Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios.

During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.

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.