Zhang Dongyan, Yang Wuyue, Wen Wanqi, Peng Liangrong, Zhuge Changjing, Hong Liu
School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, 510275, PR China.
Department of Mathematics, School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, PR China.
Heliyon. 2024 Jun 29;10(13):e33850. doi: 10.1016/j.heliyon.2024.e33850. eCollection 2024 Jul 15.
By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.
通过收集127个国家/地区在2020年7月2日之前第一波新冠疫情期间采取的各种防控政策，我们结合多元线性回归、基于神经网络的非线性回归以及敏感性分析，对这些政策对疫情动态的影响进行了定量评估。我们观察到这些国家在公共卫生政策方面存在显著差异，这些差异在很大程度上影响了传播速度和感染人数规模。一些关键的动态特征，如第100天的确诊/治愈/死亡病例归一化累积数以及半衰期，与防控措施呈现出具有统计学意义的线性相关性，从而证实了这些措施的显著影响。最重要的是，我们通过使用并行运行的多个中介变量的结构方程模型，对一般房室模型的代表SEIR-QD模型进行了中介效应分析。据我们所知，这在流行病学领域尚属首次。结果证实，SEIR-QD模型的感染率和保护率在防控措施与疫情动态特征之间呈现出具有统计学意义的中介效应。从第2类防控措施通过感染率到四个动态特征的路径上的中介效应，突出了核酸检测和疑似病例追踪在遏制疫情传播中的关键作用。我们基于数据的分析为深入了解公共卫生政策的有效性与新冠疫情动态特征之间的内在关联提供了见解。
