In Silico Fit Evaluation of Additively Manufactured Face Coverings.

In response to the respiratory protection device shortage during the COVID-19 pandemic, the additive manufacturing (AM) community designed and disseminated numerous AM face masks. Questions regarding the effectiveness of AM masks arose because these masks were often designed with limited (if any) functional performance evaluation. In this study, we present a fit evaluation methodology in which AM face masks are virtually donned on a standard digital headform using finite element-based numerical simulations. We then extract contour plots to visualize the contact patches and gaps and quantify the leakage surface area for each mask frame. We also use the methodology to evaluate the effects of adding a foam gasket and variable face mask sizing, and finally propose a series of best practices. Herein, the methodology is focused only on characterizing the fit of AM mask frames and does not considering filter material or overall performance. We found that AM face masks may provide a sufficiently good fit if the sizing is appropriate and if a sealing gasket material is present to fill the gaps between the mask and face. Without these precautions, the rigid nature of AM materials combined with the wide variation in facial morphology likely results in large gaps and insufficient adaptability to varying user conditions which may render the AM face masks ineffective.

Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model.

When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies - increased mask filtration efficiency, higher mask compliance, and elevated social distancing - in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess.

Retrospective analysis of interventions to epidemics using dynamic simulation of population behavior.

Retrospective analyses of interventions to epidemics, in which the effectiveness of strategies implemented are compared to hypothetical alternatives, are valuable for performing the cost-benefit calculations necessary to optimize infection countermeasures. SIR (susceptible-infected-removed) models are useful in this regard but are limited by the challenge of deciding how and when to update the numerous parameters as the epidemic changes in response to population behaviors. Behaviors of particular interest include facemask adoption (at various levels) and social distancing. We present a method that uses a "dynamic spread function" to systematically capture the continuous variation in the population behavior and the gradual change in infection evolution, resulting from interventions. No parameter updates are made by the user. We use the tool to quantify the reduction in infection rate realizable from the population of New York City adopting different facemask strategies during COVID-19. Assuming a baseline facemask of 67% filtration efficiency, calculations show that increasing the efficiency to 80% could have reduced the roughly 5000 new infections per day occurring at the peak of the epidemic to around 4000. Population behavior that may not be varied as part of the retrospective analysis, such as social distancing in a facemask analysis, are automatically captured as part of the calibration of the dynamic spread function.