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COVID-19 is a highly infectious disease and public health hazard that has been wreaking havoc around the world; thus, assessing and simulating the risk of the current pandemic is crucial to its management and prevention. The severe situation of COVID-19 around the world cannot be ignored, and there are signs of a second outbreak; therefore, the accurate assessment and prediction of COVID-19 risks, as well as the prevention and control of COVID-19, will remain the top priority of major public health agencies for the foreseeable future. In this study, the risk of the epidemic in Guangzhou was first assessed through logistic regression (LR) on the basis of Tencent-migration data and urban point of interest (POI) data, and then the regional distribution of high- and low-risk epidemic outbreaks in Guangzhou in February 2021 was predicted. The main factors affecting the distribution of the epidemic were also analyzed by using geographical detectors. The results show that the number of cases mainly exhibited a declining and then increasing trend in 2020, and the high-risk areas were concentrated in areas with resident populations and floating populations. In addition, in February 2021, the “Spring Festival travel rush” in China was predicted to be the peak period of population movement. The epidemic risk value was also predicted to reach its highest level at external transportation stations, such as Baiyun Airport and Guangzhou South Railway Station. The accuracy verification showed that the prediction accuracy exceeded 99%. Finally, the interaction between the resident population and floating population could explain the risk of COVID-19 to the highest degree, which indicates that the effective control of population agglomeration and interaction is conducive to the prevention and control of COVID-19. This study identifies and predicts high-risk areas of the epidemic, which has important practical value for urban public health prevention and control and containment of the second outbreak of COVID-19.

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography