cord-000282-phepjf55	2010	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] .
cord-000332-u3f89kvg	2011	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.
cord-000759-36dhfptw	2011	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.
cord-001603-vlv8x8l8	2015	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.
cord-001687-paax8pqh	2013	
cord-001921-73esrper	2016	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.
cord-002169-7kwlteyr	2016	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).
cord-002474-2l31d7ew	2017	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 .
cord-003243-u744apzw	2018	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.
cord-003377-9vkhptas	2018	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.
cord-004157-osol7wdp	2020	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.
cord-004332-99lxmq4u	2020	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)).
cord-004416-qw6tusd2	2020	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 .
cord-004584-bcw90f5b	2011	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).
cord-005033-voi9gu0l	2008	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.
cord-005321-b3pyg5b3	2017	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
cord-006229-7yoilsho	2016	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).
cord-007129-qjdg46o9	2005	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.
cord-007147-0v8ltunv	2010	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) .
cord-007255-jmjolo9p	2009	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.
cord-007726-bqlf72fe	2018	
cord-009481-6pm3rpzj	2009	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.
cord-010903-kuwy7pbo	2020	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.
cord-010977-fwz7chzf	2020	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.
cord-011400-zyjd9rmp	2019	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 .
cord-012866-p3mb7r0v	2020	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.
cord-013784-zhgjmt2j	2020	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.
cord-015147-h0o0yqv8	2014	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.
cord-015255-1qhgeirb	2012	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.
cord-016045-od0fr8l0	2019	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.
cord-016261-jms7hrmp	2005	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.
cord-016364-80l5mua2	2008	
cord-016954-l3b6n7ej	2008	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.
cord-016965-z7a6eoyo	2017	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.
cord-017003-3farxcc3	2010	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.
cord-017181-ywz6w2po	2008	
cord-017423-cxua1o5t	2011	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.
cord-017595-v3rllyyu	2009	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.
cord-017728-yazo0lga	2008	
cord-017934-3wyebaxb	2019	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.
cord-018746-s9knxdne	2015	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] .
cord-018791-h3bfdr14	2016	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.
cord-018899-tbfg0vmd	2011	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.
cord-018947-d4im0p9e	2012	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.
cord-018976-0ndb7rm2	2007	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.
cord-020193-3oqkdbq0	2020	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.
cord-020610-hsw7dk4d	2019	
cord-020683-5s3lghj6	2020	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.
cord-020764-5tq9cr7o	2010	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.
cord-020871-1v6dcmt3	2020	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).
cord-020888-ov2lzus4	2020	
cord-021426-zo9dx8mr	2013	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.
cord-022219-y7vsc6r7	2013	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) .
cord-022494-d66rz6dc	2014	
cord-022891-vgfv5pi4	2000	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).
cord-023284-i0ecxgus	2006	
cord-024061-gxv8y146	2020	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).
cord-024283-ydnxotsq	2020	
cord-024341-sw2pdnh6	2020	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.
cord-024501-nl0gsr0c	2020	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.
cord-024515-iioqkydg	2020	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.
cord-024552-hgowgq41	2020	
cord-024866-9og7pivv	2020	
cord-025283-kf65lxp5	2020	
cord-025348-sh1kehrh	2020	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.
cord-025404-rk2fuovf	2020	
cord-025517-rb4sr8r4	2020	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.
cord-025843-5gpasqtr	2020	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.
cord-026336-xdymj4dk	2020	
cord-026384-ejk9wjr1	2020	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?
cord-026503-yomnqr78	2020	
cord-026742-us7llnva	2020	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.
cord-026827-6vjg386e	2020	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.
cord-026949-nu46ok9w	2020	
cord-027119-zazr8uj5	2020	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.
cord-027201-owzhv0xy	2020	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.
cord-027228-s32v6bmd	2020	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 .
cord-027286-mckqp89v	2020	
cord-027315-1i94ye79	2020	
cord-027316-echxuw74	2020	
cord-027318-hinho0mh	2020	
cord-027336-yk3cs8up	2020	
cord-027337-eorjnma3	2020	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.
