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

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