The transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of the related disease (COVID-19) are influenced by a large number of factors. This study aimed to investigate the correlation of COVID-19 case and death rates with possible causal climatological and sociodemographic factors for the March to May 2020 (first wave) period in a worldwide scale by statistically processing data for over one hundred countries. The weather parameters considered herein were air temperature, relative humidity, cumulative precipitation, and cloud cover, while sociodemographic factors included population density, median age, and government measures in response to the pandemic. The results of this study indicate that there is a statistically significant correlation between average atmospheric temperature and the COVID-19 case and death rates, with chi-square test p -values in the 0.001–0.02 range. Regarding sociodemographic factors, there is an even stronger dependence of the case and death rates on the population median age ( p = 0.0006–0.0012). Multivariate linear regression analysis using Lasso and the forward stepwise approach revealed that the median age ranks first in importance among the examined variables, followed by the temperature and the delays in taking first governmental measures or issuing stay-at-home orders.
【저자키워드】 COVID-19, weather, sociodemographic factors, response measures, 【초록키워드】 SARS-CoV-2, coronavirus, pandemic, severity, Transmission, Factors, age, temperature, correlation, disease, First wave, death rates, death rate, chi-square test, Government, acute respiratory syndrome, COVID-19 case, median age, Linear regression analysis, average, P -value, multivariate, measure, cumulative, LASSO, parameter, Sociodemographic factor, approach, examined, statistically significant, statistically, atmospheric, the median, variables, 【제목키워드】 investigation, Factor, Importance,