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Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity

Author

Listed:
  • Najada Firza

    (Dipartimento di Economia e Finanza, Università degli Studi di Bari “Aldo Moro”, Largo Abbazia S. Scolastica, 70124 Bari, Italy
    Faculty of Economic, Political and Social Sciences, Catholic University Our Lady of Good Counsel, Rr. Dritan Hoxha 123, Laprake, 1031 Tirana, Albania)

  • Alfonso Monaco

    (Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
    Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70124 Bari, Italy)

Abstract

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated ( 24.6 % ) together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.

Suggested Citation

  • Najada Firza & Alfonso Monaco, 2022. "Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity," IJERPH, MDPI, vol. 19(19), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12538-:d:931172
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