A data-driven analysis of the impact of non-compliant individuals on epidemic diffusion in urban settings

TL;DR

This study uses detailed contact networks and a heterogeneous epidemic model to analyze how non-compliant individuals influence disease spread in urban Italian cities, highlighting the importance of targeted interventions.

Contribution

It introduces a data-driven, spatially heterogeneous model distinguishing compliant and non-compliant individuals, revealing their significant impact on epidemic dynamics in urban settings.

Findings

Small proportions of non-compliant individuals increase infections and accelerate peaks.

Impact of non-compliance is greatest at moderate transmission rates.

Infection hotspots vary with disease transmission and non-compliance distribution.

Abstract

Individuals who do not comply with public health safety measures pose a significant challenge to effective epidemic control, as their risky behaviours can undermine public health interventions. This is particularly relevant in urban environments because of their high population density and complex social interactions. In this study, we employ detailed contact networks, built using a data-driven approach, to examine the impact of non-compliant individuals on epidemic dynamics in three major Italian cities: Torino, Milano, and Palermo. We use a heterogeneous extension of the Susceptible-Infected-Recovered model that distinguishes between ordinary and non-compliant individuals, who are more infectious and/or more susceptible. By combining electoral data with recent findings on vaccine hesitancy, we obtain spatially heterogeneous distributions of non-compliance. Epidemic simulations demonstrate that even a small proportion of non-compliant individuals in the population can substantially increase the number of infections and accelerate the timing of their peak. Furthermore, the impact of non-compliance is greatest when disease transmission rates are moderate. Including the heterogeneous, data-driven distribution of non-compliance in the simulations results in infection hotspots forming with varying intensity according to the disease transmission rate. Overall, these findings emphasise the importance of monitoring behavioural compliance and tailoring public health interventions to address localised risks.