key: cord-0934012-ra3clbbr
authors: Geng, Elvin H.; Schwab, Joshua; Foraker, Randi; Fox, Branson; Hoehner, Christine M.; Schootman, Mario; Mody, Aaloke; Powderly, William; Yount, Byron; Woeltje, Keith; Petersen, Maya
title: Outcomes Associated With Social Distancing Policies in St Louis, Missouri, During the Early Phase of the COVID-19 Pandemic
date: 2021-09-01
journal: JAMA Netw Open
DOI: 10.1001/jamanetworkopen.2021.23374
sha: fe29d1741d0cadeb00b08cf621d7f7a4cb4abc18
doc_id: 934012
cord_uid: ra3clbbr

IMPORTANCE: 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. OBJECTIVE: 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. EXPOSURES: 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. RESULTS: 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.

The Local Epidemic Modeling for Management & Action (LEMMA) model used in this analysis is a discrete time compartmental epidemic model. 1 The version of the model (V 1.0; https://github.com/LocalEpi/LEMMA/tree/StLouis) used in the current analysis is an eight compartment model, which extends the classic four compartment Susceptible-Exposed-Infectious-Removed structure to incorporate disease severity, hospitalization and ICU use. Specifically, the population is conceptualized as existing in one of 8 states: 1) Susceptible to infection; 2) Exposed to SARS Cov-2, but not yet Infectious; 3a) Infected with SARS Cov-2 and Infectious to others with a more severe infection that will (with delay) lead to hospitalization; 3b) Infected with SARS Cov-2 and Infectious to others with a mild infection that will not lead to hospitalization; 4a) Hospitalized, but not in the ICU; 4b) Hospitalized in the ICU; 5a) Deceased; 5b) Recovered and Immune.

Model dynamics are deterministic on a discrete time scale. Specifically, on each day a fixed proportion of exposed become infected, a fixed proportion of infected become either hospitalized or recover and a fixed proportion of those in the ICU either die or recover. The proportion of those susceptible that become exposed is proportional to the number of infected and the time-varying effective contact rate parameter. Key epidemiologic and context parameters are treated as unknown and estimated based on local input data and user-specified prior distributions using Bayesian inference methods. Specifically, in the version of the model used in this analysis, posterior distributions on the following parameters were estimated: Basic reproductive number R0 before Intervention; Number of Days from Infection to Becoming Infectious (Latent Period); Duration of infectiousness (days); Time from onset of infectiousness to hospitalization (days); Average Hospital Length of Stay for Patients not in ICU (Days); Average Hospital Length of Stay for Patients in ICU (Days); Percent of Infected that are Hospitalized; Percent of Hospitalized COVID-19 Patients That are Currently in the ICU; Mortality Rate among ICU COVID-19 Patients. In the version of the model used in this analysis, the timing and magnitude of changes in the effective contact rate (and by extension, the effective reproductive number) constituted additional parameters with posterior distributions estimated during model calibration.

Point estimates and credibility intervals for unknown parameters are generated based on data inputs using the Stan programming language. Stan uses Markov chain Monte Carlo methods, in particular the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. 2, 3 As with compartmental models generally, additional assumptions of the model include a closed population and random mixing. The hospitalization model used in this analysis also made a number of simplifying assumptions, with the aim of decreasing the number of parameters to be estimated from data, an important consideration particularly early during the pandemic when reliable data available to calibrate the model were sparse. Specifically, the hospitalization model used here partitioned hospitalized patents into ICU and non-ICU patients, rather than explicitly modeling transitions between these states. It further made the simplifying assumption that all deaths occur among ICU patients. The current version of the LEMMA model (V 2.0) relaxes the above simplifying assumptions and uses a hospital model with more complexity. Due to the early stage of the epidemic to which it was applied, the version of the model used in this analysis further did not incorporate the possibility of waning immunity or re-infection. Version 2.0 allows for (user-specified) transitions from a recovered immune state back to a susceptible state.

In the current analysis, the model was calibrated to hospital census data only, given the sparsity of reliable testing data and other data sources available early during the pandemic. LEMMA supports the option to calibrate to multiple additional data sources, including deaths, cases, hospital admissions, ICU use, and, in Version 2.0, regional seroprevalence estimates and vaccine doses distributed. Incorporation of variants is also supported via input parameters on relative variant prevalence and growth rates, as well as epidemiologic characteristics of the variants (eg., relative transmissibility and severity, vaccine efficacy after first and second dose). Given the early stage of the epidemic to which it was applied, however, the current analysis employed a simpler model structure with no vaccine deployment and a single variant.

All code is open source and available on Github (https://github.com/LocalEpi/LEMMA/). Users can flexibly modify prior distributions on parameters, data inputs, and scenarios considered (including vaccine deployment, changes in effective contact rate, and variants) through either an Excel interface available on Github, or via an R shiny Application (https://localepi.shinyapps.io/LEMMA-Shiny; with automated data integration for all California counties).

Using data from hospital census up until April 1 (Panel A) or May 1 (Panel B)indicated as black vertical line on those datesto calibrate the model, subsequent projections over time resemble actuals in both the number hospitalized over time as well as cumulative deaths. Projected and actuals were very similar over 12 weeks after last data input. 

Local Epidemiological Modeling for Management and Action

Version 1.0. The MIT Open Source License (MIT) Copyright (c) 2020 LEMMA

Stan Development Team. 2021. Stan Modeling Language Users Guide and Reference Manual, 2

Stan: A probabilistic programming language

Projections exploring additional scenarios exploring counterfactual scenarios in which rapid behavior change occurs in the absence of social distancing policies and leads to an additional drop in effective reproductive number by 50% when hospitalizations reach a "panic" threshold of 600, 800 or 1000.