  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.
