The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate

Hui Hu; Shuaizhou Xiong; Xiaoling Zhang; Shuzhou Liu; Lin Gu; Yuqi Zhu; Dongjin Xiang; Martin Skitmore

doi:10.1016/j.heliyon.2023.e14533

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate

Heliyon. 2023 Mar;9(3):e14533. doi: 10.1016/j.heliyon.2023.e14533. Epub 2023 Mar 16.

Authors

Hui Hu^{1

2

3}, Shuaizhou Xiong³, Xiaoling Zhang^{4

5

6}, Shuzhou Liu⁷, Lin Gu^{8

9}, Yuqi Zhu³, Dongjin Xiang⁷, Martin Skitmore¹⁰

Affiliations

¹ Economic Development Research Centre, Wuhan University, Hubei, China.
² Health Economics and Management Centre, Wuhan University, Hubei, China.
³ School of Economics & Management, Wuhan University, Hubei, China.
⁴ Department of Public and International Affairs, City University of Hong Kong, Kowloon, Hong Kong.
⁵ School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong.
⁶ Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China.
⁷ School of Mathematics and Physics, China University of Geosciences, Hubei, China.
⁸ RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan.
⁹ Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.
¹⁰ Bond University, Brisbane, Queensland, Australia.

Abstract

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

Keywords: COVID-19; Delayed-rejection adaptive metropolis method; Dynamic social contact rate; Epidemic control; General dynamic model; Restriction policy.