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

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.14.20063420 doi: medRxiv preprint Notably, the prevalence and cumulative death curves for both figures look exponential. While both incidence curves are increasing, the incidence curves become volatile after the stay-at-home order went into effect. Prior to March 26, doubling time was estimated to be 2.56 days in the CRI (95% CI: (2.11, 3.25)) and 2.94 days in NC (95% CI: (2.33, 4.00)). Once data after March 26 are included, the doubling times increased and were estimated to be 4.70 days in the CRI (95% CI: (3.77, 6.22)) and 4.01 days in NC (95% CI: (3.43, 4.83)). Tables 1 and 2 gives a synopsis of the model fits for each location and model type. The estimated R0 of 2.36 for the CRI prior to March 26 is more typical of the range of R0 values given in the literature for COVID-19, while the value of 1.79 for NC is substantially lower [29, 30] . After the intervention, the estimated R0 values for both locations drop to a similar value, although this result is affected by reduced model fit. A comparison of the efficacy of intervention, defined as 1 -the hazard ratio of infection, gives 0.25 for NC and 0.43 for the CRI.