Abstract
BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran. Study Design: An ecological study. METHODS: In this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO(2) ), nitrogen dioxide (NO(2) ), nitric oxide (NO), ozone (O(3) ), carbon monoxide (CO), temperature (T), relative humidity (RH), dew point (DP), air pressure (AP), and wind speed (WS). RESULTS: Based on the three ML techniques, NO(2) (lag 5 day), CO (lag 4), and T (lag 25) were the most important environmental features affecting the spread of COVID-19 infection. In addition, the most important features contributing to hospitalization due to COVID-19 included RH (lag 28), T (lag 11), and O(3) (lag 10). After adjusting for the number of infections, the most important features affecting the number of deaths caused by COVID-19 were NO(2) (lag 20), O(3) (lag 22), and NO (lag 23). CONCLUSION: Our findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures.