first medical visit, date of final diagnosis, and date of recovery. doi = 10.1016/j.ajic.2020.06.191 id = cord-313700-enivzp1f author = Lio, Chon Fu title = The common personal behavior and preventive measures among 42 uninfected travelers from the Hubei province, China during COVID-19 outbreak: a cross-sectional survey in Macao SAR, China date = 2020-06-19 keywords = COVID-19; China; Hubei summary = title: The common personal behavior and preventive measures among 42 uninfected travelers from the Hubei province, China during COVID-19 outbreak: a cross-sectional survey in Macao SAR, China A further survey of comparison of personal preventive measures before and during disease outbreak showed increased alert and practice of personal protection and hygiene during the spread (Table 3) , such as wearing a mask when outdoor (16.7% and 95.2%, P < 0.001), wearing a mask every time when contact or talk with people (10% and 95%, P < 0.001), often wash hands with soap/liquid soap (85.7% and 100%, P = 0.031), use of alcohol-based hand sanitizers or disinfected wipes as substitute if handwashing facility not available (71.4% and 95.2%, P = 0.006), cleaning clothes and personal belongings immediately once get back home (35.7% and 78.6%, P < 0.001), cleaning mobile phone regularly (43.9% and 65.9%, P = 0.012). Good personal hygiene and adequate preventive measures such as less gathering, frequent handwashing, in addition to wearing a mask outdoor, were common grounds among 42 uninfected participants during the stay in Hubei province under COVID-19 outbreak. doi = 10.7717/peerj.9428 id = cord-292537-9ra4r6v6 author = Liu, Fenglin title = Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models date = 2020-08-27 keywords = COVID-19; China; Hubei; Wuhan summary = For the study of infectious diseases like COVID-19, SARS, and Ebola, most of the literature used descriptive research or model methods to assess indicators and analyze the effect of interventions, such as combining migration data to evaluate the potential infection rate [18, 19] , understanding the impact of factors like environmental temperature and vaccines that might be potentially linked to the diseases [20, 21] , using basic and time-varying reproduction number (R 0 & R t ) to estimate changeable transmission dynamics of epidemic conditions [22] [23] [24] [25] [26] [27] , calculating and predicting the fatal risk to display any stage of outbreak [28] [29] [30] , or providing suggestions and interventions from risk management and other related aspects based on the results of modeling tools or historical lessons [31] [32] [33] [34] [35] [36] [37] [38] [39] . doi = 10.1371/journal.pone.0238280 id = cord-339743-jxj10857 author = Liu, H. title = Synchronized travel restrictions across cities can be effective in COVID-19 control date = 2020-04-06 keywords = Hubei; Wuhan summary = Previous studies established the impact of population outflow from Wuhan on the spatial spread of coronavirus in China and hinted the impact of the other three mobility patterns, i.e., population outflow from Hubei province excluding Wuhan, population inflow from cities outside Hubei, and intra-city population movement. Here we apply the cumulative confirmed cases and mobility data of 350 Chinese cities outside Hubei to explore the relationships between all mobility patterns and epidemic spread, and estimate the impact of local travel restrictions, both in terms of level and timing, on the epidemic control based on mobility change. We assume, after the Wuhan lockdown, the local travel restrictions in cities outside Hubei contributed to the epidemic control by influencing population mobility. The daily population outflow from Hubei (excluding Wuhan), inter-city population movement, and intra-city population movement after Feb 03, 2019, aligned by the Chinese lunar calendar with Jan 23, 2020, were used as proxy mobility data for the no local travel restrictions status in cities outside Hubei. doi = 10.1101/2020.04.02.20050781 id = cord-035307-r74ovkbd author = Liu, Shuchang title = Attitudes towards Wildlife Consumption inside and outside Hubei Province, China, in Relation to the SARS and COVID-19 Outbreaks date = 2020-11-11 keywords = COVID-19; Hubei; SARS summary = Our study results indicate over the period between the SARS epidemic to the outbreak of the COVID-19 pandemic, attitudes towards the consumption of wildlife in China have changed significantly. Therefore, our aim in this study was to determine changes in attitudes towards wildlife consumption in Chinese adults in relation to the SARS and COVID-19 outbreaks with a particular focus on Hubei Province. doi = 10.1007/s10745-020-00199-5 id = cord-291750-4s93wniq author = Lv, Boyan title = Global COVID-19 fatality analysis reveals Hubei-like countries potentially with severe outbreaks date = 2020-04-14 keywords = Hubei summary = The