arXiv2021
Face masks have been widely used as a protective measure against COVID-19. However, pre-pandemic empirical studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses. The implications of the studies' recognized limitations have not been quantitatively and statistically analyzed, leading to confusion regarding the effectiveness of masks. Such confusion may have contributed to organizations such as the WHO and CDC initially not recommending that the general public wear masks. Here we show that when the adherence to mask-usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analyzed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100% effective, the studies in question would still have been unlikely to find a statistically significant effect. We also provide a framework for understanding the effect of masks on the probability of infection for single and repeated exposures. The framework demonstrates that more frequently wearing a mask provides super-linearly compounding protection, as does both the susceptible and infected individual wearing a mask. This work shows (1) that both theoretical and empirical evidence is consistent with masks protecting against respiratory infections and (2) that nonlinear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.