COVID-19 distributes socially in China: A Bayesian spatial analysis

Abstract

Purpose The ongoing coronavirus disease 2019 (COVID-19) epidemic increasingly threatens the public health security worldwide. We aimed to identify high-risk areas of COVID-19 and understand how socioeconomic factors are associated with the spatial distribution of COVID-19 in China, which may help other countries control the epidemic. Methods We analyzed the data of COVID-19 cases from 30 provinces in mainland China (outside of Hubei) from 16 January 2020 to 31 March 2020, considering the data of demographic, economic, health, and transportation factors. Global autocorrelation analysis and Bayesian spatial models were used to present the spatial pattern of COVID-19 and explore the relationship between COVID-19 risk and various factors. Results Global Moran’s I statistics of COVID-19 incidences was 0.31 (PConclusion Our results suggested that COVID-19 risk was positively associated with the level of economic development and population movements. Blocking population movement and reducing local exposures are effective in preventing the local transmission of COVID-19.