A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian Province

TL;DR

This paper presents a data-driven epidemic model incorporating social structure and age-specific contact patterns to understand and forecast COVID-19 spread in Pavia, Italy, aiding healthcare planning and vaccination strategies.

Contribution

It introduces a novel mathematical model combining social contact data with epidemiological analysis, calibrated on detailed local infection data, to improve COVID-19 spread prediction and intervention planning.

Findings

Disease spread closely related to average contacts per individual

Model accurately forecasts hospitalizations with confidence intervals

Effective vaccination strategies can be tested using the model

Abstract

In this work, using a detailed dataset furnished by National Health Authorities concerning the Province of Pavia (Lombardy, Italy), we propose to determine the essential features of the ongoing COVID-19 pandemic in term of contact dynamics. Our contribution is devoted to provide a possible planning of the needs of medical infrastructures in the Pavia Province and to suggest different scenarios about the vaccination campaign which possibly help in reducing the fatalities and/or reducing the number of infected in the population. The proposed research combines a new mathematical description of the spread of an infectious diseases which takes into account both age and average daily social contacts with a detailed analysis of the dataset of all traced infected individuals in the Province of Pavia. These information are used to develop a data-driven model in which calibration and feeding of the model are extensively used. The epidemiological evolution is obtained by relying on an approach based on statistical mechanics. This leads to study the evolution over time of a system of probability distributions characterizing the age and social contacts of the population. One of the main outcomes shows that, as expected, the spread of the disease is closely related to the mean number of contacts of individuals. The model permits to forecast thanks to an uncertainty quantification approach and in the short time horizon, the average number and the confidence bands of expected hospitalized classified by age and to test different options for an effective vaccination campaign with age-decreasing priority.