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. We also
investigate trade-offs between masks with high outward efficiency but low
inward efficiency and masks with high inward efficiency but low outward
efficiency. Interestingly, we find that the former type of mask is most useful
for controlling the spread in the early stages of an epidemic, while the latter
type is most useful in mitigating the impact of an already large spread.
Lastly, we study whether degree-based mask allocation is more effective in
reducing probability as well as epidemic size compared to random allocation.
The result echoes the previous findings that spreading processes should be
treated with two different stages that source-control before epidemic starts
and self-protection after epidemic forms