A Decision Support System Based on Machine Learning to Counteract Covid-Like Pandemic Events

In this paper, the authors aim to design a decision support system (DSS) based on machine learning (ML) to assist institutions in implementing targeted countermeasures to combat and prevent emergencies such as the COVID -19 pandemic. The DSS relies on an ensemble of several ML models that combine heterogeneous data to predict risk levels at the micro and macro levels. Some preliminary analyses have already been conducted showing the correlation between nitrogen dioxide (NO2), mobility-related parameters, and COVID -19 data. However, given the complexity of the virus spread mechanism, which is related to many different factors, these preliminary studies confirmed the need to perform more in-depth analyses on the one hand and to use ML algorithms on the other hand to capture the hidden relationships between the huge amounts of data that need to be processed.