Can climate help fighting COVID-19 trauma? A case study of Maricopa County, Arizona, USA

Abstract

Introduction: Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the disease has spread rapidly throughout the world and became a traumatic stressor. Identification of the factors affecting the spread of the disease makes it possible to prevent its further propagation and save more people in similar situations. Environmental and climatic parameters are among the factors affecting the prevalence of diseases. Determination of environmental effects on Coronavirus disease (COVID-19) prevalence can help develop policies to suppress the spread. Methods: This study investigated the effect of climatic parameters on the spread of COVID-19 disease in County Maricopa from March 11, 2020, to November 30, 2020. These parameters include maximum, minimum, and mean daily temperature as well as maximum, minimum, and mean daily humidity, wind speed, solar radiation, and Air Quality Index (AQI) of particulate matter10 (PM10), PM2.5, and O3. A Shapiro-Wilk test was used to evaluate the normality of variables and the Spearman correlation test was used to determine the correlation between parameters and daily COVID-19 cases. A simple linear regression was applied on parameters that had significant Spearman’sranked correlation with the daily COVID-19 cases to determine their contribution to the pandemic. Results: The present study showed that the maximum, minimum, and mean temperature parameters and PM10 and PM2.5 particles had a positive and significant correlation (P<0.01) with the prevalence of COVID-19 disease. The effect of PM10 particles was higher than the other parameters (0.488, P<0.01). The parameters of maximum, minimum, and mean relative humidity along with solar radiation and O3 AQI had a significant and negative correlation with the development of COVID-19 disease (P<0.01). The effect of maximum humidity was higher than that of the other parameters (-0.364, P<0.01). A linear regression test showed that O3 (β=-15.16, P<0.001) and Tmean (β=18.47, P<0.01) significantly predicted daily COVID-19 cases. Conclusion: It can be concluded that climatic parameters can affect the COVID-19 pandemic and should be addressed.