# Impact of Homophily in Adherence to Anti-Epidemic Measures on the Spread of Infectious Diseases in Social Networks

## TL;DR

This study shows that in social networks, increasing separation between groups who follow or ignore anti-epidemic measures can paradoxically lead to more infections in the compliant group.

## Contribution

The paper introduces a modified SIR model with behavioral groups and asymmetric transmission to show how network structure and homophily affect epidemic outcomes.

## Key findings

- Higher group separation can increase infections in the compliant population when transmission is reduced by compliance.
- This effect occurs only in clustered networks, not in stochastic block models.
- Local network structure significantly influences the impact of behavioral homophily on epidemics.

## 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.