The risk of future waves of COVID-19: modeling and data analysis

After a major outbreak of the coronavirus disease (COVID-19) starting in late December 2019, there were no new cases reported in mainland China for the first time on March 18, 2020, and no new cases reported in Hong Kong Special Administrative Region on April 20, 2020. However, these places had repo...

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Autores principales:	Sha He, Jie Yang, Mengqi He, Dingding Yan, Sanyi Tang, Libin Rong
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	covid-19 stochastic model second wave risk analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/e1b70ba4133a45678610ea065dee1195
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spelling	oai:doaj.org-article:e1b70ba4133a45678610ea065dee11952021-11-09T02:13:15ZThe risk of future waves of COVID-19: modeling and data analysis10.3934/mbe.20212741551-0018https://doaj.org/article/e1b70ba4133a45678610ea065dee11952021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021274?viewType=HTMLhttps://doaj.org/toc/1551-0018After a major outbreak of the coronavirus disease (COVID-19) starting in late December 2019, there were no new cases reported in mainland China for the first time on March 18, 2020, and no new cases reported in Hong Kong Special Administrative Region on April 20, 2020. However, these places had reported new cases and experienced a second wave since June 11, 2020. Here we develop a stochastic discrete-time epidemic model to evaluate the risk of COVID-19 resurgence by analyzing the data from the beginning of the outbreak to the second wave in these three places. In the model, we use an input parameter to represent a few potential risks that may cause a second wave, including asymptomatic infection, imported cases from other places, and virus from the environment such as frozen food packages. The effect of physical distancing restrictions imposed at different stages of the outbreak is also included in the model. Model simulations show that the magnitude of the input and the time between the initial entry and subsequent case confirmation significantly affect the probability of the second wave occurrence. Although the susceptible population size does not change the probability of resurgence, it can influence the severity of the outbreak when a second wave occurs. Therefore, to prevent the occurrence of a future wave, timely screening and detection are needed to identify infected cases in the early stage of infection. When infected cases appear, various measures such as contact tracing and quarantine should be followed to reduce the size of susceptible population in order to mitigate the COVID-19 outbreak.Sha He Jie YangMengqi HeDingding YanSanyi TangLibin RongAIMS Pressarticlecovid-19stochastic modelsecond waverisk analysisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5409-5426 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 stochastic model second wave risk analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	covid-19 stochastic model second wave risk analysis Biotechnology TP248.13-248.65 Mathematics QA1-939 Sha He Jie Yang Mengqi He Dingding Yan Sanyi Tang Libin Rong The risk of future waves of COVID-19: modeling and data analysis
description	After a major outbreak of the coronavirus disease (COVID-19) starting in late December 2019, there were no new cases reported in mainland China for the first time on March 18, 2020, and no new cases reported in Hong Kong Special Administrative Region on April 20, 2020. However, these places had reported new cases and experienced a second wave since June 11, 2020. Here we develop a stochastic discrete-time epidemic model to evaluate the risk of COVID-19 resurgence by analyzing the data from the beginning of the outbreak to the second wave in these three places. In the model, we use an input parameter to represent a few potential risks that may cause a second wave, including asymptomatic infection, imported cases from other places, and virus from the environment such as frozen food packages. The effect of physical distancing restrictions imposed at different stages of the outbreak is also included in the model. Model simulations show that the magnitude of the input and the time between the initial entry and subsequent case confirmation significantly affect the probability of the second wave occurrence. Although the susceptible population size does not change the probability of resurgence, it can influence the severity of the outbreak when a second wave occurs. Therefore, to prevent the occurrence of a future wave, timely screening and detection are needed to identify infected cases in the early stage of infection. When infected cases appear, various measures such as contact tracing and quarantine should be followed to reduce the size of susceptible population in order to mitigate the COVID-19 outbreak.
format	article
author	Sha He Jie Yang Mengqi He Dingding Yan Sanyi Tang Libin Rong
author_facet	Sha He Jie Yang Mengqi He Dingding Yan Sanyi Tang Libin Rong
author_sort	Sha He
title	The risk of future waves of COVID-19: modeling and data analysis
title_short	The risk of future waves of COVID-19: modeling and data analysis
title_full	The risk of future waves of COVID-19: modeling and data analysis
title_fullStr	The risk of future waves of COVID-19: modeling and data analysis
title_full_unstemmed	The risk of future waves of COVID-19: modeling and data analysis
title_sort	risk of future waves of covid-19: modeling and data analysis
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/e1b70ba4133a45678610ea065dee1195
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The risk of future waves of COVID-19: modeling and data analysis

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