1 Objectives: To evaluate the effectiveness of widespread adoption of masks or face coverings to reduce community transmission of the SARS-CoV-2 virus that causes Covid-19.
We employed an agent-based stochastic network simulation model, where Covid-19 progresses across census tracts according to a variant of SEIR. We considered a mask order that was initiated 3.5 months after the first confirmed Covid-19 case. We evaluated scenarios where wearing a mask reduces transmission and susceptibility by 50% or 80%; an individual wears a mask with a probability of 0%, 20%, 40%, 60%, 80%, or 100%.
Results: If 60% of the population wears masks that are 50% effective, this decreases the cumulative infection attack rate (CAR) by 25%, the peak prevalence by 51%, and the population mortality by 25%. If 100% of people wear masks (or 60% wear masks that are 80% effective), this decreases the CAR by 38%, the peak prevalence by 67%, and the population mortality by 40%.
After community transmission is present, masks can significantly reduce infections.
. CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.
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The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint
In the United States, by August 2020, SARS-CoV-2 that causes Covid-19 was widely circulating in many communities, especially throughout the South and Midwest (1) . There is much speculation over the role of masks or face coverings with some officials stating that if people would wear masks for a few weeks that community transmission could be stopped (2).
While there is growing empirical evidence that masks can be effective at reducing droplets (3, 4) , it is not well understood what this would mean across a population or if they can stop an outbreak when community transmission is already in existence. This study prospectively assesses the effectiveness of face coverings at the state level and across urban, suburban, and rural counties under different population compliance and mask effectiveness levels.
We employ an agent-based stochastic network model with an SEIR framework (5) for the progression of SARS-CoV-2 (6, 7). As in Keskinocak et al (7), we simulate interactions among agents, where transmission can occur daily in households, workplaces and schools, and community settings, with day/night differentiation in interactions. The network of agents is built using US Census data (8) at the tract level using household size, presence of children, and age groups (defined as <= 4, 9, 19 , 64, or above). Model parameters include the reproductive rate (2.4 without interventions), hospitalization and mortality rates (by age group), an effective overall Infection Fatality Rate just under 0.5%, and asymptomatic and symptomatic transmission coefficients (6, 7) . Full details are outlined in Supplementary Materials available online.
Analysis was performed for the representatiave state of North Carolina (NC), which has had moderate transmission as represented by deaths per capita(1). The population of 10.5 million people is represented with 1,017,720 agents. The simulation is seeded (day 1) with cumulative cases as of March 24(1), where the cases are multiplied by 10 to account for underreporting (9) and scaled to the number of agents in the simulation; the simulation is run for 365 days. County cases are distributed to random households across the county's census tracts proportionally to the tract population; the random location drives much of the uncertainty in the results.
The model accounts for distancing measures put in place similar to Keskinocak et al (7):
initially 60% of adults stay home, decreasing by 1 point weekly to a minimum of 50% for the is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint 2 46 day sheltering period in NC from March 24 to May 8. After the sheltering period, we assume that households with a symptomatic Covid-19 infection will voluntarily quarantine (low quarantining scenario from (7)), beginning at 60% and decreasing by 15 points each week to a minimum of 15%, based on comparisons to mobility data. Schools are closed throughout, and anyone who is symptomatic stays home.
Unlike Keskinocak et al (7), a proportion of the population wears masks. The rate increases approximately monthly (4/1 to 6/26 or days 8-94, corresponding to the state mask order), evenly from 0 to the final compliance probability of (0, 40, 60, 80, or 100%). We assume the rate is homogeneous across the population, as supported by recent reports (10). According to recent analysis(4), in the baseline mask cases we assume masks reduce the infectivity to others and susceptibility of self by 50% each. We compare the baseline mask effectiveness with scenarios where higher quality masks are employed and are more effective (e.g., 80%
reduction in transmission and susceptibility risk, like surgical or N95 masks). For our nointervention benchmark, we assume there are no interventions throughout the pandemic.
The model is validated against reported hospitalizations and deaths as of August 18, 2020.
All simulation output values are adjusted to the true population of 10.49 million.
