Does climate help modeling COVID-19 risk and to what extent?

Results We designed a cross-national data-oriented study using global records of confirmed daily cases of COVID-19 to examine the association between the pandemic growth and climatic conditions, using several socioeconomic, environmental, and global health factors as control variables. We first apply Spearman’s and Kendall’s rank-order correlation analysis to the selected data to derive a first estimate of the statistical relationship between each explanatory variable and COVID-19 transmission at each geographic location under study. We then use machine learning techniques to model and understand the relative importance that climatic and control variables have on the spread of COVID-19. We finally use panel data econometric analysis to estimate the impact of climatic conditions on COVID-19 daily rates and test the efficacy of different containment policies. Statistical correlation analysis Fig 4 provides the distribution of Spearman’s rank correlation coefficients (ρ) that model the dependency of COVID-19 daily rates on the environmental and meteorological explanatory Fig 4. Spearman’s coefficient. https://doi.org/10.1371/journal.pone.0273078.g004 PLOS ONE Does climate help modeling COVID-19 risk and to what extent? PLOS ONE | https://doi.org/10.1371/journal.pone.0273078 September 7, 2022 12 / 27