Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale

Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams

In response to the need for actionable data insights in our community and health system, investigators from the Atrium Health Center for Outcomes Research and Evaluation (CORE) developed a series of COVID-19 forecasting models, which were used to guide Atrium Health's initial proactive response to ensure sufficient capacity to treat the expected surge in patient care demands. In this study, we present an initial Susceptible-Infected-Removed (SIR) epidemic model and its evolution to the Susceptible-Infected-Removed-Social Distancing-Detection Rate (SIR-Int) model. Here we describe and compare these models, the spatial differences in a pandemic, the significance of observed cases versus actual prevalence in the setting of rapidly evolving testing strategies, the current epidemiological trends and the potential effects of non-pharmaceutical interventions applied locally (e.g., social distancing). We also offer recommendations for how community and health system leaders may interpret our models to better prepare and act to decrease the negative consequences of COVID-19 spread within their own communities.