Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

Lorenzo Gianquintieri, Maria Antonia Brovelli, Andrea Pagliosa, Gabriele Dassi, Piero Maria Brambilla, Rodolfo Bonora, Giuseppe Maria Sechi and Enrico Gianluca Caiani
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Lorenzo Gianquintieri: Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy
Maria Antonia Brovelli: Civil and Environmental Engineering Department, Politecnico di Milano, 20133 Milan, Italy
Andrea Pagliosa: Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy
Gabriele Dassi: Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy
Piero Maria Brambilla: Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy
Rodolfo Bonora: Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy
Giuseppe Maria Sechi: Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy
Enrico Gianluca Caiani: Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy

IJERPH, 2022, vol. 19, issue 15, 1-19

Abstract: The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Keywords: COVID-19; machine learning; health geomatics; geographic information system; emergency medical services; spatial filtering; geo-AI; resources management (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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