Analysis of Factors Influencing the Spatial Distribution of Provincial Cumulative Confirmed Count of Novel Coronavirus Pneumonia (COVID-19) in China

摘要

In early 2020, the COVID-19 outbreak was gripping the nation. In this paper, spatial statistics were used to analyze the potential social causes affecting the spatial distribution of provincial cumulative confirmed count of COVID-19 in China. The analysis results showed that the number of COVID-19 patients diagnosed in each province in China was significantly positively correlated with the number of elderly people. The more the number of elderly people, the more the corresponding number of COVID-19 patients diagnosed. Meanwhile, the higher the total population, the smaller the proportion of the total number of COVID-19 diagnosed, which is negatively correlated. However, COVID-19 is highly contagious and has a long incubation period, so it still needs special attention. At the same time, it was found that the proportion of people moving out of Hubei province also had a positive impact on the number of COVID-19 cases, which is reflected in people who come into contact with people from Hubei are more susceptible to COVID-19. For the spatial statistical analysis of the cumulative number of COVID-19 confirmed cases, the spatial durbin model is superior to the ordinary least-squares regression model.