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

Turk, P. J.; Chou, S.-H.; Kowalkowski, M. A.; Palmer, P. P.; Priem, J. S.; Spencer, M. D.; Taylor, Y. J.; McWilliams, A. D.

Background: Emergence of COVID-19 caught the world off-guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their healthcare systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policymakers to make informed decisions during a rapidly evolving pandemic. Objective: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte metropolitan region and to incorporate the effect of a public health intervention to reduce disease spread, while accounting for unique regional features and imperfect detection. Methods: Three SIR models were fit to prevalence data from the state and the greater Charlotte region and then rigorously compared. One of these models (SIR-Int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. Results: Presently, the COVID-19 outbreak is rapidly decelerating in NC and the Charlotte region. Infection curves are flattening at both the state and regional level. Relatively speaking, the greater Charlotte region has responded more favorably to the stay-at-home intervention than NC as a whole. While an initial basic SIR model served the purpose of informing decision making in the early days of the pandemic, its forecast increasingly did not fit the data over time. However, as the pandemic and local conditions evolved, the SIR-Int model provided a good fit to the data. Conclusions: Using local data and continuous attention to model adaptation, our findings have enabled policymakers, public health officials and health systems to do capacity planning and evaluate the impact of a public health intervention. Our SIR-Int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated the efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.