cord-027438-ovhzult0	2020	
cord-028420-z8sv9f5k	2020	
cord-028636-wxack9zv	2020	
cord-028789-dqa74cus	2020	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
cord-029311-9769dgb6	2020	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.
cord-030681-4brd2efp	2020	
cord-030683-xe9bn1cc	2020	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.
cord-030686-wv77zwsc	2020	
cord-031143-a1qyadm6	2020	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.
cord-031232-6cv8n2bf	2020	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.
cord-031460-nrxtfl3i	2020	
cord-031957-df4luh5v	2020	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.
cord-032413-zbbpfaj4	2020	
cord-033010-o5kiadfm	2020	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.
cord-033882-uts6wfqw	2020	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.
cord-034181-ji4empe6	2020	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.
cord-034834-zap82dta	2020	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.
cord-034839-6xctzwng	2020	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.
cord-034843-cirltmy4	2020	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.
cord-034846-05h2no14	2020	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.
cord-035388-n9hza6vm	2020	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) .
cord-048325-pk7pnmlo	2006	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.
cord-048353-hqc7u9w3	2007	
cord-048461-397hp1yt	2008	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):
cord-102359-k1xxz4hc	2005	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.
cord-102850-0kiypige	2020	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.
cord-103280-kf6mqv4e	2020	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.
cord-103435-yufvt44t	2020	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.
cord-103502-asphso2s	2020	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.
cord-103913-jgko7b0j	2020	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.
cord-104133-d01joq23	2020	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
cord-104486-syirijql	2020	
cord-117688-20gfpbyf	2020	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.
cord-118553-ki6bbuod	2020	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.
cord-119104-9d421si9	2020	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.
cord-121200-2qys8j4u	2020	
cord-122344-2lepkvby	2020	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.
cord-123800-pxhott2p	2020	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.
cord-125330-jyppul4o	2020	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.
cord-126012-h7er0prc	2020	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.
cord-127900-78x19fw4	2020	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
cord-128991-mb91j2zs	2020	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.
cord-129272-p1jeiljo	2020	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.
cord-130240-bfnav9sn	2020	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.
cord-130928-ozxvtvjt	2020	
cord-130967-cvbpgvso	2020	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.
cord-132307-bkkzg6h1	2020	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.
cord-132843-ilxt4b6g	2020	
cord-133273-kvyzuayp	2020	
cord-133917-uap1vvbm	2020	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.
cord-135004-68y19dpg	2020	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.
cord-140624-lphr5prl	2020	
cord-140839-rij8f137	2020	
cord-140977-mg04drna	2020	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".
cord-142398-glq4mjau	2020	
cord-143539-gvt25gac	2020	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
cord-143847-vtwn5mmd	2020	
cord-147202-clje3b2r	2020	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.
cord-151871-228t4ymc	2020	
cord-154170-7pnz98o6	2020	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.
cord-156676-wes5my9e	2020	
cord-158494-dww63e9f	2020	
cord-159103-dbgs2ado	2020	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.
cord-160382-8n3s5j8w	2020	
cord-161039-qh9hz4wz	2020	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
cord-162105-u0w56xrp	2020	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.
cord-162772-5jgqgoet	2020	
cord-163946-a4vtc7rp	2020	
cord-164964-vcxx1s6k	2020	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
cord-167889-um3djluz	2020	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
cord-168862-3tj63eve	2019	
cord-169288-aeyz2t6c	2020	
cord-171231-m54moffr	2020	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.
cord-174036-b3frnfr7	2020	
cord-174692-ljph6cao	2020	
cord-175015-d2am45tu	2020	
cord-175366-jomeywqr	2020	
cord-176131-0vrb3law	2020	
cord-176677-exej3zwh	2020	
cord-178783-894gkrsk	2020	
cord-182586-xdph25ld	2020	
cord-184685-ho72q46e	2020	
cord-185125-be11h9wn	2020	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.
cord-190495-xpfbw7lo	2020	
cord-191876-03a757gf	2020	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.
cord-193856-6vs16mq3	2020	
cord-195082-7tnwkxuh	2020	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
cord-195263-i4wyhque	2020	
cord-196353-p05a8zjy	2020	
cord-198449-cru40qp4	2020	
cord-203620-mt9ivgzi	2020	
cord-205559-q50vog59	2020	
cord-208252-e0vlaoii	2020	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.
