The Role of Masks in Mitigating Viral Spread on Networks

TL;DR

This paper presents a comprehensive analysis of how different types of masks influence viral spread on networks, using a novel agent-based model and analytical expressions to inform effective mitigation strategies.

Contribution

It introduces a new agent-based model incorporating various mask types and derives analytical formulas for key epidemiological metrics, enhancing understanding of mask efficacy in network-based viral spread.

Findings

Masks with high outward efficiency are most effective early in an epidemic.

Masks with high inward efficiency are better for reducing epidemic size after spread.

Degree-based mask allocation outperforms random allocation in mitigation.

Abstract

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: the probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and trade-offs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Lastly, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.