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A stochastic discrete-time co-evolution model and its application in simulating the impacts of behavior changes on the secondary and multiple outbreaks of the COVID-19 pandemic

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Abstract

In this paper, we propose a new stochastic discrete-time multi-scale model to quantify the co-evolution of behavior changes and COVID-19. In this model, initial values and parameters of behavior change are used to describe individual behavior before and during the epidemic, and diagnosis rate parameters are used to describe government policies. In order to comprehensively reflect the characteristics of the epidemic in China, the epidemic data of three representative cities were selected for analysis and research based on the principles of traffic status, epidemic outbreak degree and epidemic data distribution. Firstly, the sensitivity analysis of parameters obtained from data fitting of three cities was carried out, respectively. It was found that different cities have different parameter sensitivities, but the sensitivity of initial value of behavior change was the strongest in all the three cities. Secondly, control measures such as phased unlocking were added to observe the impact of these control measures on secondary outbreaks based on behavior changes. Finally, considering that the Chinese government has fully relaxed the control of the epidemic, we further simulated the epidemic curve with only behavior changes and no government policies, and found that under the same behavior change, the epidemic curve in places with more severe outbreaks will change more, revealing the role of behavior change in secondary and multiple outbreaks.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: The data of daily confirmed cases linked to COVID-19 in Yangzhou, Nanjing and Zhengzhou from Health Commission of Jiangsu Province, Nanjing City and Henan Province (Yangzhou: http://wjw.jiangsu.gov.cn/art/2021/7/29/art_7290_9954475.html, Nanjing: http://wjw.nanjing.gov.cn/njswshjhsywyh/202108/t20210801_3089917.html, Zhengzhou: http://wsjkw.henan.gov.cn/ztzl/xxgzbdfyyqfk/yqtb/) and from the corresponding author on request].

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFCs 12031010, 12126350).

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Authors and Affiliations

  1. Department of Mathematics, Shanghai University, Shanghai, 201900, People’s Republic of China

    Jie Yang & Jie Lou

  2. School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062, People’s Republic of China

    Sanyi Tang

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  1. Jie Yang
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Appendices

Appendices

See Tables 2, 3, and 4.

Table 2 The probability of the secondary outbreak in Yangzhou from the first day to the 31st day after the case clearance varied in different proportions among the susceptible population depending on individual behavior. Data with probability values less than 0.05 are indicated in bold
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Table 3 The probability of the secondary outbreak in Nanjing from the first day to the 31st day after the case clearance varied in different proportions among the susceptible population depending on individual behavior. Data with probability values less than 0.05 are indicated in bold
Full size table
Table 4 The probability of the secondary outbreak in Zhengzhou from the first day to the 31st day after the case clearance varied in different proportions among the susceptible population depending on individual behavior. Data with probability values less than 0.05 are indicated in bold
Full size table

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Yang, J., Tang, S. & Lou, J. A stochastic discrete-time co-evolution model and its application in simulating the impacts of behavior changes on the secondary and multiple outbreaks of the COVID-19 pandemic. Eur. Phys. J. Plus 138, 1086 (2023). https://doi.org/10.1140/epjp/s13360-023-04702-x

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