cord-209221-vjfmxsks	2020	
cord-213974-rtltf11w	2020	
cord-214774-yro1iw80	2020	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.
cord-216208-kn0njkqg	2020	
cord-217139-d9q7zkog	2020	
cord-219817-dqmztvo4	2020	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.
cord-222868-k3k0iqds	2020	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.
cord-225347-lnzz2chk	2020	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.
cord-225429-pz9lsaw6	2014	
cord-225640-l0z56qx4	2020	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.
cord-229393-t3cpzmwj	2020	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.
cord-229937-fy90oebs	2020	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.
cord-230430-38fkbjq0	2020	
cord-232238-aicird98	2020	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.
cord-238342-ecuex64m	2020	
cord-240372-39yqeux4	2020	
cord-241057-cq20z1jt	2020	
cord-241351-li476eqy	2020	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.
cord-241596-vh90s8vi	2020	
cord-244657-zp65561y	2020	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.
cord-246317-wz7epr3n	2020	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.
cord-248050-apjwnwky	2020	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.
cord-250288-obsl0nbf	2020	
cord-252166-qah877pk	2007	
cord-252894-c02v47jz	2018	
cord-252903-pg0l92zb	2020	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.
cord-254107-02bik024	2004	
cord-254339-djmibi3a	2020	
cord-254729-hoa39sx2	2011	
cord-255557-k0xat0u7	2015	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.
cord-256289-rls5lr27	2020	
cord-258018-29vtxz89	2020	
cord-258316-uiusqr59	2020	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.
cord-258762-vabyyx01	2020	
cord-259426-qbolo3k3	2020	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.
cord-259534-hpyf0uj6	2020	
cord-260407-jf1dnllj	2004	
cord-260797-tc3pueow	2020	
cord-260966-9n23fjnz	2020	
cord-261530-vmsq5hhz	2020	
cord-261599-ddgoxape	2020	
cord-262524-ununcin0	2011	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.
cord-262966-8b1esll4	2020	
cord-263571-6i64lee0	2020	
cord-263606-aiey8nvq	2020	
cord-263620-9rvlnqxk	2020	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.
cord-263987-ff6kor0c	2017	
cord-264136-jjtsd4n3	2020	[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.
cord-264408-vk4lt83x	2017	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 .
cord-264994-j8iawzp8	2019	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.
cord-265299-oovkoiyj	2016	
cord-266090-f40v4039	2020	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
cord-266189-b3b36d72	2020	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.
cord-266424-wchxkdtj	2008	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.
cord-266593-hmx2wy1p	2020	
cord-266626-9vn6yt8m	2020	
cord-267150-hf0jtfmx	2020	
cord-267180-56wqok4c	2020	
cord-267890-j64x6f5r	2020	
cord-268142-lmkfxme5	2019	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
cord-268298-25brblfq	2014	
cord-268779-qbn3i2nq	2020	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.
cord-268959-wh28s0ws	2017	
cord-269212-oeu48ili	2020	
cord-269559-gvvnvcfo	2020	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 .
cord-269873-4hxwo5kt	2020	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.
cord-270249-miys1fve	2020	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.
cord-273429-dl6z8x9h	2020	
cord-273815-7ftztaqn	2020	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 ).
cord-274209-n0aast22	2019	
cord-274513-0biyfhab	2020	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 ).
cord-274732-mh0xixzh	2020	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
cord-275258-azpg5yrh	2019	
cord-275395-w2u7fq1g	2020	
cord-276218-dcg9oq6y	2020	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 .
cord-276782-3fpmatkb	2020	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.
cord-277128-g90hp8j7	2020	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 .
cord-277237-tjsw205c	2020	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
cord-278693-r55g26qw	2021	
cord-280064-rz8cglyt	2020	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.
cord-280640-0h3yv2m4	2020	
cord-280683-5572l6bo	2020	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.
cord-280722-glcifqyp	2020	
cord-281122-dtgmn9e0	2020	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).
cord-281543-ivhr2no3	2020	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
cord-283092-t3yqsac3	2020	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.
cord-283678-xdma6vyo	2020	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 ).
