Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States
6 Pages Posted: 10 Jun 2020 Last revised: 1 Jul 2020
Date Written: May 12, 2020
Background: The United States has been particularly hard-hit by COVID-19, accounting for approximately 30% of all global cases and deaths from the disease that have been reported as of May 20, 2020. We extended our agent-based model for COVID-19 transmission to study the effect of alternative lockdown and reopening policies on disease dynamics in Georgia, Florida, and Mississippi. Specifically, for each state we simulated the spread of the disease had the state enforced its lockdown approximately one week earlier than it did. We also simulated Georgia's reopening plan under various levels of physical distancing if enacted in each state, making projections until June 15, 2020.
Methods: We used an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model was first calibrated to each state using publicly available COVID-19 death data as of April 23, then implemented to simulate given lockdown or reopening policies.
Results: Our model estimated that imposing lockdowns one week earlier could have resulted in hundreds fewer COVID-19-related deaths in the context of all three states. These estimates quantify the effect of early action, a key metric to weigh in developing prospective policies to combat a potential second wave of infection in each of these states. Further, when simulating Georgia’s plan to reopen select businesses as of April 27, our model found that a reopening policy that includes physical distancing to ensure no more than 25% of pre-lockdown contact rates at reopened businesses could allow limited economic activity to resume in any of the three states, while also eventually flattening the curve of COVID-19-related deaths by June 15, 2020.
Funding: This work was supported in part by the Army Research Office by grant MURI W911NF1810208 and in part by grant T32HD040128 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. Killian was supported by the National Science Foundation Graduate Research Fellowship by grant DGE1745303. Perrault and Jabbari were supported by the Harvard Center for Research on Computation and Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Declaration of Interest: There is no competing interest
Keywords: COVID-19, Agent based model, SEIR, policy simulation
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