Data-driven analysis of the simulations of the spread of COVID-19 under different interventions of China

Ting Tian, Jingwen Zhang, Shiyun Lin, Yukang Jiang, Jianbin Tan, Zhongfei Li and Xueqin Wang

Journal of Applied Statistics, 2023, vol. 50, issue 11-12, 2547-2560

Abstract: Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time.

Date: 2023
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DOI: 10.1080/02664763.2021.1895089

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