To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Steffen E. Eikenberry; Marina Mancuso; Enahoro Iboi; Tin Phan; Keenan Eikenberry; Yang Kuang; Eric Kostelich; Abba B. Gumel

Note that the product ϵ × π predicts quite well the effect of mask deployment: Figure 1 also shows (relative) peak hospitalizations and cumulative deaths as functions of this product. There is, however, a slight asymmetry between coverage and efficacy, such that increasing coverage of moderately effective masks is generally more useful than increasing the effectiveness of masks from a starting point of moderate coverage. Figure 2 : Equivalent β 0 ,β 0 (infectious contact rate) under baseline model dynamics as a function of mask coverage × efficacy, with the left panel giving the absolute value, and the right giving the ratio ofβ 0 to the true β 0 in the simulation with masks. That is, simulated epidemics are run with mask coverage and effectiveness ranging from 0 to 1, and the outcomes are tracked as synthetic data. The baseline model without mask dynamics is then fit to this synthetic data, with β 0 the trainable parameter; the resulting β 0 is theβ 0 . This is done for simulated epidemics with a true β 0 of 1.5, 1, or 0.5 day −1 .