The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality.
Bibliographical noteFunding Information:
Funding: This research is partially supported by grants from the Hong Kong Research Grants Council (Grant No. 15602619; Grant no. C7064-18GF), and the Research Institute for Sustainable Urban Development (Grant No. 1-BBWD), the Hong Kong Polytechnic University. Mei-Po Kwan was supported by RGC grants (GRF Grant no. 14605920; CRF Grant no. C4023-20GF) and a grant from the Research Committee on Research Sustainability of Major RGC Funding Schemes of the Chinese University of Hong Kong.
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All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Computers in Earth Sciences
- Earth and Planetary Sciences (miscellaneous)