SEIHRDV: a multi-age multi-group epidemiological model and its validation on the COVID-19 epidemics in Italy

TL;DR

The paper introduces SEIHRDV, a detailed multi-age, multi-context epidemiological model for COVID-19, validated with Italian data, enabling analysis of interventions, vaccination strategies, and epidemic forecasting.

Contribution

It presents a novel, comprehensive COVID-19 model incorporating age groups and exposure contexts, enhancing epidemic analysis and policy evaluation.

Findings

Effective modeling of COVID-19 trends in Italy

Assessment of non-pharmaceutical interventions

Forecasting of epidemic trajectories

Abstract

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, which we validate using data from Italy starting in September 2020. SEIHRDV features the following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D) and Vaccinated (V). The model is age-stratified, as it considers the population split into 15 age groups. Moreover, it takes into account 7 different contexts of exposition to the infection (family, home, school, work, transport, leisure, other contexts), which impact on the transmission mechanism. Thanks to these features, the model can address the analysis of the epidemics and the efficacy of non-pharmaceutical interventions, as well as possible vaccination strategies and the introduction of the Green Pass, a containment measure introduced in Italy in 2021. By leveraging on the SEIHRDV model, we successfully analyzed epidemic trends during the COVID-19 outbreak from September 2020 to July 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 information for informed decision making and public health strategies.