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

Luca Dede’ (), Nicola Parolini, Alfio Quarteroni, Giulia Villani and Giovanni Ziarelli
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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
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

Mathematics, 2025, vol. 13, issue 5, 1-31

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.

Keywords: epidemiological model; numerical simulation; validation on COVID-19 epidemic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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