outbreak of 2019 novel coronavirus diseases (COVID-19) is ongoing in China, 1 but appears to reach late stage and also just starts to devastate other countries. We collected data of the officially released cumulative numbers of confirmed cases and deaths (from 23 January to 13 March 2020) with respect to mainland China, epicenter of the outbreak (i.e., Hubei Province and Wuhan City), outside Hubei (in China) and outside Wuhan (in Hubei), as well as to typical countries reported with a substantial number of deaths including South Korea, Japan, Iran, Italy, USA, France and Spain ( Fig. 1 ) . In view of the detailed P values among all pairs (Table S1 ), we suppose the ranking for the severity of COVID-19 outbreaks in different countries/regions in terms of CFRs as follows: Iran > Wuhan > Hubei ≈USA ≈Italy > outside Wuhan ≈Spain ≈Japan ≈France > South Korea ≈outside Hubei. doi = 10.1016/j.jinf.2020.03.029 id = cord-345877-rhybnlw0 author = Pei, Lijun title = Prediction of numbers of the accumulative confirmed patients (NACP) and the plateau phase of 2019-nCoV in China date = 2020-04-27 keywords = China; Hubei summary = Initially, the numbers of the accumulative confirmed patients in different cities, provinces and geographical locations in China were predicted very accurately in the short term period of infection. 2, the novel fitting method of the outbreak of 2019-nCoV in China is proposed, and the selection of the data and basic functions is presented. Initially, I will present the novel fitting method for the prediction of NACP and the plateau phase of 2019-nCoV in China. In future studies, the data should be fitted to the new relaxed confirmed standards of Hubei Province and Wuhan City in order to predict the number of the accumulative confirmed patients and the plateau phase of the 2019-nCoV infection. In the present study, the novel fitting method was employed to predict the NACP and the plateau phase of the 2019-nCoV infection in different regions of China. doi = 10.1007/s11571-020-09588-4 id = cord-327096-m87tapjp author = Peng, Liangrong title = Epidemic analysis of COVID-19 in China by dynamical modeling date = 2020-02-18 keywords = Hubei; Wuhan; case summary = As shown in Fig. 3e-f , the predicted total infected cases at the end of epidemic, as well as the the inflection point, at which the basic reproduction number is less than 1 6 , both show a positive correlation with the infection rate β and the quarantined time δ −1 and a negative correlation with the protection rate α. 16.20023465 doi: medRxiv preprint of COVID-19 since its onset in Mainland * , Hubei * , and Wuhan (Beijing and Shanghai are not considered due to their too small numbers of infected cases on Jan. 20th). Based on detailed analysis of the public data of NHC of China from Jan. 20th to Feb. 9th, we estimate several key parameters for COVID-19, like the latent time, the quarantine time and the basic reproduction number in a relatively reliable way, and predict the inflection point, possible ending time and final total infected cases for Hubei, Wuhan, Beijing, Shanghai, etc. doi = 10.1101/2020.02.16.20023465 id = cord-354095-4sweo53l author = Qiu, Yun title = Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China date = 2020-05-09 keywords = China; February; Hubei; January; Wuhan summary = First, our instrumental variable approach helps isolate the causal effect of virus transmissions from other confounded factors; second, our estimate is based on an extended time period of the COVID-19 pandemic (until the end of February 2020) that may mitigate potential biases in the literature that relies on a shorter sampling period within 1-28 January 2020; third, our modeling makes minimum assumptions of virus transmissions, such as imposing fewer restrictions on the relationship between the unobserved determinants of new cases and the number of cases in the past; fourth, our model simultaneously considers comprehensive factors that may affect virus transmissions, including multiple policy instruments (such as closed management of communities and shelter-at-home order), population flow, within-and between-city transmissions, economic and demographic conditions, weather patterns, and preparedness of health care system. doi = 10.1007/s00148-020-00778-2 id = cord-285965-mar8zt2t author = Su, Liang title = The different clinical characteristics of corona virus disease cases between children and their families in China – the character of children with COVID-19 date = 2020-03-25 keywords = Hubei; SARS; child summary = This study aims to analyze the different clinical characteristics between children and their families infected with severe acute respiratory syndrome coronavirus 2. Here, we report the clinical manifestations, laboratory test results, imaging characteristics, and treatment regimen of nine SARS-CoV-2 infected children and their