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

Sha He; Jie Yang; Mengqi He; Dingding Yan; Sanyi Tang; Libin Rong; Sha He; Jie Yang; Mengqi He; Dingding Yan; Sanyi Tang; Libin Rong

doi:10.3934/mbe.2021274

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 5409-5426. doi: 10.3934/mbe.2021274

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

1.
School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, China
2.
Department of Mathematics, University of Florida, Gainesville, 32611, USA

Received: 09 April 2021 Accepted: 11 June 2021 Published: 18 June 2021

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.
- COVID-19,
- stochastic model,
- second wave,
- risk analysis
Citation: Sha He, Jie Yang, Mengqi He, Dingding Yan, Sanyi Tang, Libin Rong. The risk of future waves of COVID-19: modeling and data analysis[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5409-5426. doi: 10.3934/mbe.2021274

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Abstract

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.

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