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

when stringency and the containment & health are above 60% (median value) and 0 otherwise. We separately test the lagged effect of these two factors at 7 and 14 days as expressed by the numerical suffix associated with the two variables in Table 5. We find that high levels of clo- sure-type restrictions show significant effects on limiting COVID-19 spread only after about two weeks from their introduction (P < 0.01). Conversely, the containment & health index presents a strong negative regression coefficient at both 7 and 14 days from introduction (P < 0.01). This may be related to the added efficacy of combining different health and preven- tion policies (public info campaigns, PCR testing, contact tracing, and facial coverings) to Table 3. Results of the Fisher-type unit-root test analysis based on augmented Dickey-Fuller tests at 0 and 1 lag for each variable considered in the econometric study. I(0) 0 Lags I(1) 1 Lag Inverse chi- squared Inverse normal Inverse logit t Modified inv. chi- squared Inverse chi- squared Inverse normal Inverse logit t Modified inv. chi- squared P Z L� Pm P Z L� Pm Daily cases (log) 9101.3 -74.1 -106.8 171.2 5631.1 -53.5 -67.5 101.6 Temperature_7 1868.7 -14.4 -14.2 16.5 3157.1 -33.9 -35.7 47.0 Absolute Humidity_7 1912.3 -15.2 -14.8 17.4 3546.0 -36.7 -40.4 55.6 Pressure_7 3112.6 -29.9 -31.9 43.1 8835.9 -69.5 -106.1 172.4 Wind speed_7 4163.5 -40.5 -46.5 65.6 7312.3 -62.4 -87.8 138.8 Rainfall_7 4974.1 -43.9 -54.1 82.9 6797.6 -57.4 -80.1 127.4 Short-wave irradiation_7

2599.7 -26.5 -26.9 32.2 3156.0 -34.0 -36.0 47.0 PM2P5_7 3763.2 -36.7 -40.9 57.1 8004.9 -65.7 -96.0 154.0 PM10_7 3765.8 -36.2 -40.9 57.1 7778.1 -63.4 -92.9 149.0 UV_7 2516.5 -25.4 -25.7 30.4 3184.9 -33.6 -36.2 47.7 High_stringency_7 3333.0 -40.3 -50.0 73.7 2805.7 -36.5 -43.6 62.9 High_containment_7 4123.7 -45.5 -59.62 90.4 3302.7 -39.9 -50.2 74.4 For each test, we report inverse chi-squared, inverse normal, inverse logit t, and modified https://doi.org/10.1371/journal.pone.0273078.t003 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 18 / 27

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

undetermined (black) polarity of variables correlated with COVID-19 transmission. Relative ranking per analytic method is indicated by integers enclosed within parentheses. For the sta- tistical analysis, we report the median magnitude of Spearman’s coefficient, and we rank the results based on their absolute value. For the machine learning analysis, we relay feature importance and its ranking as the mean absolute SHAP value. For the econometric analysis, we look at the significance of each estimated regression coefficient and we rank them based on their magnitude normalized with reference to their standard error. As discussed in the Table 5. Panel data fixed-effects model—testing the effect of restrictions. Dependent variable: (1) (2) (3) (4) Daily cases (log) days_from_start 0.006��� 0.006��� 0.006��� 0.006��� (0.00) (0.00) (0.00) (0.00) Temperature_7 0.024��� 0.020��� 0.022��� 0.017��� (0.01) (0.01) (0.01) (0.01) Absolute Humidity_7 -0.028 -0.023 -0.028 -0.021 (0.02) (0.02) (0.02) (0.02) Pressure_7 -0.024��� -0.025��� -0.025��� -0.023��� (0.01) (0.00) (0.00) (0.00) Wind speed_7 0.030 0.030 0.029 0.031 (0.02) (0.02) (0.02) (0.02) Rainfall_7 -0.013 -0.010 -0.013 -0.013 (0.01) (0.01) (0.01) (0.01) Short-wave irradiation_7 -0.000 -0.000 -0.000 -0.000 (0.00) (0.00) (0.00) (0.00) PM2P5_7 0.002 0.004 0.001 0.001 (0.01) (0.01) (0.01) (0.01) PM10_7 -0.003 -0.002 -0.004 -0.004 (0.01) (0.01) (0.01) (0.01) UV_7 -0.376�� -0.374�� -0.372�� -0.362�� (0.17) (0.17) (0.17) (0.17) High_stringency_7 -0.087 (0.07)

