About: A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy

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About: A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy
Creator	Sun, Wei Hyman, James Miller, Joel Estrada-Franco, Guillermo Jing, Shuanglin Li, Huafeng Xue, Ling Zhu, Huaiping
source	ArXiv
abstract	The ongoing Coronavirus Disease 2019 (COVID-19) pandemic threatens the health of humans and causes great economic losses. Predictive modelling and forecasting the epidemic trends are essential for developing countermeasures to mitigate this pandemic. We develop a network model, where each node represents an individual and the edges represent contacts between individuals where the infection can spread. The individuals are classified based on the number of contacts they have each day (their node degrees) and their infection status. The transmission network model was respectively fitted to the reported data for the COVID-19 epidemic in Wuhan (China), Toronto (Canada), and the Italian Republic using a Markov Chain Monte Carlo (MCMC) optimization algorithm. Our model fits all three regions well with narrow confidence intervals and could be adapted to simulate other megacities or regions. The model projections on the role of containment strategies can help inform public health authorities to plan control measures.
has issue date	2020-05-28(xsd:dateTime)
has license	arxiv
sha1sum (hex)	9cf221e601accd8ab6e8f272bd088d45cc776775
resource representing a document's title	A Data-Driven Network Model for the Emerging COVID-19 Epidemics in Wuhan, Toronto and Italy
resource representing a document's body	covid:9cf221e601accd8ab6e8f272bd088d45cc776775#body_text
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