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. 2023 Jan;43(1):8-18.
doi: 10.1111/risa.14080. Epub 2022 Dec 12.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study

Alberto J Alaniz  1   2   3   4 , Mario A Carvajal  2   3 , Jorge G Carvajal  3   4 , Pablo M Vergara  3
Affiliations

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study

Alberto J Alaniz et al. Risk Anal. 2023 Jan.

Abstract

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO2 ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

Keywords: SARS-CoV-2; environmental quality; infection risk; pollutant gases; relative humidity; temperature.

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Figures

FIGURE 1
FIGURE 1
Relative risk predicted from the Bayesian model based on mean temperature, specific humidity, and air pollutants for COVID‐19 infection in China (A), Spain (B and C), the United States (D), and Italy (E)
FIGURE 2
FIGURE 2
Map of the Bayesian estimates of median weekly risk (RR) of COVID‐19 disease in States (the United States), provinces (Italy and China), and autonomous communities (Spain). Each country has a specific color square with the respective confidence intervals for the United States (blue), Spain (purple), Italy (green), and China (red). The effect of lockdown and season is not shown for China because the complete analyzed timeframe was under lockdown and without season change
FIGURE 3
FIGURE 3
Bayesian posterior coefficients associated with the effects of carbon monoxide (left) and formaldehyde (right) on the risk of COVID‐19 infection in the United States, China, Italy, and Spain. Solid lines represent the mean of each effect, while shaded areas represent the 95% Bayesian credible intervals (parameter convergence is detailed in Table S3). Plotted correlations lines correspond to the Bayesian relationship between Log of relative risk in relation with the corresponding environmental variable. Subgraphs show the temporal changes of the corresponding variable over time, with the color (country) of lines being the same as shown for main graphs
FIGURE 4
FIGURE 4
Bayesian posterior coefficients associated with the effects of nitrogen dioxide (upper), ozone (middle) and sulfur dioxide (lower) on the risk of COVID‐19 infection in the United States, China, Italy, and Spain (see details in Figure 3)
FIGURE 5
FIGURE 5
Bayesian posterior coefficients associated with the effects of specific humidity (left) and mean temperature (left) on the risk of COVID‐19 infection in the United States, China, Italy, and Spain (details in Figure 3)
See this image and copyright information in PMC

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