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Given the geographic heterogeneity of the COVID-19 pandemic, is it possible to assess the outcomes of delayed social distancing policies within any one geographic location?
In this decision analytical model of 1.3 million people in St Louis, Missouri, a delay of 2 weeks in public health policies initiated on March 17, 2020, was estimated to be associated with a nearly 6-fold total increase in deaths due to COVID-19 by June 15, 2020.
These findings suggest that timely local social distancing policies are associated with the number of COVID-19–related hospitalizations and deaths; local public health policies may avoid more severe pandemic consequences even in a widespread pandemic.
In the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear.
To use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed.
Design, Setting, and Participants
A previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model.
Hypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies.
Main Outcomes and Measures
Hospitalizations and deaths.
A model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI: 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)—an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections.
Conclusions and Relevance
The results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings.
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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.
Accepted for Publication: June 27, 2021.
Published: September 1, 2021. doi:10.1001/jamanetworkopen.2021.23374
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Geng EH et al. JAMA Network Open.
Corresponding Author: Elvin H. Geng, MD, MPH, Washington University School of Medicine in St Louis, 4523 Clayton Ave, CB 8051, St Louis, MO 63110 (email@example.com).
Author Contributions: Dr Geng and Mr Schwab had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Geng, Fox, Schootman, Mody, Powderly, Yount, Petersen.
Acquisition, analysis, or interpretation of data: Geng, Schwab, Foraker, Fox, Hoehner, Schootman, Mody, Powderly, Woeltje, Petersen.
Drafting of the manuscript: Geng, Petersen.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Geng, Schwab, Fox, Schootman, Petersen.
Obtained funding: Geng, Powderly.
Administrative, technical, or material support: Mody, Yount, Woeltje.
Supervision: Geng, Woeltje, Petersen.
Conflict of Interest Disclosures: Dr Geng reported receiving an educational grant from Viiv Healthcare and grants from the National Center for Advancing Translational Sciences/the National Institutes of Health Washington University Institute of Clinical and Translational Sciences during the conduct of the study. Dr Schwab reported receiving personal fees from Washington University in St Louis during the conduct of the study. Dr Powderly reported receiving personal fees from Merck Labs outside the submitted work. Dr Petersen reported receiving personal fees from University of Washington as a methodology consultant outside the submitted work. No other disclosures were reported.
Funding/Support: This study was supported by grant No. 5221 from the Institute for Public Health, Washington University in St Louis, Missouri (Dr Geng).
Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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