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

m from ground level (station pressure) in hPa, wind speed at 10 m above ground in m/s, and rainfall in mm. Temperature is combined with relative humidity to derive measures of absolute humidity in g/m3 [59]. Data obtained from CAMS include downward UV Radiation at the Surface (UV) in J/m2, and Particulate Matter (PM) concentrations (PM2.5 and PM10) for 3-hour periods at a spatial resolution of 40 km. UV exposure can have a sterilizing effect [60] and ultraviolet B light (UVB), which is present in small amounts in natural sunlight, is known to rapidly inactivate SARS-CoV-2 on surfaces [61]. Data on particulate matter [62], originally in kg/m3, is converted to micrograms/m3 and it can provide preliminary evidence on the rela- tion between air quality and the chronicity of exposure to the viral infection. Coccia [63] and Bloise and Tancioni [64] both suggest that air pollution may have accelerated the transmission rate of COVID-19 in northern Italy, even though the viability of infectious viruses embedded in suspended aerosol particles is still under debate [65]. Streaming access to MERRA-2 and CAMS was provided by Transvalor S.A. For each loca- tion considered in the study, we derived the geographical centroids of the country’s most pop- ulous cities [66]. For country-level locations, we considered the ten most populated cities, while for admin-level locations (sub-country, i.e., states, territories, provinces, cantons, etc. as appropriate per country) we considered the five most populous cities. We used the corre-

sponding latitude and longitude coordinates of each city to query the climatic information from MERRA-2 and CAMS through Transvalor’s SoDa data service (http://www.soda-pro. com/) and we finally derived the resulting time-variant meteorological data by averaging across the different cities for each location. After merging all the various sources, the resulting dataset includes data on 196 countries covering 96% of the world population and 97.6% of worldwide confirmed COVID-19 cases (123,491,126 –at the period of the study). Data for 28 of these countries are detailed in the data- set at a state or regional level (i.e., admin-level) for the available periods (see S1 File for a detailed list). We only consider country-level epidemiological data for the remaining 152 countries, even though admin-level data are available from coronadatascraper.com, in order to keep a certain level of minimum comparison between the locations under study in terms of overall population size. S2 and S3 Tables provide detailed information on the variables used for the study and their descriptive statistics. The COVID-19 mean incubation period, defined as the time period ranging between expo- sure to the virus and the onset of the illness, is estimated by WHO at 5–6 days (median 5.1 days, 95% Confidence Interval (CI): 4.5 to 5.8 days) [67]. According to Lauer et al. [67], 97.5% of those who develop symptoms will do so within 11.5 days (CI: 8.2 to 15.6 days) from the day of infection. Moreover, the results of a COVID-19 PCR test have been known to take up to an

average of 3–4 days (3.6 days according to Cereda et al. [68]), particularly during the initial months of the pandemic. For these reasons, the number of new COVID-19 cases that are offi- cially announced each day corresponds to a time window of infection that spans from a few days up to potentially two weeks earlier. To account for this timeframe uncertainty and test the robustness of our results, the analysis is carried out with moving averages for both the time- variant meteorological and the policy variables of different duration: 5, 7, 10, 12, and 14 days (minimum length of 3 days). In reporting results, the number of days determining the moving average of time-variant variables is encoded as either a number suffix (e.g., Temperature_7), or a suffix variable indexed to a specific numeric value (e.g., Temperature_T, . . . T = 7). Limitations and assumptions As for other data-driven studies on COVID-19 transmission, the present analysis relies on rec- ords whose quality varies across sources, due to heterogeneous collection and reporting 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 5 / 27