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

variables used in this study. Correlation coefficients are calculated for each location and time series. Results are clustered based on the specific location and consider only geographical areas with at least 90 data records (at least 3 months of data). Correlation coefficients greater than 1.5 IQR (interquartile range) that are below the first quartile or above the third quartile are considered outliers and reported in Fig 4 as scatter points. Only locations that showed statisti- cal significance (P < 0.01) were considered and displayed in the descriptive analysis reported in Fig 4 (see S2 and S3 Tables for variable description and statistics). The Spearman’s rank correlation results suggest that solar irradiation and UV emission are strongly negatively correlated with COVID-19 spread (median ρ equal to -0.55 and -0.56, respectively). Temperature and absolute humidity also show a negative correlation, but with a weaker amplitude (at median values of -0.42 and 0.39, respectively; moderately correlated). The other meteorological and air-quality factors do not show a significant association with COVID-19 transmission (low |ρ| and large IQR: high standard deviation). PM2.5 concentra- tions and pressure register a positively weak correlation. All other variables present a weakly negative correlation. These results are corroborated by Kendall’s rank correlation analysis shown in Fig 5. Fig 5. Kendall’s coefficient. https://doi.org/10.1371/journal.pone.0273078.g005 PLOS ONE Does climate help modeling COVID-19 risk and to what extent?

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