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

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.

Impact of immobility and mobility activities on the spread of COVID‐19: Evidence from European countries

“…The long‐run results from the PMG–ARDL series confirm the negative impact of temperature on the spread of COVID‐19 (Table 6 ). This variable has negative and significant effects in the seven models, confirming the results of other studies on the regions of the same country (Palialol et al, 2020 ; Rios & Gianmoena, 2021 ). It also has the most important effect among all the other variables.…”

“…The highest rates of growth of the pandemic are recorded in particular during periods of falling temperatures, which favour the concentration of populations in closed places that are conducive to the transmission of the virus. Several studies show that the temperature differential explains a large part of the evolution of the number of infections (Palialol et al, 2020 ; Pan et al, 2021 ; Rios & Gianmoena, 2021 ). Temperature is an important control variable to include when analysing the role of other variables, such as mobility, on the spread of COVID‐19.…”

Place-based factors affecting COVID-19 incidences in Turkey

“…Arbel et al ( 2021 ) emphasized that high population density increases the vulnerability of cities to COVID-19; moreover, Florida and Mellander ( 2022 ) underlined that the existence of hospitals increases the risk of infection due to the interaction between people. Also, many studies found a negative correlation between temperature and the spread of COVID-19 (Wang et al 2020 ; Ma et al 2020 ; Rios and Gianmoena 2021 ). Considering the findings from the literature, it can be said that the statistical significance of these factors in explaining the model is a sensible conclusion to draw.…”

“…Although the reasons for variation in rate of spread of the COVID-19 pandemic remain somewhat unclear, many studies around the world have found that geo-environmental factors (Coccia 2020 ), demographic characteristics (Mollato et al 2020 ; Arbel et al 2021 ), and climatic conditions (Ma et al 2020 ; Oto-Peralías 2020 ; Wu et al 2020 ; Rios and Gianmoena 2021 ) can affect the severeness and spread of COVID-19. Some researchers have also focused on the relationship between health and social geography (De Kadt et al 2020 ; Gibson and Rush, 2020 ; Kuupiel et al 2020 ; Hierro and Maza 2022 ).…”

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

“…Spatial implications of the COVID-19 pandemic have been investigated from different perspectives. Some studies have analyzed geo-environmental factors (air pollution, weather conditions) (Coccia 2020 ), demographic characteristics (population density) (Mollato et al 2020 ; Arbel et al 2022 ), and climate conditions (temperature, humidity) (Oto-Peralías 2020 ; Ma et al 2020 ; Sajadi et al 2020 ; Wang et al 2020 ; Wu et al 2020 ; Rios and Gianmoena 2021 ) can affect the spread of COVID-19. Some studies have found a strong correlation between human mobility and the spread of disease (Chen et al 2020 ; Furceri et al 2020 ; Gross et al 2020 ; Zhang et al 2020 ; Hierro and Maza 2022 ).…”