Causal Analysis of Impact Factors of COVID-19 in China

Mobility, group awareness, and temperature are considered as the important factors that may impact the increase in confirmed cases of the COVID-19^[1]. This paper aims to verify the above factors on the COVID-19 and show the possible confounding factors of each research variable in reality. Based on this, we collected data about the epidemic from January 20, 2020 to February 24, 2021, including the relevant data of 31 provinces and regions in China. Plus, we use the directed acyclic graph (DAG)^[2] to show the causal relationship between the above influencing factors and the confirmed daily epidemic cases, and the confounding is estimated based on DAG. The effective adjustment set of factors are used to perform the regression of the total causal effect among the explanatory variables and the confirmed cases of the epidemic using negative binomial regression. Through the comprehensive causal analysis of the decisive factors for the COVID-19, we provide strong evidence for population mobility, group awareness and the impact of weather on the epidemic, and estimates the possible confounding factors in all aspects of society. Incorporating the above factors, we provide suggestions for future decisions on the prevention of large-scale epidemics.