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

population age data were derived from the 2019 Population Division dataset compiled by the Department of Economic and Social Affairs of the United Nation (UNDESA) [53] and the World Bank indicators database [54]. Information for geographical locations not included in the UNDESA dataset was retrieved online from national official sources. Rates of internet users, subscribers to mobile telephony services, and the number of secure Internet servers were retrieved from the World Bank indicators database. These technology adoption variables are used as proxies for the capacity of different countries to provide smart-work environments under lockdown, create awareness and keep the population updated about the development of the pandemic, and support effective contact tracing applications. GDPP data at constant price purchasing-power-parity were sourced from the International Monetary Fund’s World Eco- nomic Outlook Database [55]. Environmental indicators retrieved from the World Bank database include population- weighted exposure to ambient PM2.5 pollution, carbon dioxide, methane, nitrous oxide emis- sions, and greenhouse gas emissions. These time-invariant variables were used as control indi- cators of the degree of pollution in each country, on the assumption that long-term exposure to pollutants may increase the risk of contracting COVID-19 given that outdoor air pollution has been positively correlated with respiratory diseases. Health indicators included time-invariant variables such as the general Global Health Secu-

rity (GHS) index, the GHS detect and prevent scores, diabetes prevalence, and the number of hospital beds for both acute and chronic care. GHS provides a country-level score of health security and was used as a proxy variable for a country’s capability to prevent and mitigate infectious diseases. For the purpose of this study, only the “detect” and “prevent” GHS catego- ries were used, which focus on a country’s readiness to promptly identify, report, and antici- pate disease outbreaks of potential international concern [56]. Health indicators relative to diabetes prevalence and the number of hospital beds for both acute and chronic care were retrieved from the World Bank database. These variables serve as proxies for population health status and public health preparedness. Differences in intervention responses by governments to mitigate the pandemic were accounted through a variety of indicators from the Oxford COVID-19 Government Response Tracker (OxCGRT) project [57, 58]. The OxCGRT variables include policy information on school closures (C1), workplace closures (C2), cancellation of public events (C3), restriction on gatherings (C4), closure of public transports (C5), lockdowns (C6), and restriction on inter- nal (C7) and international (C8) movements and travels. In addition, the OxCGRT database provides health system policy data on the presence of public information campaigns (H1), test- ing policy (H2), contact tracing (H3), and facial covering policies (H6). Finally, we used two

more variables that are calculated as a weighted aggregation of the single C and H indices: the stringency and containment & health indices. The first variable reflects the strictness of “lock- down style” policies that primarily restrict people’s behavior. The second combines “lock- down” restrictions with measures such as testing policy and contact tracing, short-term investment in healthcare, and investments in vaccines. OxCGRT data are provided at a coun- try-level, with subnational data available for Canadian provinces, US states, and UK regions (New England, Northern Ireland, Scotland, and Wales). Meteorological variables were obtained from two main sources: the Modern-Era Retrospec- tive analysis for Research and Applications, Version 2 (MERRA-2), a gridded reanalysis data- set produced by NASA’s Global Modeling and Assimilation Office (GMAO), and the Copernicus Atmosphere Monitoring Service (CAMS) with specific reference to the McClear Clear-Sky Irradiation model. The MERRA-2 data include daily averages of the original hourly data at a spatial resolution of approximately 50 km, temperature in ˚C, relative humidity at 2 m above ground in %, short-wave solar irradiation (total of the day) in Wh/m2, pressure at 2 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 4 / 27