Mask or no mask for COVID-19: A public health and market study

Abstract

Efficient strategies to contain the coronavirus disease 2019 (COVID-19) pandemic are peremptory to relieve the negatively impacted public health and global economy, with the full scope yet to unfold. In the absence of highly effective drugs, vaccines, and abundant medical resources, many measures are used to manage the infection rate and avoid exhausting limited hospital resources. Wearing masks is among the non-pharmaceutical intervention (NPI) measures that could be effectively implemented at a minimum cost and without dramatically disrupting social practices. The mask-wearing guidelines vary significantly across countries. Regardless of the debates in the medical community and the global mask production shortage, more countries and regions are moving forward with recommendations or mandates to wear masks in public. Our study combines mathematical modeling and existing scientific evidence to evaluate the potential impact of the utilization of normal medical masks in public to combat the COVID-19 pandemic. We consider three key factors that contribute to the effectiveness of wearing a quality mask in reducing the transmission risk, including the mask aerosol reduction rate, mask population coverage, and mask availability. We first simulate the impact of these three factors on the virus reproduction number and infection attack rate in a general population. Using the intervened viral transmission route by wearing a mask, we further model the impact of mask-wearing on the epidemic curve with increasing mask awareness and availability. Our study indicates that wearing a face mask can be effectively combined with social distancing to flatten the epidemic curve. Wearing a mask presents a rational way to implement as an NPI to combat COVID-19. We recognize our study provides a projection based only on currently available data and estimates potential probabilities. As such, our model warrants further validation studies.

Fig 1. R_int and attack rate dependence on mask availability.

The R_int and attack rate a values are simulated based seven scenarios in Table 2. R_int 1 is calculated based on scenarios 1. The same annotation principle applies to all other R_int calculations.

Fig 2. Simulation on infected cases based on R_int.

All figures use two hypothetical controls. The blue dash line curve “all-infected 2.3” is simulated using R₀ value of 2.3. The red solid curve “all-infected s_d” is simulated using R₀ of 1.7 with relaxed social distancing, assuming a rough extrapolation of reducing about 50% of overall transmission risks in the general population. Fig 2A shows the applying social distancing and wearing a mask in three scenarios (S1,S2, and S3) when M_red is 57.5%. Fig 2B shows the applying social distancing and wearing a mask in two scenarios (S4 and S5) for two extreme conditions. S4 is the scenario when M_red = 40%, M_cov = 8% and M_ava = 5%.S5 is the scenario when M_red = 75%, M_cov = 100% and M_ava = 100%. Fig 2C shows the applying social distancing and wearing a mask in two scenarios (S6 and S7) for two intermediate conditions, S6 with M_red = 75%, M_cov = 8%, M_ava = 52.5%, and S7 with M_red = 75%, M_cov = 54%, M_ava = 5%.

Fig 3. Confirmed cases indifferent countries.