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

impediments by investigating the contribution of meteorological factors to modeling COVID- 19 transmission at the global scale with reference to reported COVID-19 cases in 196 countries over a 14-month period, using socioeconomic, environmental, and global health factors as control variables. The study employs three complementary approaches to measure the correla- tion between reported rates of COVID-19 transmission and selected meteorological, socioeco- nomic, environmental, and global health factors. The first approach is based on the statistical analysis of the rank correlation of every factor with the number of daily confirmed COVID-19 cases. Following is a feature importance analysis that uses Shapley Additive Explanations (SHAP) to elucidate the COVID-19 rate predictions of a regression model by computing the contribution of each feature in the model to the prediction task. The third and last approach is based on econometric analysis with panel data fixed-effect regression models. The three approaches supply independent analytic evidence of the relationship between confirmed COVID-19 cases and meteorological factors. Of particular interest is the complementarity between the machine learning analysis, which is intent on prediction, and the econometric analysis, which focuses on explanation. As discussed in the literature, the use of a hybrid approach where machine learning modeling is paired with econometric analysis can help address relative weaknesses in the two methods by leveraging relative strengths. Further details

are provided in the Methodology subsection below. Materials and methods Data sources and pre-processing The data used in this study include epidemiological, socioeconomic, environmental, global health indicators, and meteorological variables–see S2 Table. All population-related variables are converted to percentages of the total population per country. Parameters that behave as time-invariant variables during the period of focus for this study, e.g., socioeconomic variables, are used as control variables. Epidemiological data on the cumulative number of confirmed COVID-19 cases were retrieved for the period from 23 January 2020 to 21 March 2021 from two main sources: the data repository from the Johns Hopkins Center for Systems Science and Engineering [51], and the Corona Data Scraper online data service that retrieves COVID-19 Coronavirus cases data from verified sources worldwide and adds population data on a daily basis (coronadatascraper. com). Data from these two sources were merged to create the first dataset. In addition to coun- try-level records, data at the regional or state level were included when available. We derived the number of daily registered COVID-19 cases by differencing entries in the initial dataset. Data points characterized by a modified Z-score > 3.5 and with values above the third quartile or below the first quartile and 1.5 times greater than the interquartile range were discarded as outliers together with other inconsistent data points (e.g., negative values) [52]. From the daily

values, we retrieved 3-day and 7-day moving averages as features to incorporate an auto- regressive component in the learning model. Only records reporting more than 10 daily cases were included in the analysis. The time of exposure to the pandemic for each country was also calculated as the cumulative temporal distance from the first registered case in the country. We selected data through March 2021, when most countries began their vaccination cam- paigns, to avoid including vaccination as an additional impact on COVID-19 transmission rates. Such inclusion would have created discrepancies with earlier data and introduced incon- sistencies emerging from the adoption of diverse vaccination strategies worldwide. We used several socioeconomic time-invariant data sources including demographic infor- mation, technology adoption rates, and Gross Domestic Product per-capita (GDPP) as control factors in the cross-sectional fixed effect analysis. Demographic, population density, and 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 3 / 27