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

The observed cumulative case and death counts were obtained daily at noon starting March 2, 2020 from the North Carolina Department of Health and Human Services website for all 100 counties [12] . Data collection for this manuscript ended on April 7. In order to accurately estimate the actual latent prevalence at time t, the cumulative case counts were adjusted for imperfect detection by dividing them by 0.14. While estimates of detection probability for coronavirus, also known as the ascertainment rate, vary in the literature, ours is in line with those reported [18, 19, 20, 21, 22] . Next, cumulative deaths were subtracted from adjusted cumulative cases. We also adjusted cumulative cases for recoveries by removing cases after 20 days, the estimated median duration of viral shedding from illness onset [23] . Daily incremental incidence was obtained by subtracting the estimated latent prevalence at time t − 1 from that at time t. Crucially, in our research, we model estimated latent prevalence as constructed here, not observed prevalence. For the sake of brevity moving forward, we use the terms 'latent prevalence' and 'prevalence' interchangeably.