ArticleRegional science policy & practice2021
On the link between temperature and regional COVID-19 severity: Evidence from Italy.
Article in Regional science policy & practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Democratic quality and excess mortality during the COVID-19 pandemic.Scientific reports · 2024Article
- What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review.Sustainable cities and society · 2022Article
- Impact of immobility and mobility activities on the spread of COVID-19: Evidence from European countries.Regional science policy & practice · 2022Article
- On the link between temperature and regional COVID-19 severity: Evidence from Italy.Regional science policy & practice · 2021Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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