Effect of Population Migration and Socioeconomic Factors on the COVID-19 Epidemic at County Level in Guangdong, China

Abstract

Coronavirus disease 2019 (COVID-19) has become a major public health concern worldwide. In this study, we aimed to analyze spatial clusters of the COVID-19 epidemic and explore the effects of population emigration and socioeconomic factors on the epidemic at the county level in Guangdong, China. Data on confirmed cases, population migration, and socioeconomic factors for 121 counties were collected from 1 December 2019 to 17 February 2020, during which there were a total of 1,328 confirmed cases. County-level infected migrants of Guangdong moving from Hubei were calculated by integrating the incidence rate, population migration data of Baidu Qianxi, and the resident population. Using the spatial autocorrelation method, we identified high-cluster areas of the epidemic. We also used a geographical detector to explore infected migrants and socioeconomic factors associated with transmission of COVID-19 in Guangdong. Our results showed that: 1) the epidemic exhibited significant positive global spatial autocorrelation; high–high spatial clusters were mainly distributed in the Pearl River Estuary region; 2) city-level population migration data corroborated with the incidence rate of each city in Hubei showed significant association with confirmed cases; 3) in terms of potential factors, infected migrants greatly contributed to the spread of COVID-19, which has strong ability to explain the COVID-19 epidemic; besides, the companies, transport services, residential communities, restaurants, and community facilities were also the dominant factors in the spread of the epidemic; 4) the combined effect produced by the intersecting factors can increase the explanatory power. The infected migrant factor interacted strongly with the community facility factor with the q value of 0.895. This indicates that the interaction between infected migrants and community facilities played an important role in transmitting COVID-19 at the county level.