Modeling outbreaks of COVID-19 in China: The impact of vaccination and other control measures on curbing the epidemic

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%–60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

本研究旨在研判中国新型冠状病毒肺炎（COVID-19）的流行发展趋势，并构建模型以评估各类防控措施在抗击新冠疫情中的作用效果。本研究采用2020年1月2日至2022年1月2日由中国国家卫生健康委员会通报的新冠疫情数据，构建了易感-暴露-感染-无症状-隔离-接种疫苗-住院-移除（SEIAQVHR）模型，用于计算新冠病毒传播率与有效再生数Rt，并评估各项防控措施的效果。此外，本研究还构建了随机模型以探究新冠疫情的流行态势，并对2020年至2022年间的5次疫情暴发的发病趋势进行了建模分析。基于SEIAQVHR模型得到的估算结果，能够反映新冠疫情流行的若干关键特征。本模型结果显示，1例输入社区的感染指示病例引发新冠疫情暴发的概率为50%至60%。在各类防控措施中，佩戴口罩与接种疫苗的防控效果最为显著。仅针对无症状感染者的防控策略，对新冠病毒的传播并未产生显著影响。通过调整防控参数开展模拟分析可知，提升有效疫苗接种率与口罩佩戴率，可显著降低中国境内的新冠确诊病例数。本研究构建的随机模型分析框架，可为研判中国境内的新冠疫情形势提供有效工具。