COVID-19 Infection and Mortality: Association with PM 2.5 Concentration and Population Density—An Exploratory Study

ABSTRACT

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. [ABSTRACT FROM AUTHOR] Copyright of ISPRS International Journal of Geo-Information is the property of MDPI Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)