cord-283907-ev1ghlwl	2020	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.
cord-284617-uwby8r3y	2020	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.
cord-285435-fu90vb2z	2020	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.
cord-285774-hvuzxlna	2020	
cord-285897-ahysay2l	2020	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.
cord-287145-w518a0wa	2020	
cord-288183-pz3t29a7	2013	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.
cord-288303-88c6qsek	2020	
cord-288342-i37v602u	2015	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.
cord-289325-jhokn5bu	2016	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:
cord-289447-d93qwjui	2020	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.
cord-289496-d8ac6l6o	2020	
cord-289542-u86ujtur	2020	
cord-289917-2mxd7zxf	2020	
cord-290421-9v841ose	2020	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.
cord-290952-tbsccwgx	2020	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.
cord-291180-xurmzmwj	2020	
cord-292699-855am0mv	2020	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).
cord-293333-mqoml9o5	2020	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.
cord-293562-69nnyq8p	2018	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.
cord-293893-ibca88xu	2020	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.
cord-294586-95iwcocn	2020	
cord-295116-eo887olu	2020	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.
cord-295786-cpuz08vl	2020	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.
cord-296388-ayfdsn07	2020	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
cord-296560-ehrww6uu	2006	
cord-296565-apqm0i58	2020	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.
cord-296826-870mxd1t	2020	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) .
cord-297161-ziwfr9dv	2020	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.
cord-297517-w8cvq0m5	2020	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.
cord-297530-7zbvgvk8	2011	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.
cord-298646-wurzy88k	2012	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.
cord-299312-asc120pn	2020	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.
cord-299439-xvfab24g	2020	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.
cord-299852-t0mqe7yy	2020	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%).
cord-299932-c079r94n	2020	
cord-300570-xes201g7	2020	
cord-301117-egd1gxby	2013	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.
cord-301505-np4nr7gg	2006	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.
cord-302277-c66xm2n4	2004	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.
cord-302336-zj3oixvk	2020	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.
cord-303187-ny4qr2a2	2017	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.
cord-303651-fkdep6cp	2020	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).
cord-305318-cont592g	2019	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.
cord-307133-bm9z8gss	2016	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.
cord-307340-00m2g55u	2020	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.
cord-308115-bjyr6ehq	2020	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
cord-308219-97gor71p	2020	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.
cord-308302-5yns1hg9	2020	
cord-308652-i6q23olv	2020	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.
cord-309010-tmfm5u5h	2017	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.
cord-309096-vwbpkpxd	2020	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.
cord-309301-ai84el0j	2020	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
cord-310406-5pvln91x	2010	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.
cord-310844-7i92mk4x	2020	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.
cord-310863-jxbw8wl2	2020	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)).
cord-311086-i4e0rdxp	2020	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
cord-311432-js84ruve	2020	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.
cord-311868-40bri19f	2020	
cord-312366-8qg1fn8f	2020	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.).
cord-312911-nqq87d0m	2020	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.
cord-313046-3g2us5zh	2020	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.
cord-313279-15wii9nn	2014	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.
cord-315462-u2dj79yw	2020	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.
cord-315685-ute3dxwu	2020	
cord-316393-ozl28ztz	2020	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.
cord-317643-pk8cabxj	2020	
cord-317993-012hx4kc	2020	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] .
cord-318079-jvx1rh7g	2020	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.
cord-318187-c59c9vi3	2020	
cord-318562-jif88gof	2020	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.
cord-318900-dovu6kha	2020	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.
cord-319291-6l688krc	2006	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] .
cord-319378-li77za5e	2020	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.
cord-319436-mlitd45q	2020	
cord-319885-8qyavs7m	2021	
cord-319933-yp9ofhi8	2013	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.
cord-320141-892v3b7m	2020	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).
cord-320666-cmqj8get	2020	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.
cord-320914-zf54jfol	2020	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.
cord-320953-1st77mvh	2020	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.
cord-321715-bkfkmtld	2007	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.
cord-321735-c40m2o5l	2020	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.
cord-321852-e7369brf	2020	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.
cord-321984-qjfkvu6n	2020	
cord-322577-5bboc1z0	2020	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.
cord-322806-g01wmmbx	2020	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.
