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. 2020 Apr 21;5:293-308.
doi: 10.1016/j.idm.2020.04.001. eCollection 2020.

To Mask or Not to Mask: Modeling the Potential for Face Mask Use by the General Public to Curtail the COVID-19 Pandemic

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Free PMC article

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 et al. Infect Dis Model. .
Free PMC article

Abstract

Face mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial, though increasingly recommended, and the potential of this intervention is not well understood. We develop a compartmental model for assessing the community-wide impact of mask use by the general, asymptomatic public, a portion of which may be asymptomatically infectious. Model simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths. Moreover, mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness (as a fraction of potentially infectious contacts blocked) and coverage rate (as a fraction of the general population), while the impact on epidemiologic outcomes (death, hospitalizations) is highly nonlinear, indicating masks could synergize with other non-pharmaceutical measures. Notably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission. Hypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17-45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34-58%, absent other changes in epidemic dynamics. Even very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24-65% (and peak deaths 15-69%), compared to 2-9% mortality reduction in New York (peak death reduction 9-18%). Our results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic. The community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.

Keywords: COVID-19; Cloth mask; Face mask; N95 respirator; Non-pharmaceutical intervention; SARS-CoV-2; Surgical mask.

Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Relative peak hospitalizations and cumulative mortality under simulated epidemics, under either a base β0 = 0.5 or 1.5 day −1, under different general mask coverage levels and efficacies (where εo=εi=ε). Results are relative to a base case with no mask use. The left half of the figure gives these metrics as two-dimensional functions of coverage and efficacy. The right half gives these metrics as one-dimensional functions of coverage × efficacy.
Fig. 2
Fig. 2
Equivalent β0, 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 of β˜0 to the true β0 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 β0 the trainable parameter; the resulting β0 is the β˜0. This is done for simulated epidemics with a true β0 of 1.5, 1, or 0.5 day−1.
Fig. 3
Fig. 3
Epidemiologic outcomes and equivalent β0 changes as a function of mask coverage when masks are either much better at blocking outgoing (εo=0.8, εi=0.2) or incoming (ε0=0.2, εi=0.8) transmission. Results are demonstrated for both mask permutations under simulated epidemics with baseline β0 = 0.5 or 1.5 day −1.
Fig. 4
Fig. 4
Equivalent β0 under the model where all symptomatic persons wear a mask (whether they otherwise habitually wear a mask or not), under varying levels of efficacy for the masks given to the symptomatic (εoI), and in combination with different degrees of coverage and effectiveness for masks used by the rest of the general public. Results are for simulated epidemics with a baseline β0 of 1.5 day−1.
Fig. 5
Fig. 5
The left half of the figure gives model predictions and data for Washington state, using either a constant (top panels) or variable β (bottom panel), as described in the text. The right half of the figure is similar, but for New York state.
Fig. 6
Fig. 6
Simulated future (cumulative) death tolls for Washington state, using either a fixed (top panels) or variable (bottom panels) transmission rate, β, and nine different permutations of general public mask coverage and effectiveness. The y-axes are scaled differently in top and bottom panels.
Fig. 7
Fig. 7
Simulated future daily death rates for Washington state, using either a fixed (top panels) or variable (bottom panels) transmission rate, β, and nine different permutations of general public mask coverage and effectiveness. The y-axes are scaled differently in top and bottom panels.
Fig. 8
Fig. 8
Simulated future (cumulative) death tolls for New York state, using either a fixed (top panels) or variable (bottom panels) transmission rate, β, and nine different permutations of general public mask coverage and effectiveness.
Fig. 9
Fig. 9
Simulated future daily death rates for New York state, using either a fixed (top panels) or variable (bottom panels) transmission rate, β, and nine different permutations of general public mask coverage and effectiveness.

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