families in Jinan, Shandong province to increase awareness of this disease, especially in children. A retrospective review was conducted of the clinical, lab tests, and radiologic findings for nine children and their families admitted to the Jinan Infectious Diseases Hospital identified to be nucleic acid-positive for SARS-CoV-2 from 24 January 2020 to 24 February 2020. All the patients were recorded with basic information and epidemiological histories [4] including (1) History of travel or residence in Wuhan and surrounding areas or other reported cases within 14 days of onset; (2) History of contact with new coronavirus infection (nucleic acid-positive) 14 days before onset; (3) history of contact with patients with fever or respiratory symptoms from Wuhan and surrounding areas, or from communities with case reports within 14 days before onset; (4) Cluster onset, along with disease condition changes. doi = 10.1080/22221751.2020.1744483 id = cord-352108-py93yvjy author = Tu, Lh title = Birth Defects Data from Surveillance Hospitals in Hubei Province, China, 200l – 2008 date = 2012-03-31 keywords = Hubei summary = title: Birth Defects Data from Surveillance Hospitals in Hubei Province, China, 200l – 2008 METHODS: The prevalence of birth defects in perinatal infants delivered after 28 weeks or more was analyzed in Hubei surveillance hospitals during 200l–2008. The two leading birth defects were cleft lip and/or palate and polydactyly, followed by congenital heart disease, hydrocephaly, external ear malformation and neural tube defects. Data published on Annual Report of the National Maternal and Child Health Care Surveillance and Communications in June, 2009 indicated that the prenatal diagnosis rate in Hubei province in 2008 was 14.29%, a bit lower than eastern coastal cities and provinces. Eight years'' BD data indicate that the BD prevalence was rising and the BD prevalence in Hubei province should be valued; prevention program of BD shall be better performed to decrease prevalence of birth deformation in perinatal infants based on improved perinatal care and prenatal diagnosis. doi = nan id = cord-321727-xyowl659 author = Wang, Lishi title = Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm date = 2020-07-20 keywords = Hubei; PIBA; Wuhan summary = We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The PIBA uses patient data in real-time to build a model that estimates and predicts death rates for the near future. Based on the days between confirmation of COVID-19 and the days of death in the hospital, calculated from Wuhan, as mentioned in method 1 and information from the whole country and Hubei Province, we tested the number of days from diagnosis to death, that most likely reflects the actual death rate. doi = 10.1016/j.scitotenv.2020.138394 id = cord-271980-8x5g8r7c author = Yao, Ye title = Ambient nitrogen dioxide pollution and spread ability of COVID-19 in Chinese cities date = 2020-09-30 keywords = COVID-19; Hubei summary = When examining the correlation between NO 2 and R 0 of COVID-19, we estimated the associations of NO 2 concentration with R 0 both inside and outside Hubei province (r & p) in the same period by using multiple linear regression models after controlling for temperature and relative humidity (as covariates in the regression model) separately. We also examined the corresponding temporal associations between NO 2 and R 0 of COVID-19 across the different cities inside and outside Hubei Province using multiple linear regression models after controlling for temperature and relative humidity separately. The cross-sectional analysis indicates that, after adjustment for temperature and relative humidity, R 0 was positively associated with NO 2 concentration at city level (meta χ 2 =10.18, J o u r n a l P r e -p r o o f p=0.037) (Figure 3) . doi = 10.1016/j.ecoenv.2020.111421 id = cord-333265-na7f0yam author = Zeng, Yiping title = Forecasting of COVID-19 Spread with dynamic transmission rate date = 2020-08-21 keywords = Hubei summary = In Section 3, based on the least square method, the improved model is optimized by considering accumulated number of infected individuals and daily new cases. 