The Effects of Air Quality on the Spread of the COVID-19 Pandemic in Italy: An Artificial Intelligence Approach

TL;DR

This study investigates the relationship between air quality and COVID-19 spread in Italy using AI techniques, revealing significant environmental correlations and proposing predictive models to aid decision-making.

Contribution

It introduces an AI-based analysis linking environmental factors to COVID-19 cases and develops predictive models for future infection trends in Italy.

Findings

Significant correlation between environmental parameters and COVID-19 cases

AI models can predict future infection numbers based on environmental data

Environmental factors may influence the spread of COVID-19

Abstract

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.