As of July 27, 2020, COVID-19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID-19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID-19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID-19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID-19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID-19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID-19 epidemic.
Keywords: COVID‐19; ESDA; GWR; Hubei province; environmental and meteorological factors; spatial autocorrelation.
© 2021. The Authors.