Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China

As a public health emergency, the COVID‐19 pandemic has attracted widespread attention from scholars worldwide. Combining social network models, GIS analysis and spatial econometric models, we explored the characteristics of the Wuhan population outflow network and factors affecting the number of COVID‐19 cases. The results show that the Wuhan population outflow network has strong temporal and spatial heterogeneity. Cities in Hubei Province, central cities such as Beijing and Shanghai, and cities rich in tourism resources were the main destinations of Wuhan’s population inflow. The distribution of COVID‐19 cases not only showed a strong spatial autocorrelation but also a hierarchical diffusion effect. The benchmark regression results showed that the population outflow from Wuhan determines the number of COVID‐19 cases in other cities. Temperature was negatively correlated with the number of COVID‐19 cases, while the PM2.5 concentration failed the significance test. Thus, the lower is the temperature, the greater are the survival and spread of the virus facilitated. Furthermore, cities with a higher population density and more employees in the middle reaches of the Yangtze River are more vulnerable to COVID‐19. Finally, by replacing the weight matrix and setting instrumental variables, we proved the robustness of the above main conclusions.