1906. Impact of Mask Mandate Timing on Community Transmission of SARS-CoV-2 in the St. Louis Metropolitan Area

Abstract

Abstract Background Mask mandates have been a widely used public health tool during the COVID-19 pandemic, but how to optimize their impact in the setting of concurrent but spontaneous population-level behavior changes due to rising case counts is not known. This study aimed to examine how earlier or later mask mandate implementation in the context of spontaneous behavior change would have affected transmission of SARS-CoV-2 and severe COVID-19 outcomes in the St. Louis, Missouri area. Methods Our model utilized aggregated hospitalization and death data for St. Louis city and county residents admitted to nearly all hospitals in the metropolitan area. We first fit a real-life model to estimate changes in transmission after the July 3, 2020 mask mandate, and then created counterfactual scenarios in which 1) 10%, 25%, and 50% of the changes were attributed to the mandate (as opposed to spontaneous behavior change) and 2) the mandate was implemented 3 or 7 days earlier, or 7 or 14 days later. We used an SEIR (Susceptible-Exposed-Infectious-Recovered) model framework and fit models in R. Results Assuming that 50% of increased masking was due to the mandate, implementing a mandate 7 days earlier was associated with a reduction from 12,685 (IQR: 10,463-16,560) to 12,294 (10,296-15,205) cumulative hospitalizations by September 30, while a 2-week delay was associated with an increase to 13,277 (10,808-17,908) hospitalizations. Trends were similar, but with reduced magnitude, when assuming that 10% or 25% of increased masking was due to the mandate (Figure). Depending on whether 10%, 25%, or 50% of increased masking was due to the mandate, implementing the mandate 1 week early was associated with a return to baseline (June 26) hospital census 1-7 days earlier, while delaying the mandate by 2 weeks led to a 2-12 day delay in return to baseline. Hospital census and cumulative deaths in the real-life (baseline) model and under 12 counterfactual scenarios which vary mask mandate timing (3 or 7 days earlier, or 7 or 14 days delayed) and percentage of increase in masking that is attributed to the mask mandate (Panels A-B: 10%, Panels C-D: 25%, and Panels E-F: 50%). As more of the increase in masking is attributed to the mandate, the costs of delaying the mandate and the benefits of earlier implementation increase. While differences in hospital census are most apparent several weeks after the mandate, differences in deaths gradually become more apparent over time. Conclusion Impact of a mask mandate depends on both timing and percent of increased masking that is attributed to the mandate. Implementing a mandate even a few days earlier is associated with fewer cumulative hospitalizations and earlier return to baseline, but the overall duration of implementation is slightly longer. Given wide variations in public behavior, locally-tailored models are essential for estimating the impact of interventions and informing the local public health response. Disclosures All Authors: No reported disclosures.