The impact of homophily on digital proximity tracing

TL;DR

This paper investigates how homophily in human interactions influences the effectiveness of digital proximity tracing, revealing complex relationships between mixing rates and epidemic metrics through analytical and simulation methods.

Contribution

It provides a mathematical analysis of homophily effects on digital contact tracing efficacy, including non-monotonous behaviors and regimes with local optima.

Findings

Reproduction number varies non-monotonously with mixing rate.

Attack rate exhibits local optima, minima, or monotonic changes.

Monte Carlo simulations confirm analytical results.

Abstract

We study how homophily of human physical interactions affects the efficacy of digital proximity tracing. Analytical results show a non monotonous dependence of the reproduction number with respect to the mixing rate between individuals that adopt the contact tracing app and the ones that do not. Furthermore, we find regimes in which the attack rate has local optima, minima or monotonously varies with the mixing rate. We corroborate our findings with Monte Carlo simulations on a primary-school network. This study provides a mathematical basis to better understand how homophily in health behavior shapes the dynamics of epidemics.