High_stringency_14 -0.273��� (0.07) High_containment_7 -0.176��� (0.07) High_containment_14 -0.388��� (0.07) Constant 28.414��� 28.456��� 28.351��� 26.469��� (4.28) (4.23) (4.36) (4.20) Observations 19,289 19,289 19,289 19,289 R2 0.733 0.736 0.734 0.741 Adjusted R2 0.732 0.736 0.733 0.740 Results only for the Canadian territories and the United States. 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.t005 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 20 / 27

introduction, we include the statistical analysis as a baseline, leverage the machine learning analysis for short-term prediction, and use the econometric results as an indication of long- term trends. SHAP values for meteorological and environmental factors other than UV tend to cluster very closely (see Fig 6), so their relative ranking points to relatively mild impact differences. Also, only pressure, temperature, UV, stringency, and containment factors show sufficient sta- tistical significance (p < 0.10) in the econometric results. Results for rainfall, absolute humid- ity, wind speed, short-wave irradiation, PM2.5, and PM10 where p > 0.10 can only be seen as weak indicators. UV emerges as the most impactful meteorological factor in COVID-19 transmission across all methods. More specifically, UV is negatively correlated with COVID-19 spread. This result is corroborated by overwhelming evidence that UV light can effectively kill SARS-CoV-2 and other coronaviruses [82–84]. Temperature is positively correlated with COVID-19 transmission in both the machine learning and econometric analyses. As discussed in the introduction, there is contrasting evi- dence about the role of temperature in the spread of COVID-19. The inverse correlation of UV and temperature with COVID-19 spread emerging in our analysis suggests that the inhib- iting factor in those studies where the temperature is negatively correlated with COVID-19 spread may not temperature per se, but rather the high UV that is often found in hotter cli- mates. This is a hypothesis that requires further inspection.

Absolute humidity is positively correlated with COVID-19 spread in the machine learning analysis and negatively correlated in the econometric analysis, though with insufficient statisti- cal significance. This is an interesting contrast as there is conflicting evidence in the literature about the role of absolute humidity in the spread of COVID-19, as discussed in the introduc- tion (S1 Table). Perhaps, the two analyses point to a different role that absolute humidity may play in the short and long term with reference to COVID-19 spread. This too is a hypothesis that requires further inspection. Table 6. Coefficients and relative rank describing the impact of climate factors on COVID-19 transmission across the three types of analysis carried out in this study. Statistical analysis (Spearman’s coefficient) Machine learning analysis (SHAP values) Econometric analysis (Panel data fixed effects model) Temperature 0:42 3 ð Þ 14:7 2 ð Þ � � �p > 0:10 5 ð Þ Absolute Humidity 0:39 4 ð Þ 11:6 4 ð Þ p > 0:10 7 ð Þ Pressure 0:19 8 ð Þ 14.5 (3) � � �p < 0:001 3 ð Þ Wind speed 0:21 6 ð Þ 10.4 (8) p > 0:10 6 ð Þ Rainfall 0:21 7 ð Þ 10:3 9 ð Þ p < 0:10 8 ð Þ Short-wave irradiation 0:55 2 ð Þ 10:9 5 ð Þ p > 0.10 (11) PM2.5 0:24 5 ð Þ 10.5 (7) p > 0:10 9 ð Þ PM10 0:17 9 ð Þ 10:8 6 ð Þ p > 0:10 10 ð Þ UV 0:56 1 ð Þ 18:1 1 ð Þ � � p < 0:001 4 ð Þ Stringency 8; 8 10 ð Þ � � �p < 0:001 2 ð Þ Containment 7:9 11 ð Þ � � �p < 0:001 1 ð Þ

Red fonts indicate negative correlations, blue positive correlations, and black undetermined polarity. The integers enclosed in parentheses describe relative rank (1 = highest, 11 = lowest). Stringency and containment results are not available in the statistical analysis. https://doi.org/10.1371/journal.pone.0273078.t006 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 21 / 27

