Analysis of the Impact of Mask-wearing in Viral Spread: Implications for COVID-19

TL;DR

This paper analyzes how mask-wearing influences COVID-19 spread using complex network models, providing analytical predictions and simulations to quantify the effectiveness of masks in reducing epidemic size and transmission probability.

Contribution

It introduces a heterogeneous bond percolation model on multi-type networks to analytically predict mask impact on epidemic dynamics, combining theory and simulations.

Findings

Analytical predictions match simulation results for epidemic size and emergence probability.

Higher mask efficiency and greater mask-wearer proportion reduce epidemic spread.

The model draws parallels with multi-strain viral mutation models.

Abstract

Masks are used as part of a comprehensive strategy of measures to limit transmission and save lives during the COVID-19 pandemic. Research about the impact of mask-wearing in the COVID-19 pandemic has raised formidable interest across multiple disciplines. In this paper, we investigate the impact of mask-wearing in spreading processes over complex networks. This is done by studying a heterogeneous bond percolation process over a multi-type network model, where nodes can be one of two types (mask-wearing, and not-mask-wearing). We provide analytical results that accurately predict the expected epidemic size and probability of emergence as functions of the characteristics of the spreading process (e.g., transmission probabilities, inward and outward efficiency of the masks, etc.), the proportion of mask-wearers in the population, and the structure of the underlying contact network. In addition to the theoretical analysis, we also conduct extensive simulations on random networks. We also comment on the analogy between the mask-model studied here and the multiple-strain viral spreading model with mutations studied recently by Eletreby et al.