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

Author

Listed:
  • 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
    Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, 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
    Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy)

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.

Suggested Citation

  • Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Gabriele Dassi & Piero Maria Brambilla & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2022. "Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9012-:d:870688
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    References listed on IDEAS

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