PM10, stringency, and containment all show the same polarity in the machine learning and econometric analysis, though only for containment and stringency the econometric analysis yields statistical relevance (p < 0.001). Discrepancies between results in the statistical analysis and the machine learning and econometric analyses emphasize the difficulty in statistical analysis to deal with non-stationary processes and account for long terms trends. Perhaps, the most interesting discrepancies are those between machine learning and econometric analysis. As discussed in the introduction, machine learning is better equipped to take advantage of structural heterogeneity in training data to make short-term predictions, whereas econometric methods are better at capturing long-term trends [73]. Therefore, diverging results across the two methods may be indicative of short-term vs. long-term impacts. Conclusions Overall, disease susceptibility is the main factor driving the pandemic growth. Compliance with lockdown and restrictions policies and regulations and increased testing are the most effective strategies for disease control and COVID-19 spread prevention. For example, various studies have reported that interventions such as restrictions on mass gatherings, school clo- sures, and social distancing measures are strongly associated with a decrease in the COVID-19 transmission growth rate [33, 38, 39, 64, 85–87]. The correlation of COVID-19 transmission with climate factors provides a valuable complementary diagnostic that sheds light on the sea-

sonal characterization of the pandemic and helps refine measures to contain and prevent the spread of COVID-19. More specifically, weather forecasts could help predict new cycles of the pandemic and future outbreaks and thus contribute to the definition of ad-hoc measures that limit the economic impact of complete lockdowns. This study also extends the reach of earlier studies (S1 Table) on the relationship between COVID-19 transmission and climate factors by assessing how climate helps modeling COVID-19 through systematic validation using statisti- cal, feature importance, and econometric analyses. Such validation is crucial in proving which are the contributing factors and their relative magnitude and direction of change. Supporting information S1 File. (DOCX) S1 Fig. Statistical distribution of the share of population emerging from the sampling approach of meteorological conditions adopted in this study. On the left is a frequency his- togram of the population share covered by the sampling approach for each county. For most of the countries the share is above 75% and for only few of them is less than 20%. On the right side of the figure is a scatter plot of the share for each location considered in the study as a function of the overall population size. (TIF) S2 Fig. Summary diagram of the methodological approach. (TIF) S3 Fig. Feature importance summary plot for the north and south hemispheres. Mean absolute SHAP value (in log scale) of each variable showing the average impact on the model

output magnitude for the locations in the north (left) and south (left) hemispheres. (TIF) 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 22 / 27

S4 Fig. Feature impact scatters plot for the north and south hemispheres. SHAP value of each variable for all the single observations as a function of their relative value for the locations in the north (left) and south (left) hemispheres. The color transition on the vertical axis indi- cates value strength (red/high to blue/low). (TIF) S5 Fig. Feature importance analysis based on Lasso, Elastic Net, and Random Forest algo- rithms. For Lasso and Elastic Net we report their regression coefficients (red for negative val- ues and blue for positive). For the random forest tree, we use the Gini importance score. (TIF) S1 Table. Summary of peer-reviewed literature on research studies on the interrelationship between COVID-19 and environmental/climatic factors. (DOCX) S2 Table. Description of the variables used for the study. (DOCX) S3 Table. Descriptive statistics. (DOCX) Acknowledgments The findings presented in this study are solely the responsibility of the authors. An earlier ver- sion of this paper is available as a Research Square preprint (DOI: http://dx.doi.org/10.21203/ rs.3.rs-108398/v1). Author Contributions Conceptualization: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni. Data curation: Giovanni Scabbia, Annamaria Mazzoni, Dunia Bachour, Daniel Perez-Astu- dillo, Etienne Wey, Mathilde Marchand-Lasserre, Laurent Saboret. Formal analysis: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni. Funding acquisition: Antonio Sanfilippo. Investigation: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni.

Methodology: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni. Project administration: Antonio Sanfilippo. Resources: Giovanni Scabbia, Antonio Sanfilippo. Software: Giovanni Scabbia. Supervision: Antonio Sanfilippo, Veronica Bermudez. Validation: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni. Visualization: Giovanni Scabbia. Writing – original draft: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni, Dunia Bachour, Daniel Perez-Astudillo, Veronica Bermudez, Etienne Wey, Mathilde Marchand- Lasserre, Laurent Saboret. 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 23 / 27