We compute the infection attack rate (IAR) of all infections over the time horizon, the peak percentage of the population simultaneously infected, peak count of hospitalizations, and the mortality of the total population. We quantify the mean and standard deviation values over 15 replications. We provide values at the state level, by county and stratified by urban/suburban/rural status, where urban corresponds to Rural-Urban-Commuting-Area (RUCA) codes of 1,2; rural of 6, 7, 8, 9, 10 ; and the remainder for suburban (11). In the studied state, 75% of people are in urban areas, 15% in suburban, and 10% in rural.
The funding supported the students performing the analysis; it had no relationship with the design of the experiments, interpretation of the results, or determining the conclusions.
This study uses publicly available de-identified data and does not require IRB approval.
The wearing of masks reduces cumulative infections, peak infections, hospitalizations, and mortality (Table 1 ). In the benchmark scenario with 0% masks, the IAR is 36%, the peak . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint infection rate is 1.15%, and the overall mortality is 0.176%, while the scenario where 60% wear masks those metrics are reduced by 25%, 51%, and 25%, respectively.
A higher rate of mask-wearing leads to improvement in each metric. For each 20 point increase in compliance (e.g., 60% compliance to 80% compliance), the IAR decreases by approximately 2.47 points, the mortality rate drops by approximately 0.014 points, and the peak prevalence decreases (14-37% of the previous value).
Notably, if 100% of people wear a mask that is 50% effective, then this results in an IAR of 22% (compared to 27.0% for 60% compliance). Hospitalizations decrease by 30% and deaths by 16.7% from the scenario with 60% compliance.
For all scenarios, community transmission continues to occur after the mask order, with the peak day of infection occurring approximately day 190.
If higher quality masks are worn, then all metrics improve. With 60% of people wearing masks, increasing the mask effectiveness from 50% to 80% is better than the scenario with 100% compliance where masks are 50% effective (see Figure 1 , which shows results over time, and Table 1 for corresponding summary values).
The results vary by RUCA county type. In the baseline scenario (60% compliance): urban, rural, and suburban counties have an average 28.1% , 23.3% and 24.5% IAR, respectively, as compared to 36.9%, 34.3%, and 32.1% with 0% compliance. The benefit of masks as measured by the percentage improvement in the IAR is slightly higher in rural and suburban areas than urban areas (see Figure 2 ).
The impact of masks is over and above that of the initial shelter in place and voluntary quarantining, where the no mask benchmark results in an IAR of 49%, peak prevalence of 3.03%, and mortality 76% higher than with 60% masks.
Widespread usage of masks decreases the impact of the pandemic, consistent with a deterministic aggregate model of disease spread (12) and empirical findings using publicly reported data from states April 1 to May 21 (13) . The impact can include 25% to 40% reductions in infections and mortality, over and above the distancing interventions in place. This is further support for states or other localities to put mask orders in place. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint However, even if 100% of people wore masks, this would not stop community transmission.
To stop transmission, other interventions are also needed, such as distancing, closures of some community settings, or a vaccine. Public health officials should take this into consideration in managing expectations of the public.
Improving the quality of masks worn also has the potential to improve population health, e.g., by shifting people from lower-effectiveness coverings like neck gaiters or bandanas to masks with multiple fabric layers or special filtration material like N95 masks. Public health organizations should consider adding this to messaging around face masks.
There are limitations with this work, including that models never match reality exactly.
Some of the key assumptions that are critical include the effectiveness of masks and the compliance in the population. If compliance is not uniform across geographies or subpopulations then results by subpopulation could differ.
In future research it would be useful to examine the role of vaccines in curbing the pandemic, compliance with masks or uptake differences in vaccine, and the link with equity across the population. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint 7 Figure 1 : Prevalence of infectious people over time is shown. Subfigure (top) has cases with different levels of population compliance (0%, 40%, 60%, 80%, 100%) while mask effectiveness is 50%. Subfigure (bottom) has cases with different levels of mask effectiveness (20%, 40%, 60%, 80%, 90%) while population compliance is 60%. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted September 29, 2020. . https://doi.org/10.1101/2020.09.27.20199737 doi: medRxiv preprint