About: COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios

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About: COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios
Creator	Wei, Wei Zhang, Yong Jin, Bin Sun, Hongguang Tick, Geoffrey Yu, Xiangnan
Source	ArXiv
abstract	The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the transmission/infection and recovery dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed in the last three months (till 3/22/2020) in various provinces and metropolises in China. The model results revealed the space dependent transmission/infection rate and the significant spatiotemporal variation 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. The validated SEIR model was then applied to predict COVID-19 evolution in United States, Italy, Japan, and South Korea which have responded differently to monitoring and mitigating COVID-19 so far, although these predictions contain high uncertainty due to the intrinsic change of the maximum infected population and the infection/recovery rates within the different countries. In addition, a stochastic model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the stochastic model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread, if not all of the infected people can be promptly diagnosed and quarantined.
has issue date	2020-03-31(xsd:dateTime)
has license	arxiv
sha1sum (hex)	af9fa74295a96130db158a3e963623671a079286
resource representing a document's title	COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios
resource representing a document's body	covid:af9fa74295a96130db158a3e963623671a079286#body_text
is schema:about of	named entity 'social distancing' named entity 'systems' named entity 'pandemic' named entity 'South Korea' named entity 'Modeling' named entity 'FIT' named entity 'DIFFERENT' named entity 'INFECTED POPULATION' named entity 'SLOWING' named entity 'SCHEME' named entity 'PEOPLE' named entity 'APPLIED' named entity 'RECOVERED' named entity 'CITY' named entity 'space' named entity 'infection' named entity 'mitigate' named entity 'United States' named entity 'mitigating' named entity 'stochastic model' named entity 'evaluate' named entity 'particle' named entity 'infection' named entity 'obscure' named entity 'stochastic model' named entity 'evolution' named entity 'China' named entity 'Japan' named entity 'self-quarantine' named entity 'COVID' named entity 'evolution' named entity 'pharmaceutical' named entity 'COVID' named entity 'infection' named entity 'severe acute respiratory syndrome coronavirus 2' named entity 'China' named entity 'probability' named entity 'PDE' named entity 'social distance' named entity 'SARS-CoV-2' named entity 'COVID' named entity 'COVID-19 outbreak' named entity 'PDE' named entity 'COVID' named entity 'national quarantine' named entity 'COVID' named entity 'PDE' named entity 'PDE' named entity 'COVID-19' named entity 'U.S.' named entity 'SEIR' named entity 'China' named entity 'COVID-19 outbreak' named entity 'Brownian motion' named entity 'Shanghai City' named entity 'COVID-19 outbreak' named entity 'partial differential equation' named entity '5.1' named entity 'best-fit' named entity 'incubation period' named entity 'COVID-19' named entity 'contact tracing' named entity 'Self-quarantine' named entity 'lockdown' named entity 'South Korea' named entity 'infection' named entity 'coronavirus cases' named entity 'SIR model' named entity 'COVID-19' named entity 'evolution' named entity 'virus' named entity 'reaction rate' named entity 'China' named entity 'stochastic model' named entity 'SEIR' named entity 'Shanghai' named entity 'stochastic model' named entity 'COVID' named entity 'Japan' named entity 'U.S.' named entity 'random walk' named entity 'virus'

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