Writing – review & editing: Giovanni Scabbia, Antonio Sanfilippo, Annamaria Mazzoni, Dunia Bachour, Daniel Perez-Astudillo, Veronica Bermudez, Etienne Wey, Mathilde Marc- hand-Lasserre, Laurent Saboret. References 1. Bukhari, Q. and Y. Jameel, Will coronavirus pandemic diminish by summer? SSRN 3556998, 2020. 2. Dong E., Du H., and Gardner L., An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 2020. 20(5): p. 533–534. 3. Briz-Redo´n A´ . and Serrano-Aroca A´ ., The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques. Progress in Physical Geography: Earth and Environment, 2020. 44(5): p. 591–604. 4. Mecenas P., et al., Effects of temperature and humidity on the spread of COVID-19: A systematic review. medRxiv, 2020. https://doi.org/10.1371/journal.pone.0238339 PMID: 32946453 5. Moriyama M., Hugentobler W.J., and Iwasaki A., Seasonality of respiratory viral infections. Annual review of virology, 2020. 7. 6. Al-Ahmadi K., Alahmadi S., and Al-Zahrani A., Spatiotemporal clustering of Middle East respiratory syn- drome coronavirus (MERS-CoV) incidence in Saudi Arabia, 2012–2019. International journal of envi- ronmental research and public health, 2019. 16(14): p. 2520. https://doi.org/10.3390/ijerph16142520 PMID: 31311073 7. Altamimi A. and Ahmed A.E., Climate factors and incidence of Middle East respiratory syndrome coro- navirus. Journal of Infection and Public Health, 2020. 13(5): p. 704–708.

8. Gardner E.G., et al., A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome. BMC infectious diseases, 2019. 19(1): p. 1–10. 9. Lin K., et al., Environmental factors on the SARS epidemic: air temperature, passage of time and multi- plicative effect of hospital infection. Epidemiology & Infection, 2006. 134(2): p. 223–230. https://doi.org/ 10.1017/S0950268805005054 PMID: 16490124 10. Jiang Y., Wu X.-J., and Guan Y.-J., Effect of ambient air pollutants and meteorological variables on COVID-19 incidence. Infection Control & Hospital Epidemiology, 2020: p. 1–11. https://doi.org/10.1017/ ice.2020.222 PMID: 32389157 11. Qi H., et al., COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Science of the Total Environment, 2020: p. 138778. 12. Wang J., et al., High temperature and high humidity reduce the transmission of COVID-19. Available at SSRN 3551767, 2020. 13. Liu J., et al., Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China. Science of the total environment, 2020. 726: p. 138513. 14. Bashir M.F., et al., Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of The Total Environment, 2020. 728: p. 138835. 15. Lin R., Wang X., and Huang J., The influence of weather conditions on the COVID-19 epidemic evi- dence from 279 prefecture-level panel data in China. Environmental Research, 2021: p. 112272.

16. Yuan J., et al., A climatologic investigation of the SARS-CoV outbreak in Beijing, China. American jour- nal of infection control, 2006. 34(4): p. 234–236. https://doi.org/10.1016/j.ajic.2005.12.006 PMID: 16679182 17. Coro G., A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate. Eco- logical Modelling, 2020. 431: p. 109187. 18. Perone G., The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors. Science of The Total Environment, 2021. 755: p. 142523. https://doi.org/10.1016/j.scitotenv.2020.142523 PMID: 33022464 19. Ferna´ ndez-Ahu´ja J.M.L. and Martı´nez J.L.F., Effects of climate variables on the COVID-19 outbreak in Spain. International journal of hygiene and environmental health, 2021. 234: p. 113723. 20. Me´ndez-Arriaga F., The temperature and regional climate effects on communitarian COVID-19 conta- gion in Mexico throughout phase 1. Science of The Total Environment, 2020: p. 139560. 21. Yin C., Zhao W., and Pereira P., Meteorological factors’ effects on COVID-19 show seasonality and spatiality in Brazil. Environmental research, 2022: p. 112690. 22. Bolaño-Ortiz T.R., et al., Spread of SARS-CoV-2 through Latin America and the Caribbean region: a look from its economic conditions, climate and air pollution indicators. Environmental research, 2020. 191: p. 109938. 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 24 / 27

23. Sobral M.F.F., et al., Association between climate variables and global transmission oF SARS-CoV-2. Science of The Total Environment, 2020. 729: p. 138997. 24. Guo C., et al., Meteorological factors and COVID-19 incidence in 190 countries: an observational study. Science of the Total Environment, 2021. 757: p. 143783. 25. Notari A., Temperature dependence of COVID-19 transmission. Science of The Total Environment, 2021. 763: p. 144390. 26. Fontal A., et al., Climatic signatures in the different COVID-19 pandemic waves across both hemi- spheres. Nature Computational Science, 2021. 1(10): p. 655–665. 27. Metelmann S., et al., Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide. One Health, 2021. 12: p. 100221. https://doi.org/10. 1016/j.onehlt.2021.100221 PMID: 33558848 28. Xie J. and Zhu Y., Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment, 2020. 724: p. 138201. 29. Auler A., et al., Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Science of The Total Environment, 2020: p. 139090. 30. Menebo M.M., Temperature and precipitation associate with Covid-19 new daily cases: A correlation study between weather and Covid-19 pandemic in Oslo, Norway. Science of The Total Environment, 2020: p. 139659.

