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

2025-06-16

AI Summary

This study investigates the impact of noncompliant individuals—those who disregard public health measures—on epidemic transmission in urban settings. We develop an enhanced spatially heterogeneous SIR model grounded in high-resolution contact networks from three Italian cities, uniquely integrating sociobehavioral heterogeneity (i.e., noncompliance), demographic data, mobile phone mobility traces, electoral records, and vaccine hesitancy indicators. Our results demonstrate that even a small fraction (5–10%) of noncompliant individuals substantially increases cumulative infection incidence, advances epidemic peak timing, and most markedly exacerbates transmission under moderate baseline transmissibility. Critically, noncompliance drives the emergence of localized infection hotspots. The model successfully reproduces empirically observed regional infection patterns, enabling quantitative identification of high-risk subpopulations and informing targeted, evidence-based intervention strategies. This work provides a novel, data-driven framework for evaluating behavioral determinants of epidemic dynamics and optimizing public health responses in complex urban environments.

Technology Category

• Search and Optimization: Non-convex Optimization
• Application Domains: Humanities & Computational Social Science
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Impact of non-compliant individuals on urban epidemic spread
• Data-driven modeling of heterogeneous compliance in Italian cities
• Non-compliance increases infections and accelerates epidemic peaks

Innovation

• Data-driven contact networks for epidemic analysis
• Heterogeneous SIR model for non-compliant individuals
• Spatially heterogeneous non-compliance distributions