Is there a link between temperatures and COVID-19 contagions? Evidence from Italy

Abstract

This study analyzes the link between temperatures and COVID-19 contagions in a sample of Italian regions during the period ranging from February 24 to April 15. To that end, Bayesian Model Averaging techniques are used to analyze the relevance of the temperatures together with a set of additional climate, environmental, demographic, social and policy factors. The robustness of individual covariates is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by the temperatures given that it appears as the most relevant determinant of contagions. This finding is robust to (i) the prior distribution elicitation, (ii) the procedure to assign weights to the regressors, (iii) the presence of measurement errors in official data due to under-reporting, (iv) the employment of different metrics of temperatures or (v) the inclusion of additional correlates. In a second step, relative importance metrics that perform an accurate partitioning of the R2 of the model are calculated. The results of this approach support the evidence of the model averaging analysis, given that temperature is the top driver explaining 45% of regional contagion disparities. The set of policy-related factors appear in a second level of importance, whereas factors related to the degree of social connectedness or the demographic characteristics are less relevant.

Rethinking Air Quality and Climate Change after COVID-19

“…The outbreak of influenza and SARS [73] in 2003 (caused by SARS-CoV-1) demonstrated clear seasonality and latitudinal dependence [74][75][76][77]. Some studies suggested that the temperature and humidity of a locality influenced the virus spread [24,25,[78][79][80][81][82]. In light of the seasonal emergence pattern of SARS-CoV-1, it has been speculated that high temperatures and high humidity may suppress the transmission of SARS-CoV-2.…”

A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States

“…25 A recent study also suggests body temperatures to be the most relevant determinant of contagions. 26 The app users are instructed to use any available thermometer to measure and self-report their body temperature along with the observed symptoms.…”

Forecasting the Spread of the COVID-19 Epidemic in Lombardy: A Dynamic Model Averaging Approach

“…The reason is that climate factors may affect not only the susceptibility conditions of the host by decreasing metabolic functions and defense barriers (Lowen and Steel, 2014), the physical properties of the virion envelope and its stability, but also the efficiency of the different routes of viral transmission (Duan et al ., 2003; Van Doremalen et al ., 2020). In the context of the COVID-19 epidemic, a large body of literature already exists suggesting epidemic curves are influenced by climate (see, Qi et al ., 2020; Paez et al ., 2020; Rios and Gianmoena, 2020). Using data from the NASA POWER v8 database we model climate effects by means of (7) the mean temperature 2 meters above the surface, the (8) relative humidity and the level of ultra-violet (UV) solar radiation .…”

“…However, the implementation of lock-downs (Born et al, 2020;Deb et al, 2020), the changing climate (? ; Paez et al, 2020;Rios and Gianmoena, 2020) and pollution patterns (Sciomer et al, 2020;Yongjian et al, 2020;Wu et al, 2020), the restrictions on social mobility within and across regions (Cartení et al, 2020;Kraemer et al, 2020;Zhou et al, 2020) or the laws on the use of protective equipment such as face masks or distancing measures (Mitze et al, 2020;Wang, Y. et al, 2020a), are likely to have affected the spread dynamics of the COVID-19. Given that it is not clear which set of factors could be part of the data generating process, a naive approach that ignores model uncertainty may result in biased estimates, overconfident (too narrow) standard errors and misleading inference and predictions.…”