31. Tosepu R., et al., Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of The Total Environment, 2020: p. 138436. 32. Ward M., Xiao S., and Zhang Z., The Role of Climate During the COVID-19 epidemic in New South Wales, Australia. Authorea, 2020. https://doi.org/10.1111/tbed.13631 PMID: 32438520 33. Ju¨ ni P., et al., Impact of climate and public health interventions on the COVID-19 pandemic: a prospec- tive cohort study. Cmaj, 2020. 192(21): p. E566–E573. https://doi.org/10.1503/cmaj.200920 PMID: 32385067 34. Aboura S., The influence of climate factors and government interventions on the Covid-19 pandemic: Evidence from 134 countries. Environmental Research, 2022: p. 112484. 35. Islam N., et al., COVID-19 and climatic factors: A global analysis. Environmental research, 2021. 193: p. 110355. 36. Shakil M.H., et al., COVID-19 and the environment: A critical review and research agenda. Science of the Total Environment, 2020: p. 141022. 37. McClymont H. and Hu W., Weather variability and COVID-19 transmission: A review of recent research. International journal of environmental research and public health, 2021. 18(2): p. 396. 38. Sera F., et al., A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries. Nature communications, 2021. 12(1): p. 1–11. 39. Paraskevis D., et al., A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks. Science of

the Total Environment, 2021. 768: p. 144578. 40. Coccia M., How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atmospheric pollution research, 2021. 12(1): p. 437–445. https://doi.org/10.1016/j.apr.2020.10.002 PMID: 33046960 41. Coccia M., The effects of atmospheric stability with low wind speed and of air pollution on the acceler- ated transmission dynamics of COVID-19. International Journal of Environmental Studies, 2021. 78(1): p. 1–27. 42. Coşkun H., Yıldırım N., and Gu¨ ndu¨z S., The spread of COVID-19 virus through population density and wind in Turkey cities. Science of the Total Environment, 2021. 751: p. 141663. 43. Rosario D.K., et al., Relationship between COVID-19 and weather: Case study in a tropical country. International journal of hygiene and environmental health, 2020. 229: p. 113587. 44. Abraham J., et al., Does climate play any role in covid-19 spreading?—an Australian perspective. Inter- national Journal of Environmental Research and Public Health, 2021. 18(17): p. 9086. 45. Karapiperis C., et al., A strong seasonality pattern for COVID-19 incidence rates modulated by UV radi- ation levels. Viruses, 2021. 13(4): p. 574. https://doi.org/10.3390/v13040574 PMID: 33805449 46. Choi Y.W., Tuel A., and Eltahir E.A., On the environmental determinants of COVID-19 seasonality. Geohealth, 2021. 5(6): p. e2021GH000413. https://doi.org/10.1029/2021GH000413 PMID: 34095688 47. Neher R.A., et al., Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss medical

weekly, 2020. 150(1112). https://doi.org/10.4414/smw.2020.20224 PMID: 32176808 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 25 / 27

48. Sajadi M.M., et al., Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19). JAMA Network Open, 2020. 3(6): p. e2011834–e2011834. https://doi.org/10.1001/jamanetworkopen.2020.11834 PMID: 32525550 49. Pan J., et al., Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries. Science of The Total Environment, 2021. 753: p. 142272. 50. Poirier C., et al., The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales. Scientific reports, 2020. 10(1): p. 1–11. 51. CSSE, J., COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, J. CSSE, Editor. 2020. 52. Iglewicz B. and Hoaglin D.C., How to detect and handle outliers. Vol. 16. 1993: Asq Press. 53. Affairs), U.U.N.D.o.E.a.S., Population Division (2019), U.U.N.D.o.E.a.S. Affairs), Editor. 2019. 54. Bank, W., World Development Indicators, W. Bank, Editor. 2020. 55. Fund), I.I.M., World Economic Outlook, I.I.M. Fund), Editor. 2019: Washington, DC. 56. Ravi S.J., et al., The value proposition of the Global Health Security Index. BMJ global health, 2020. 5 (10): p. e003648. 57. Hale T., et al., Variation in government responses to COVID-19. Blavatnik school of government work- ing paper, 2020. 31. 58. Hale T., et al., A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 2021: p. 1–10.

