Impact of homophily in adherence to anti-epidemic measures on the spread of infectious diseases in social networks

TL;DR

This study explores how homophily in adherence to anti-epidemic measures influences epidemic spread, revealing that increased separation between behavioral groups can paradoxically increase infections in clustered social networks.

Contribution

It introduces a modified SIR model incorporating behavioral homophily and demonstrates the complex effects of group separation on epidemic dynamics in clustered networks.

Findings

Higher group separation can increase infections in compliant groups within clustered networks.

Local clustering significantly influences the impact of behavioral homophily on epidemic spread.

Counterintuitive effects occur only in networks with clustering, not in random-like networks.

Abstract

We investigate how homophily in adherence to anti-epidemic measures affects the final size of epidemics in social networks. Using a modified SIR model, we divide agents into two behavioral groups-compliant and non-compliant-and introduce transmission probabilities that depend asymmetrically on the behavior of both the infected and susceptible individuals. We simulate epidemic dynamics on two types of synthetic networks with tunable inter-group connection probability: stochastic block models (SBM) and networks with triadic closure (TC) that better capture local clustering. Our main result reveals a counterintuitive effect: under conditions where compliant infected agents significantly reduce transmission, increasing the separation between groups may lead to a higher fraction of infections in the compliant population. This paradoxical outcome emerges only in networks with clustering (TC), not in SBM, suggesting that local network structure plays a crucial role. These findings highlight that increasing group separation does not always confer protection, especially when behavioral traits amplify within-group transmission.