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The spatiotemporal transmission dynamics of COVID-19 among multiple regions: a modeling study in Chinese provinces
Nonlinear Dynamics ; : 1-15, 2021.
Article in English | EuropePMC | ID: covidwho-1489730
ABSTRACT
Current explosive outbreak of COVID-19 around the world is a complex spatiotemporal process with hidden interactions between viruses and humans. This study aims at clarifying the transmission patterns and the driving mechanism that contributed to the COVID-19 prevalence across the provinces of China. Thus, a new dynamical transmission model is established by an ordinary differential system. The model takes into account the hidden circulation of COVID-19 virus among/within humans, which incorporates the spatial diffusion of infection by parameterizing human mobility. Theoretical analysis indicates that the basic reproduction number is a unique epidemic threshold, which can unite infectivity in each region by human mobility and can totally determine whether COVID-19 proceeds among multiple regions. By validating the model with real epidemic data in China, it is found that (1) if without any intervention, COVID-19 would overrun China within three months, resulting in more than 1.1 billion clinical infections and 0.2 billion subclinical infections;(2) high frequency of human mobility can trigger COVID-19 diffusion across each province in China, no matter where the initial infection locates;(3) travel restrictions and other non-pharmaceutical interventions must be implemented simultaneously for disease control;and (4) infection sites in central and east (rather than west and northeast) of China would easily stimulate quick diffusion of COVID-19 in the whole country. Supplementary Information The online version supplementary material available at 10.1007/s11071-021-07001-1.
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Collection: Databases of international organizations Database: EuropePMC Language: English Journal: Nonlinear Dynamics Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: EuropePMC Language: English Journal: Nonlinear Dynamics Year: 2021 Document Type: Article