Analysis of the Spatiotemporal Variations and Influencing Factors of COVID-19 in Various Cities in East China: Bayesian Spatio-Temporal Model

Abstract

Since 2019, COVID-19 has spread worldwide in a pandemic and caused enormous losses. Even with numerous studies focusing on the spatiotemporal analysis of COVID-19, a noticeable research gap exists as Bayesian spatiotemporal models have not been extensively utilized to analyze the localized spatial patterns of COVID-19 in China. The purpose of this study is to analyze the temporal and spatial patterns of COVID-19 in East China while delving into the impact of socioeconomic factors on the incidence of the virus. Employing a Bayesian spatial-temporal hierarchical model, this paper thoroughly examines the case data spanning 24 months from various cities in East China. The results show that location and time have significant effects, and the two interact. From a spatial perspective, the cities with the highest relative risk are Shanghai and Xiamen. In terms of time, the relative risk of onset is highest in January and February 2020, and remains stable in other time periods. It shows that since 2020, the prevention and control measures to reduce population mobility and home isolation in China are effective.