Masks Do No More Than Prevent Transmission: Theory and Data Undermine the Variolation Hypothesis

Abstract

Background. Masking serves an important role in reducing the transmission of respiratory viruses, including SARS-CoV-2. During the COVID-19 pandemic, several perspective and review articles have also argued that masking reduces the risk of developing severe disease by reducing the inoculum dose received by the contact. This hypothesis, known as the variolation hypothesis, has gained considerable traction since its development. Methods. To assess the plausibility of this hypothesis, we develop a quantitative framework for understanding the relationship between (i) inoculum dose and the risk of infection and (ii) inoculum dose and the risk of developing severe disease. We parameterize the mathematical models underlying this framework with parameters relevant for SARS-CoV-2 to quantify these relationships empirically and to gauge the range of inoculum doses in natural infections. We then identify and analyze relevant experimental studies of SARS-CoV-2 to ascertain the extent of empirical support for the proposed framework. Results. Mathematical models, when simulated under parameter values appropriate for SARS-CoV-2, indicate that the risk of infection and the risk of developing severe disease both increase with an increase in inoculum dose. However, the risk of infection increases from low to almost certain infection at low inoculum doses (with <1000 initially infected cells). In contrast, the risk of developing severe disease is only sensitive to dose at very high inoculum levels, above 106 initially infected cells. By drawing on studies that have estimated transmission bottleneck sizes of SARS-CoV-2, we find that inoculum doses are low in natural SARS-CoV-2 infections. As such, reductions in inoculum dose through masking or greater social distancing are expected to reduce the risk of infection but not the risk of developing severe disease conditional on infection. Our review of existing experimental studies support this finding. Conclusions. We find that masking and other measures such as distancing that act to reduce inoculum doses in natural infections are highly unlikely to impact the contact's risk of developing severe disease conditional on infection. However, in support of existing empirical studies, we find that masking and other mitigation measures that reduce inoculum dose are expected to reduce the risk of infection with SARS-CoV-2. Our findings therefore undermine the plausibility of the variolation hypothesis, underscoring the need to focus on other factors such as comorbidities and host age for understanding the heterogeneity in disease outcomes for SARS-CoV-2.

Severity of respiratory tract infections depends on the infectious dose. Perspectives for the next pandemic

“…This hypothesis is compatible with studies that show that increased ventilation and the use of face masks offer some protection against COVID-19 ( 25 – 27 ). Whereas the results from these studies also can be interpreted as reduced likelihood of a none-or-all process, ventilation and face masks reduces the exposure dose, which may in turn reduce the risk of getting infected as well as reduce the severity of illness.…”

The evolution of SARS-CoV-2

“…2b). The considerable role of stochasticity in the transmission of iSNVs through the transmission bottleneck impedes robust estimations of the magnitude of the selective advantage of mutants, except when selection is very strong 49,50 . D614G, and to a greater extent the emergence of VOCs, all featured striking selective advantages.…”

Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types

“…Mechanistic mathematical models, calibrated against virological data, are a powerful tool for exploring viral dynamics as they provide a framework to quantify key characteristics of different variants and build an understanding of the drivers of inter-individual variation in response to infection. Previous within-host modelling studies of SARS-CoV-2 have enabled an understanding of the effect of covariates such as age, sex and disease severity on viral load [24], the degree of heterogeneity between individuals [25], the association between viral dynamics and mortality in hospitalized patients [26], the pathogenesis of infection [27], and the potential effect of antivirals and masking on viral load [28][29][30][31][32][33][34][35][36]. Models fitted to data from vaccine trials have suggested correlates of protection [37], while more theoretical immunological models have also been developed with the ultimate aim of understanding the interplay between the immune response and disease severity [38][39][40].…”