To explore potential risk factors of COVID-19, GIS (Geographic Information System) was used to visualize the geographic distributions of COVID-19 incidence in relation to sociodemographic factors including GDP, population density, distance to Wuhan, and health resources. The local GWPR model and global GLM Poisson regression model were compared to find the optimal fitting model exploring the association between the sociodemographic factors and COVID-19 incidence. Compared with the GLM Poisson regression model, the calibration of the GWPR model obviously improved in model fitting.
According to the GLM Poisson regression model and the GWPR model, results revealed that study cities with higher GDP might have an increasing risk for COVID-19. A recent study found that the rapid spread of COVID-19 around the world tended to appear first in the most economically developed regions where high-level international trade and commercial activities were prevalent. With initially spreading along the routes of international trade between the developed regions, the virus spread later to the developing parts [20]. In our study, the higher coefficient was observed in the midlands and northern cities in comparison with the southern cities of China in the GWPR model. A possible explanation for this phenomenon is that southern cities have more robust economies compared with the northern cities, while the improvement of economy might produce a more extensive and significant influence on northern cities, accordingly increasing the infection density of COVID-19 [21]. More detailed causes required further investigation.
As the distance to Wuhan increased in our study, the incidence of COVID-19 decreased among all of the cities based on the GLM Poisson regression model and the GWPR model. The spatial varying coefficients represented a decreasing trend from the southeast to the northwest in the GWPR model. Before officially sealing off Wuhan, more than 5 million people had left the city, which disenabled us to track where exactly these people go, and the distance to Wuhan could be used to in part represent this massive level of human movement. Obviously, cities positioned farther away from Wuhan enjoyed less or no access to contact with the infectious sources, which hindered the spread of COVID-19. On the contrary, in cities near Wuhan with convenient transportation and daily population movement, their residents had more opportunities to contact with the infectious sources, which will promote the COVID-19 to spread. Many studies had revealed the aggregation characteristics of the virus and reminded us of the importance of blocking the epidemic areas and isolating the infectious sources [22], which is consistent with our findings in this paper and other studies [23].
According to the GWPR model, the coefficients of health resources were negative in 342 cities and showed a degressive trend from the southeast to the northwest, indicating that the higher level of health resources might mitigate the epidemic of COVID-19. A higher level of health resources could contribute to identifying the sources of infection and enabling suspected patients and close contacts to gain better access to quarantine measures, which will prevent the spread of the epidemic and reduce the COVID-19 incidence. Many studies had also emphasized the importance of controlling the sources of infection and cutting off the routes of transmission [24]. However, it's worth noting that health resources were poorer in the western cities than the central and eastern cities of China. Previous reports had confirmed that the availability and accessibility of health resources in China had substantial regional disparities [25]. Since the outbreak of COVID-19, Chinese government had made great efforts to construct new medical facilities, mobilize the country’s large and robust medical forces and accelerate the delivery of medical supplies, the epidemic was quickly brought under control. Our study also suggested that the government should increase the input of medical and health resources in various regions for effectively controlling infectious diseases. The situation in China could provide guide to other countries on how to prepare for possible local outbreaks, especially for resource-limited countries [26].
From the GWPR model and the GLM Poisson regression model, results showed that the population density of study cities was negatively associated with the COVID-19 incidence. In the GWPR model, this effect decreased from the north with a lower population density to the south with a greater population density. The paradox was that the incidence of COVID-19 was higher in cities with lower population density. Considering the very special background of the virus spread in China, the possible reasons were as follows. Due to the particular period, i.e., the Spring Festival and Spring Transportation in China, the result was a considerable movement of people from the large cities to middle and small cities or even rural areas for family reunion. Therefore, many large cities are much less populated during this period. After the outbreak of COVID-19 in Wuhan, furthermore, many residents of some large cities, including Wuhan, take "evasive activity" to return small cities or even rural areas. During this particular period, the above two reasons might result in the more massive transmission risk of the COVID-19 in small cities or even rural areas than those in the big cities. A study from the US reported that household size, rather than overall population density, was more strongly associated with the prevalence of COVID-19 [27]. In another study, moreover, the population density was considered as a more effective predictor of COVID-19 infections and mortality for metropolitan areas, not for rural areas [28]. Thus, it is necessary to deeply explore the relationship between population density and the COVID-19 incidence.
Some limitations in the study should be acknowledged. First, the observed differences were subject to many unobserved confounding factors. For example, age, gender, nationality, and other natural factors were not available and thus could not be controlled in multivariate analysis. Because this research was based on surveillance data, second, the causal relationship between sociodemographic characteristics and the incidence of COVID-19 could not be demonstrated. Third, due to different policies and measures in response to COVID-19 in each country, the results in our study could not be extrapolated to other countries. To the best of our knowledge, nevertheless, this study is the first to combine the COVID-19 surveillance and sociodemographic data into GIS and analyze possible risk factors of COVID-19 incidence in China from the spatial perspective, filling a gap in the knowledge of this geographical region.