A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics

Abstract

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.