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 corre-lation between nitrogen dioxide (N0O), mobility-related parameters, and COVID-19 data. However, given the complexity of the virus spread mechanism, which is re-lated to many different factors, these preliminary stud-ies 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.
Sebastianelli A., Mauro F., Di Cosmo G., Passarini F., Carminati M., Ullo S.L. (2022). A Decision Support System Based on Machine Learning to Counteract Covid-Like Pandemic Events. Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS46834.2022.9883847].
A Decision Support System Based on Machine Learning to Counteract Covid-Like Pandemic Events
Passarini F.;
2022
Abstract
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 corre-lation between nitrogen dioxide (N0O), mobility-related parameters, and COVID-19 data. However, given the complexity of the virus spread mechanism, which is re-lated to many different factors, these preliminary stud-ies 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
