Differences in how interventions coupled with effective reproduction numbers account for marked variations in COVID-19 epidemic outcomes

Fan Xia; Yanni Xiao; Peiyu Liu; Robert A. Cheke; Xuanya Li; Fan Xia; Yanni Xiao; Peiyu Liu; Robert A. Cheke; Xuanya Li

doi:10.3934/mbe.2020274

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 5085-5098. doi: 10.3934/mbe.2020274

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Differences in how interventions coupled with effective reproduction numbers account for marked variations in COVID-19 epidemic outcomes

1.
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
2.
School of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, China
3.
Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent ME4 4B, UK
4.
Baidu Inc., Beijing 100094, China

Received: 05 June 2020 Accepted: 21 July 2020 Published: 27 July 2020

The COVID-19 outbreak, designated a "pandemic" by the World Health Organization (WHO) on 11 March 2020, has spread worldwide rapidly. Each country implemented prevention and control strategies, mainly classified as SARS LCS (SARS-like containment strategy) or PAIN LMS (pandemic influenza-like mitigation strategy). The reasons for variation in each strategy's efficacy in controlling COVID-19 epidemics were unclear and are investigated in this paper. On the basis of the daily number of confirmed local (imported) cases and onset-to-confirmation distributions for local cases, we initially estimated the daily number of local (imported) illness onsets by a deconvolution method for mainland China, South Korea, Japan and Spain, and then estimated the effective reproduction numbers R_t by using a Bayesian method for each of the four countries. China and South Korea adopted a strict SARS LCS, to completely block the spread via lockdown, strict travel restrictions and by detection and isolation of patients, which led to persistent declines in effective reproduction numbers. In contrast, Japan and Spain adopted a typical PAIN LMS to mitigate the spread via maintaining social distance, self-quarantine and isolation etc., which reduced the R_t values but with oscillations around 1. The finding suggests that governments may need to consider multiple factors such as quantities of medical resources, the likely extent of the public's compliance to different intensities of intervention measures, and the economic situation to design the most appropriate policies to fight COVID-19 epidemics.
- COVID-19 outbreak,
- effective reproduction number,
- prevention and control strategy
Citation: Fan Xia, Yanni Xiao, Peiyu Liu, Robert A. Cheke, Xuanya Li. Differences in how interventions coupled with effective reproduction numbers account for marked variations in COVID-19 epidemic outcomes[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5085-5098. doi: 10.3934/mbe.2020274

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Abstract

The COVID-19 outbreak, designated a "pandemic" by the World Health Organization (WHO) on 11 March 2020, has spread worldwide rapidly. Each country implemented prevention and control strategies, mainly classified as SARS LCS (SARS-like containment strategy) or PAIN LMS (pandemic influenza-like mitigation strategy). The reasons for variation in each strategy's efficacy in controlling COVID-19 epidemics were unclear and are investigated in this paper. On the basis of the daily number of confirmed local (imported) cases and onset-to-confirmation distributions for local cases, we initially estimated the daily number of local (imported) illness onsets by a deconvolution method for mainland China, South Korea, Japan and Spain, and then estimated the effective reproduction numbers R_t by using a Bayesian method for each of the four countries. China and South Korea adopted a strict SARS LCS, to completely block the spread via lockdown, strict travel restrictions and by detection and isolation of patients, which led to persistent declines in effective reproduction numbers. In contrast, Japan and Spain adopted a typical PAIN LMS to mitigate the spread via maintaining social distance, self-quarantine and isolation etc., which reduced the R_t values but with oscillations around 1. The finding suggests that governments may need to consider multiple factors such as quantities of medical resources, the likely extent of the public's compliance to different intensities of intervention measures, and the economic situation to design the most appropriate policies to fight COVID-19 epidemics.

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