Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation

Abstract

Masks are a vital tool for limiting SARS-CoV-2 spread in the population. Here we utilize a mathematical model to assess the impact of masking on transmission within individual transmission pairs and at the population level. Our model quantitatively links mask efficacy to reductions in viral load and subsequent transmission risk. Our results reinforce that the use of masks by both a potential transmitter and exposed person substantially reduces the probability of successful transmission, even if masks only lower exposure viral load by ~ 50%. Slight increases in mask adherence and/or efficacy above current levels would reduce the effective reproductive number (Re) substantially below 1, particularly if implemented comprehensively in potential super-spreader environments. Our model predicts that moderately efficacious masks will also lower exposure viral load tenfold among people who get infected despite masking, potentially limiting infection severity. Because peak viral load tends to occur pre-symptomatically, we also identify that antiviral therapy targeting symptomatic individuals is unlikely to impact transmission risk. Instead, antiviral therapy would only lower Re if dosed as post-exposure prophylaxis and if given to ~ 50% of newly infected people within 3 days of an exposure. These results highlight the primacy of masking relative to other biomedical interventions under consideration for limiting the extent of the COVID-19 pandemic prior to widespread implementation of a vaccine. To confirm this prediction, we used a regression model of King County, Washington data and simulated the counterfactual scenario without mask wearing to estimate that in the absence of additional interventions, mask wearing decreased Re from 1.3–1.5 to ~ 1.0 between June and September 2020.

Effects of face masks and ventilation on the risk of SARS-CoV-2 respiratory transmission in public toilets: a quantitative microbial risk assessment

“…This study supports the recommendation for a person, as a receptor, to use a surgical mask or an N95 respirator as personal protective equipment to minimize the associated risk of infection in unventilated public toilets and potentially in other confined communal spaces. In general, wearing a mask was shown to be one of the most low-cost, simple yet effective intervention measures to minimize transmission risk, which is consistent with reports from other studies (Asadi, Cappa, et al, 2020;Chu, Akl, et al, 2020;WHO, 2020b;Cheng, Cheng, et al, 2021;Goyal, Reeves, et al, 2021).…”

ACE2 Receptor-Modified Algae-Based Microrobot for Removal of SARS-CoV-2 in Wastewater

“… 33 However, any reduction of viral load would have a positive correlation with the control of viral infectivity and the illness of patients. 34 The algae microrobots are expected to have greater mobility and longer lifetime in an open wastewater reservoir when compared to a confined test tube and thus hold considerable potential for future scaling-up applications. Given that new emerging SARS-CoV-2 variants show a similar binding mechanism and higher binding affinity with the ACE2 receptor, 35 , 36 the functionalized algae microrobotic platform is expected to efficiently remove different virus variants from wastewater.…”

Mathematical Modeling of Vaccines That Prevent SARS-CoV-2 Transmission

“…This model was previously employed to demonstrate the importance of rapid vaccination rates to limit the severity of a fourth wave in King County Washington [14]. In the next section of the paper, we employed an intra-host transmission model [22,35,41] to evaluate whether reduction in peak viral load could serve as a potential correlative endpoint for VE INF , which could then inform human viral challenge study designs which might provide actionable vaccine efficacy estimates within relevant timeframes for the pandemic.…”

“…We also employed our previously described model linking transmitter viral load with probability of transmission [22,35], which was validated against published data including variability in number of infections generated by individuals and distributions in observed serial intervals [21,51,52]. As previously described, the model output predicts a transmission dose response curve which captures probability of transmission given an exposure viral load [35,41]. The details of this model are described in the Supplement and in Supplementary Figure S1.…”