Small coverage effect in epidemic network models shows that masks can become more effective with less people wearing them

TL;DR

This study reveals that in epidemic networks, mask effectiveness can increase even with fewer mask wearers due to network dynamics, challenging conventional beliefs about protective behavior.

Contribution

The paper introduces a simulation model showing that lower mask coverage can sometimes lead to greater individual protection, highlighting the importance of network effects.

Findings

Mask coverage of 10% can reduce infection risk by nearly 30%.

Reductions in risk range between 5% and 15% depending on coverage.

Small, tightly connected groups can prevent outbreaks despite low overall mask adherence.

Abstract

The effectiveness of non-pharmaceutical interventions to curb the spread of SARS-CoV-2 is determined by numerous contextual factors, including adherence. Conventional wisdom holds that the effectiveness of protective behaviour such as wearing masks always increases with the number of people adopting it. Here we show in a simulation study that this is not true in general. We employ a parsimonious network model based on the well-established empirical facts that (i) adherence to such interventions wanes over time and (ii) individuals tend to align their adoption strategies with their close social ties (homophily). When combining these assumptions, a broad dynamical regime emerges where the individual-level infection risk reduction for those adopting protective behaviour increases as the adherence to protective behavior decreases. For instance, for a protective coverage of 10% we find the infection risk for adopting individuals can be reduced by close to 30% compared to situations where the coverage is 60%. Using estimates for the effectiveness of surgical masks, we find that reductions in relative risk of masking versus non-masking individuals range between 5% and 15%, i.e., vary by a factor of three. This small coverage effect originates from system-dynamical network properties that conspire to increase the chance that an outbreak will be over before the pathogen is able to invade small but tightly connected groups of individuals that protect themselves. Our results contradict the popular belief that masking becomes ineffectual as more people drop their masks and might have far-reaching implications for the protection of vulnerable population groups under resurgent infection waves.