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

Feature importance analysis Relative feature predictive value is computed by applying the Tree SHAP (SHapley Additive explanation for tree-based machine learning models) algorithm to a Gradient Boosted Regres- sion Tree (GBRT) model (see Methodology section for details). The GBRT model was trained on the whole dataset including all geographical locations independently from the number of its data records. Epidemiological, meteorological, socioeconomic, environmental, and global health indicator indices were used as explanatory variables, while the number of Covid-19 daily cases served as the dependent variable. See the Methodology section for a detailed defini- tion of the variables. S2 and S3 Tables present a brief description of the variables included in the study and their summary statistics. The results of feature importance analysis suggest that climate plays a meaningful role in modulating the dynamics of the COVID-19 pandemic, as shown in Fig 6 where feature impor- tance is ranked in terms of logarithmic mean absolute SHAP values. SHAP values relative to the average number of previous COVID-19 cases as a predictor for the current number of COVID-19 cases were computed but have been omitted in Figs 6 and 7 due to their obvious relevance in order to focus on the other variables. All meteorological and air quality factors score at similar levels of importance, showing that there is no dominant predictor. UV radia- tion is the meteorological factor with the greatest SHAP value, confirming the results of the

statistical analysis where UV radiation was the most highly correlated factor with COVID-19 cases. Socioeconomic, environmental, and global health indicator variables all show minor impacts other than population density, and the annual carbon dioxide emissions (a time- invariant proxy for the country’s overall air quality). Intervention and health system policies, described by the different OxCGRT indices, all score similar to or slightly lower than meteoro- logical factors. Although not included in Fig 6, the average number of previous COVID-19 cases results having the greatest impact (i.e. highest SHAP value) in line with results reported in the current literature on COVID-19 and other coronaviruses [80]. Fig 7 shows the intensity and polarity of specific SHAP variable values for each data point (a dot in the plot) with reference to the dependent variable (daily COVID-19 cases). The red- to-blue color scale indicates magnitude (high/red vs. low/blue). Position on the horizontal axis signals polarity (negative vs. positive). The resulting analysis suggests that higher UV radiation is significantly correlated with lower occurrence of COVID-19 cases (the dependent variable), while the other meteorological factors show a much weaker contribution. Temperature shows a weakly positive correlation, but the results are not consistent. Absolute humidity also displays a weakly positive correla- tion, despite the expected impact according to earlier studies. Rainfall appears to be negatively

correlated with the dependent variable, but there are too few observations for high rainfall val- ues in the dataset to properly confirm this result. All the other meteorological factors do not exhibit a distinct correlation directionality or significant impact. Among the intervention policies, both the OxCGRT stringency and containment & health indices display a weakly negative correlation suggesting that more stringent prevention mea- sures have been beneficial in mitigating the spread of COVID-19, at least for a significant number of countries. Policies on school closures, stay-at-home requirements, and testing reveal a somewhat lower weakly negative correlation. In order to provide a more detailed analysis, we have also compared feature importance analysis results of locations in the northern and southern hemispheres. As the SHAP values in S3 Fig show, the results for each hemisphere are in line with those of the global analysis (Fig 6). Meteorological conditions are still the more crucial factors when compared to intervention policies, with UV still among the most prominent features for both datasets. As shown in S4 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 14 / 27