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

enable a faster control on the containment of viral transmission. The R2 coefficient of 0.73 over 19,289 observations indicates a high level of correlation for this regression model. The robustness of our results is corroborated by the fact that both polarity and magnitude of the regression coefficients for the climate variables are still in line with statistical correlation and feature importance results, despite having developed the regression model with a smaller pool of data and additional factors. Results in Tables 4 and 5 are presented in tabular format by listing the values of the inter- cept (constant) and the β coefficients with their standard error for each regressor under the different hypotheses of duration relative to various moving average window sizes (T). Discussion Table 6 summarizes the results about the impact of climate factors on COVID-19 transmission from the three types of analysis carried out in this study (statistical, machine learning, and econometric analyses). Scores are color-coded to indicate the positive (red), negative (blue), or Table 4. Panel data fixed-effects model. Dependent variable: (1) (2) (3) (4) (5) Daily cases (log) T = 5 T = 7 T = 10 T = 12 T = 14 Days_from_start 0.006��� 0.006��� 0.006��� 0.006��� 0.006��� (0.00) (0.00) (0.00) (0.00) (0.00) Temperature_T 0.022��� 0.025��� 0.028��� 0.030��� 0.032��� (0.01) (0.01) (0.01) (0.01) (0.01) Absolute Humidity_T -0.011 -0.012 -0.013 -0.015 -0.016 (0.02) (0.02) (0.02) (0.02) (0.02) Pressure_T -0.004 -0.004 -0.005 -0.005 -0.005

(0.00) (0.00) (0.01) (0.01) (0.01) Windspeed_T -0.031 -0.036 -0.040 -0.043 -0.046 (0.02) (0.02) (0.03) (0.03) (0.04) Rainfall_T -0.010�� -0.009 -0.008 -0.007 -0.006 (0.00) (0.01) (0.01) (0.01) (0.01) Shortwave Irradiation_T -0.000 -0.000 -0.000 -0.000 -0.000 (0.00) (0.00) (0.00) (0.00) (0.00) PM 2.5_T -0.014��� -0.016��� -0.018��� -0.019��� -0.020��� (0.00) (0.00) (0.00) (0.00) (0.01) PM 10_T 0.003��� 0.003��� 0.004��� 0.004��� 0.004��� (0.00) (0.00) (0.00) (0.00) (0.00) UV_T -0.492��� -0.543��� -0.607��� -0.634��� -0.657��� (0.09) (0.11) (0.13) (0.14) (0.15) Constant 7.283�� 7.648�� 8.128�� 8.473� 8.860�� (3.12) (3.51) (4.06) (4.37) (4.69) Observations 65,369 65,369 65,369 65,369 65,369 R2 0.734 0.735 0.736 0.736 0.737 Adjusted R2 0.733 0.734 0.735 0.735 0.736 T-days moving average. Standard errors in parentheses are clustered at location (country/region) level, � p < 0.10, �� p < 0.05, ��� p < 0.01. https://doi.org/10.1371/journal.pone.0273078.t004 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 19 / 27