Modelling the transmission and control of COVID-19 in Yangzhou city with the implementation of Zero-COVID policy

Juan Li; Wendi Bao; Xianghong Zhang; Yongzhong Song; Zhigui Lin; Huaiping Zhu; Juan Li; Wendi Bao; Xianghong Zhang; Yongzhong Song; Zhigui Lin; Huaiping Zhu

doi:10.3934/mbe.2023703

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 15781-15808. doi: 10.3934/mbe.2023703

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Research article Special Issues

Modelling the transmission and control of COVID-19 in Yangzhou city with the implementation of Zero-COVID policy

1.
School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
College of Science, China University of Petroleum, Qingdao 266580, China
3.
School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
4.
Jiangsu Key Laboratory for NSLSCS, Institute of Mathematics School of Mathematics Science Nanjing Normal University, Nanjing 210023, China
5.
School of Mathematical Science, Yangzhou University, Yangzhou 225002, China
6.
LAMPS and Center for Diseases Modeling (CDM), Department of Mathematics and Statistics, York University, Toronto M3J 1P3, ON, Canada

Academic Editor: Xiang-Sheng Wang

Received: 02 June 2023 Revised: 18 June 2023 Accepted: 25 June 2023 Published: 31 July 2023

In the fight against the COVID-19 pandemic, China has long adhered to the "Dynamic Zero COVID-19" strategy till the end of 2022. To understand the mechanism of this strategy, we used the case of the Yangzhou summer outbreak in 2021 and a multi-stage dynamical model incorporating city-wide and key area testing-trace-isolation (TTI) strategies. We defined two time-varying indexes for measuring the disease transmission risk and the public health prevention and control force, respectively, which allowed us to explore the mechanisms of TTI policies. Integrating with the historical data and literature parameter values, we first estimated the parameters and then quantified the relevant indexes over time. The findings showed that multiple rounds of rapid testing were one of the critical measures to overcome the outbreak in Yangzhou within one month. In addition, we compared the impact of the duration of the free transmission stage, tracking rate, testing interval and precise division of key areas on the epidemiological indicators, including the final sizes of infections and isolations, peak value, peak arrival time and epidemic duration and the minimum round of testing. Our results suggest that the early detection of the epidemic, an improved efficiency of tracking, and a reduced duration of each test play a positive role in restraining COVID-19; however, a considerable investment of resources was essential to achieve a significant effect quickly.
Citation: Juan Li, Wendi Bao, Xianghong Zhang, Yongzhong Song, Zhigui Lin, Huaiping Zhu. Modelling the transmission and control of COVID-19 in Yangzhou city with the implementation of Zero-COVID policy[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15781-15808. doi: 10.3934/mbe.2023703

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Abstract

In the fight against the COVID-19 pandemic, China has long adhered to the "Dynamic Zero COVID-19" strategy till the end of 2022. To understand the mechanism of this strategy, we used the case of the Yangzhou summer outbreak in 2021 and a multi-stage dynamical model incorporating city-wide and key area testing-trace-isolation (TTI) strategies. We defined two time-varying indexes for measuring the disease transmission risk and the public health prevention and control force, respectively, which allowed us to explore the mechanisms of TTI policies. Integrating with the historical data and literature parameter values, we first estimated the parameters and then quantified the relevant indexes over time. The findings showed that multiple rounds of rapid testing were one of the critical measures to overcome the outbreak in Yangzhou within one month. In addition, we compared the impact of the duration of the free transmission stage, tracking rate, testing interval and precise division of key areas on the epidemiological indicators, including the final sizes of infections and isolations, peak value, peak arrival time and epidemic duration and the minimum round of testing. Our results suggest that the early detection of the epidemic, an improved efficiency of tracking, and a reduced duration of each test play a positive role in restraining COVID-19; however, a considerable investment of resources was essential to achieve a significant effect quickly.

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