The Effects of Air Quality on the Spread of the COVID-19 Pandemic in Italy: An Artificial Intelligence Approach
This work addresses public health decision-making during the pandemic by identifying environmental risk factors, but it is incremental as it applies existing methods to new data.
The study investigated the relationship between air quality and COVID-19 spread in Italy, finding a significant association between environmental factors like pollutants and daily cases, suggesting machine learning models could accurately predict future infections.
The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events. Artificial intelligence techniques may be useful in analyzing data related to areas affected by the virus. The aim of this work is to investigate any possible relationships between air quality and confirmed cases of COVID-19 in Italian districts. Specifically, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained on the environmental parameters to predict the number of future infected cases may be accurate. Predictive models may be useful for helping institutions in making decisions for protecting the population and contrasting the pandemic.