On the link between temperatures and regional COVID‐19 severity: Evidence from Italy

This study analyzes the link between temperatures and COVID-19 incidence in a sample of Italian regions during the period that covers the first epidemic wave of 2020. To that end, Bayesian Model Averaging techniques are used to analyze the relevance of the temperatures together with a set of additional climatic, demographic, social and health policy factors. The robustness of individual predictors is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by the temperatures given that they appear as one of the most relevant determinants reducing regional COVID-19 severity. The strong negative link observed in our baseline analysis is robust to the specification of priors, the scale of analysis, the correction of measurement errors in the data due to under-reporting, the time-window considered, or the inclusion of spatial effects in the model. In a second step, we compute relative importance metrics that decompose the variability explained by the model. We find that cross-regional temperature differentials explain a large share of the observed variation on the number of infections.