59. Oyj V., Humidity conversion formulas. Calculation formulas for humidity. 2013: Helsinki, Finland. 60. Hockberger P.E., The discovery of the damaging effect of sunlight on bacteria. Journal of Photochemis- try and Photobiology B: Biology, 2000. 58(2–3): p. 185–191. 61. Ratnesar-Shumate S., et al., Simulated sunlight rapidly inactivates SARS-CoV-2 on surfaces. The Jour- nal of Infectious Diseases, 2020. 62. Fattorini D. and Regoli F., Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environmental Pollution, 2020: p. 114732. 63. Coccia M., Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Science of the Total Environment, 2020: p. 138474. https://doi.org/10.1016/j.scitotenv.2020.138474 PMID: 32498152 64. Bloise F. and Tancioni M., Predicting the spread of COVID-19 in Italy using machine learning: Do socio- economic factors matter? Structural Change and Economic Dynamics, 2021. 56: p. 310–329. https:// doi.org/10.1016/j.strueco.2021.01.001 PMID: 35317020 65. Lewis D., Is the coronavirus airborne? Experts can’t agree. Nature, 2020. 580(7802): p. 175. 66. simplemaps.com. World cities database, accessed on 01/05/2022. 2022; https://simplemaps.com/data/ world-cities. 67. Lauer S.A., et al., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine, 2020. 172(9): p. 577–582. https://doi.org/10.7326/M20-0504 PMID: 32150748

68. Cereda D., et al., The early phase of the COVID-19 outbreak in Lombardy, Italy. 2020, Arxiv. 69. Malhotra A., A hybrid econometric–machine learning approach for relative importance analysis: priori- tizing food policy. Eurasian Economic Review, 2021: p. 1–33. 70. Charpentier A., Flachaire E., and Ly A., Econometrics and machine learning. Economie et Statistique, 2018. 505(1): p. 147–169. 71. Zheng E., et al., When econometrics meets machine learning. Data and Information Management, 2017. 1(2): p. 75–83. 72. Iskhakov F., Rust J., and Schjerning B., Machine learning and structural econometrics: contrasts and synergies. The Econometrics Journal, 2020. 23(3): p. S81–S124. 73. Liu Y. and Xie T., Machine learning versus econometrics: prediction of box office. Applied Economics Letters, 2019. 26(2): p. 124–130. 74. Zaitchik B.F., et al., A framework for research linking weather, climate and COVID-19. Nature Commu- nications, 2020. 11(1): p. 1–3. 75. Friedman J.H., Greedy function approximation: a gradient boosting machine. Annals of statistics, 2001: p. 1189–1232. 76. Chen, T. and C. Guestrin. Xgboost: A scalable tree boosting system. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016. 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 26 / 27

77. Lundberg, S.M. and S.-I. Lee. A unified approach to interpreting model predictions. in Advances in neu- ral information processing systems. 2017. 78. Lundberg S.M., Erion G.G., and Lee S.-I., Consistent individualized feature attribution for tree ensem- bles. arXiv preprint arXiv:1802. 03888, 2018. 79. Wooldridge J.M., Econometric analysis of cross section and panel data. 2010: MIT press. 80. Weitz J.S., et al., Modeling shield immunity to reduce COVID-19 epidemic spread. Nature Medicine, 2020. 26(6): p. 849–854. 81. Choi I., Unit root tests for panel data. Journal of international money and Finance, 2001. 20(2): p. 249– 272. 82. Heilingloh C.S., et al., Susceptibility of SARS-CoV-2 to UV irradiation. American journal of infection con- trol, 2020. 48(10): p. 1273–1275. https://doi.org/10.1016/j.ajic.2020.07.031 PMID: 32763344 83. Kitagawa H., et al., Effectiveness of 222-nm ultraviolet light on disinfecting SARS-CoV-2 surface con- tamination. American journal of infection control, 2021. 49(3): p. 299–301. 84. Biasin M., et al., UV-C irradiation is highly effective in inactivating SARS-CoV-2 replication. Scientific Reports, 2021. 11(1): p. 1–7. 85. Chinazzi M., et al., The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID- 19) outbreak. Science, 2020. 368(6489): p. 395–400. https://doi.org/10.1126/science.aba9757 PMID: 32144116 86. Hsiang S., et al., The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature, 2020. 584(7820): p. 262–267.