1) The exposed individuals and infected individuals have same probability to infect susceptible individuals, that is β 1 =β 2 ; 2) There is no pedestrian flow between Hubei and outside Hubei, and COVID-19 spreads in the corresponding area; 3) Removed individual from the system has no ability to infect others; 4) The transmission rate β is assumed to follow an exponential function considering the fact that fewer individuals are infected after measures are in placed; 5) The removal rate γ is supposed to follow a power exponent function, and the removal rate increases as the time processes due to the better treatment. In our model, the transmission rate β is assumed to follow exponential function by considering the fact that fewer individuals are infected after measures to prevent the virus spread. doi = 10.1016/j.jnlssr.2020.07.003 id = cord-327721-y39751g4 author = Zhang, Yan title = Emotional “inflection point” in public health emergencies with the 2019 New Coronavirus Pneumonia (NCP) in China date = 2020-07-19 keywords = Hubei; Province; SARS; student summary = BACKGROUND: The outbreak of the new coronavirus pneumonia (NCP) in Wuhan, Hubei, has caused very serious consequences and severely affected people''s lives and mental health. METHODS: This study used self-designed questionnaires and artificial intelligence (AI) to assess and analyze the emotional state of over 30,000 college students during the outbreak period in January (T1) and home quarantine in February (T2). From these data, it indicated that during the period of home isolation, college students in Hubei Province showed more negative emotions due to their long-term exposure to the epidemic. There is also the stress symptom of "seeming as being infected" caused by too much browsing of the relevant news every day, which directly affects the emotions of students, they became more sensible and anxious to disease, this is a mental tension (Peng et al., 2019) . This survey found that there is an emotional "infection point" in February among college students, especially in the Hubei area. doi = 10.1016/j.jad.2020.07.097 id = cord-009688-kjx6cvzh author = Zhao, Ze-Yu title = Relative transmissibility of shigellosis among male and female individuals: a modeling study in Hubei Province, China date = 2020-04-17 keywords = China; Hubei; Province summary = title: Relative transmissibility of shigellosis among male and female individuals: a modeling study in Hubei Province, China Owing to the different incidences in males and females, this study aims to analyze the features involved in the transmission of shigellosis among male (subscript m) and female (subscript f) individuals using a newly developed sex-based model. METHODS: The data of reported shigellosis cases were collected from the China Information System for Disease Control and Prevention in Hubei Province from 2005 to 2017. With the aim of exploring the transmission features in different gender and age groups, the SEIAR model was adopted to fit the data of shigellosis cases reported from 2005 to 2017 in Hubei Province, China. A mathematical study was implemented using a sexand age-based model to analyze the transmission characteristics of reported shigellosis cases in Hubei Province, China, from 2005 to 2017. doi = 10.1186/s40249-020-00654-x id = cord-309032-idjdzs97 author = Zhou, Feng title = Epidemiological Characteristics and Factors Associated with Critical Time Intervals of COVID-19 in Eighteen Provinces, China: A Retrospective Study date = 2020-10-09 keywords = COVID-19; China; Hubei summary = Several studies conducted in China, Italy and the United States have reported some epidemiological characteristics of COVID-19 in the initial phase (Grasselli et al., 2020 , Liang et al., 2020 , Price-Haywood et al., 2020 , Richardson et al., 2020 , Wu and McGoogan, 2020 , However, there is still a lack of research on the space-time characteristics in the populations of imported and local cases respectively which is of great significance. In this study, we described the spatiotemporal distribution of the COVID-19 in eighteen provinces of China (outside Hubei province) and investigated the epidemiological characteristics in the population of imported cases and local cases, from the beginning of this epidemic until it was under good control. We further assessed the critical influence factors associated with time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS), including demographic and temporal and spatial characteristics. doi = 10.1016/j.ijid.2020.09.1487 id = cord-332898-gi23un26 author = Zhou, Lingyun title = CIRD-F: Spread and Influence of COVID-19 in China date = 2020-04-07 keywords = China; Hubei; epidemic summary = By changing the parameters of the model accordingly, we demonstrate the control effect of the policies of the government on the epidemic situation, which can reduce about 68% possible infections. At the same time, we use the capital asset pricing model with dummy variable to evaluate the effects of the epidemic and official policies on the revenue of multiple industries. We also use a capital asset pricing model with dummy variable [6] [7] , which is called CAPM-DV model, to quantify the influence of official policies on different industries. Therefore, we use CIRD-F model for Hubei to predict the tendency of the epidemic in China, which shows that the policies help reduce about 68% possible infections. Furthermore, we use CAPM-DV model to calculate the economic impacts of the epidemic and official policies on different industries. doi = 10.1007/s12204-020-2168-1