Rios Vicente, Gianmoena Lisa
Department of Economics University of Milan Via Festa del Perdono, 7 Milano 20122 Italy.
Department of Economics and Management University of Pisa Cosimo Ridolfi 10 Pisa 56124 Italy.
Reg Sci Policy Prac. 2021 Nov;13(Suppl 1):109-137. doi: 10.1111/rsp3.12472. Epub 2021 Oct 11.
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.
本研究分析了在涵盖2020年第一波疫情的时间段内，意大利部分地区温度与新冠病毒病（COVID-19）发病率之间的联系。为此，采用贝叶斯模型平均技术来分析温度以及一系列其他气候、人口、社会和卫生政策因素的相关性。通过后验包含概率来衡量各个预测变量的稳健性。实证分析为温度所起的作用提供了确凿证据，因为它似乎是降低地区2019冠状病毒病（COVID-19）严重程度的最相关决定因素之一。在我们的基线分析中观察到的强烈负相关关系，对于先验设定、分析规模、因报告不足对数据测量误差的校正、所考虑的时间窗口以及模型中空间效应的纳入都是稳健的。在第二步中，我们计算相对重要性指标，该指标分解了模型所解释的变异性。我们发现，跨地区温度差异解释了观察到的感染数量变化的很大一部分。
