Impact of behavioral heterogeneity on epidemic outcome and its mapping into effective network topologies
Fabio Mazza, Gabriele Ricci, Francesca Colaiori, Stefano Guarino, Sandro Meloni, Fabio Saracco

TL;DR
This paper introduces a minimal extended SIR model, HeSIR, capturing behavioral heterogeneity and homophily effects, deriving epidemic thresholds, and mapping the dynamics onto modified networks to better understand epidemic outcomes.
Contribution
It presents the HeSIR model incorporating behavioral heterogeneity, derives analytical epidemic thresholds, and links epidemic dynamics to network structure, advancing understanding of behavioral impacts on epidemics.
Findings
Heterogeneity and homophily significantly influence epidemic thresholds.
A resurgence regime can occur just beyond the classical epidemic threshold.
The HeSIR model maps onto a standard SIR process on a modified network.
Abstract
Human behavior plays a critical role in shaping epidemic trajectories. During health crises, people respond in diverse ways in terms of self-protection and adherence to recommended measures, largely reflecting differences in how individuals assess risk. This behavioral variability induces effective heterogeneity into key epidemic parameters, such as infectivity and susceptibility. We introduce a minimal extension of the susceptible-infected-removed~(SIR) model, denoted HeSIR, that captures these effects through a simple bimodal scheme, where individuals may have higher or lower transmission--related traits. We derive a closed-form expression for the epidemic threshold in terms of the model parameters, and the network's degree distribution and homophily, defined as the tendency of like--risk individuals to preferentially interact. We identify a resurgence regime just beyond the classical…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Complex Network Analysis Techniques · Mental Health Research Topics
