Estimating the reduction in SARS-CoV-2 viral load by common face masks with a simple leak model

During the current pandemic and in the past, shortages of high quality respirators have forced people to protect themselves with homemade face masks that filter poorly in comparison to N95 respirators and are often designed in ways that makes them susceptible to leaks. Nevertheless, there is compelling epidemiological and laboratory evidence that face masks can be effective in impeding the spread of respiratory viruses such as SARS-CoV2. We explored this apparent inconsistency by developing a simple model of mask performance that estimates reduction of viral load during breathing and speaking as a function of breathing rate, relative humidity, mask surface area and leak size. The model uses viral concentrations measured in primates infected with COVID-19, existing data on exhaled speech and breath aerosol size distributions, and our filtration efficiency measurements of various mask materials with NaCl aerosol between 0.02 µm and 3 µm. Evaluating the model under a few selected conditions, we found that electrostatically charged materials perform the best but most uncharged fabrics remove up to >90% of the viral load on <10 µm particles in humid exhaled air if leaks are avoided, but modeled leaks around the nose dramatically reduce mask performance. As a consequence, when leaks are present, thinner and more breathable fabrics can outperform thicker materials which are less permeable and so force unfiltered air through the leak. The model also shows that most fabric masks provide some protection for the wearer from aerosols exhaled by others and dried to ambient humidity, reducing inhaled viral load by ∼20% when leaky and by ∼70% if sealed. Generally, masks with more surface area provide better protection.