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A Multi-Age Multi-Group Epidemiological Model and Its Validation on the COVID-19 Epidemic in Italy: SEIHRDV

Author

Listed:
  • Luca Dede’

    (MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy)

  • Nicola Parolini

    (MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy)

  • Alfio Quarteroni

    (MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy
    Institute of Mathematics, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland)

  • Giulia Villani

    (Department of Mathematics, University of Rome “La Sapienza”, 00185 Rome, Italy)

  • Giovanni Ziarelli

    (Department of Mathematics, Università degli Studi di Milano, 20133 Milan, Italy)

Abstract

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, validated using data from Italy starting in September 2020. SEIHRDV includes the following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D), and Vaccinated (V). The model is age-stratified, with the population divided into 15 age groups, and it considers seven different contexts of exposure to infection (family, home, school, work, transport, leisure, and other contexts), which impact the transmission mechanism. The primary goal of this work is to provide a valuable tool for analyzing the spread of the epidemic in Italy during 2020 and 2021, supporting the country’s decision making processes. By leveraging the SEIHRDV model, we analyzed epidemic trends, assessed the efficacy of non-pharmaceutical interventions, and evaluated vaccination strategies, including the introduction of the Green Pass, a containment measure implemented in Italy in 2021. The model proved instrumental in conducting comprehensive what-if studies and scenario analyses tailored to Italy and its regions. Furthermore, SEIHRDV facilitated accurate forecasting of the future potential trajectory of the epidemic, providing critical insights for improved public health strategies and informed decision making for authorities.

Suggested Citation

  • Luca Dede’ & Nicola Parolini & Alfio Quarteroni & Giulia Villani & Giovanni Ziarelli, 2025. "A Multi-Age Multi-Group Epidemiological Model and Its Validation on the COVID-19 Epidemic in Italy: SEIHRDV," Mathematics, MDPI, vol. 13(5), pages 1-31, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:788-:d:1601436
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    References listed on IDEAS

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    1. Amit Summan & Arindam Nandi, 2022. "Timing of non-pharmaceutical interventions to mitigate COVID-19 transmission and their effects on mobility: a cross-country analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(1), pages 105-117, February.
    2. Phenyo E. Lekone & Bärbel F. Finkenstädt, 2006. "Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study," Biometrics, The International Biometric Society, vol. 62(4), pages 1170-1177, December.
    3. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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