cord-323743-hr23ux58	2020	
cord-324230-nu0pn2q8	2020	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.
cord-324254-qikr9ryf	2020	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) .
cord-324924-5f7b02yq	2020	. 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.
cord-325321-37kyd8ak	2020	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.
cord-325738-c800ynvc	2020	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.
cord-325862-rohhvq4h	2020	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.
cord-326280-kjjljbl5	2020	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
cord-326314-9ycht8gi	2020	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.
cord-326409-m3rgspxc	2007	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
cord-326540-1r4gm2d4	2020	[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.
cord-326831-dvg0isgt	2020	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.
cord-326908-l9wrrapv	2015	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.
cord-327784-xet20fcw	2020	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
cord-328181-b2o05j3j	2020	
cord-329256-7njgmdd1	2011	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) .
cord-329276-tfrjw743	2020	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.
cord-329534-deoyowto	2020	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.
cord-330148-yltc6wpv	2016	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] .
cord-330474-c6eq1djd	2020	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.
cord-330596-p4k7jexz	2020	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.
cord-330668-7aw17jf8	2011	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.
cord-330714-hhvap8ts	2020	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
cord-330978-f3uednt5	2014	
cord-331374-3gau0vmc	2016	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.
cord-331376-l0o1weus	2009	
cord-331646-j5mkparg	2009	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) :
cord-331849-o346txxr	2020	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.
cord-332093-iluqwwxs	2016	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.
cord-332412-lrn0wpvj	2020	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.
cord-332583-5enha3g9	2020	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.
cord-332618-8al98ya2	2020	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.
cord-332729-f1e334g0	2020	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?
cord-332922-2qjae0x7	2020	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.
cord-333088-ygdau2px	2006	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.
cord-333490-8pv5x6tz	2020	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.
cord-333693-z2ni79al	2020	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
cord-333919-nrd9ajj2	2020	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.
cord-335418-s8ugu8e1	2020	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.
cord-335465-sckfkciz	2020	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).
cord-335689-8a704p38	2018	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 ).
cord-336644-kgrdul35	2020	
cord-336687-iw3bzy0m	2015	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.
cord-336747-8m7n5r85	2020	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.
cord-337897-hkvll3xh	2009	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 .
cord-337915-usi3crfl	2020	
cord-338466-7uvta990	2020	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) .
cord-339374-2hxnez28	2020	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.
cord-339649-ppgmmeuz	2020	
cord-340244-qjf23a7e	2020	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%.
cord-340354-j3xsp2po	2020	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.
cord-340375-lhv83zac	2020	
cord-340564-3fu914lk	2020	
cord-340713-v5sdowb7	2020	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.
cord-340805-qbvgnr4r	2020	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.
cord-340827-vx37vlkf	2011	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.
cord-342591-6joc2ld1	2020	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 .
cord-342855-dvgqouk2	2020	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, Ü´ 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.
cord-343701-x5rghsbs	2020	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.
cord-344115-gtbkwuqv	2020	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.
cord-344252-6g3zzj0o	2020	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.
cord-344417-1seb8b09	2020	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?
cord-346136-sqc09x9c	2020	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.
cord-346265-jx4kspen	2020	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.
cord-346309-hveuq2x9	2007	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).
cord-346921-3hfxv6h8	2020	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.
cord-347199-slq70aou	2020	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.
cord-347791-wofyftrs	2020	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
cord-347906-3ehsg8oi	2020	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
cord-347952-k95wrory	2012	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] .
cord-348010-m3a3utvz	2020	(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.
cord-350001-pd2bnqbp	2020	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.
cord-350240-bmppif8g	2020	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.
cord-350510-o4libq5d	2020	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.
cord-350603-ssen3q08	2018	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
cord-350870-a89zj5mh	2014	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.
cord-351411-q9kqjvvf	2015	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.
cord-352348-2wtyk3r5	2007	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.
cord-352431-yu7kxnab	2020	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:
cord-352543-8il0dh58	2020	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.
cord-353200-5csewb1k	2020	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.
cord-354254-89vjfkfd	2020	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.
cord-354627-y07w2f43	2020	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.
cord-355102-jcyq8qve	2020	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.