This study analyzes the link between temperature 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 temperature 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 temperature given that it appears as one of the most relevant determinants reducing regional coronavirus disease 2019 (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, and 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.

On the link between temperature and regional COVID-19 severity: Evidence from Italy / V. Rios, L. Gianmoena. - In: REGIONAL SCIENCE POLICY & PRACTICE. - ISSN 1757-7802. - 13:1(2021 Nov), pp. 109-137. [10.1111/rsp3.12472]

On the link between temperature and regional COVID-19 severity: Evidence from Italy

V. Rios;
2021

Abstract

This study analyzes the link between temperature 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 temperature 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 temperature given that it appears as one of the most relevant determinants reducing regional coronavirus disease 2019 (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, and 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.
Bayesian model averaging; COVID-19; infections; Italian regions; spatial econometrics; temperatures
Settore SECS-S/01 - Statistica
6-set-2021
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/891387
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