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is the (which was not peer-reviewed) The copyright holder for this preprint .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 Figures 7 and 8 show plots of the three fitted models' infection curves for NC and the CRI, respectively, projected out to the beginning of August. In both plots, we see the dramatic effect of the public health intervention; that is, the so-called "flattening of the curve". There are two important differences to note between NC and the CRI region. First, the CRI visibly shows relatively more flattening. This effect can be best observed in Table 3 in the Peak % Infected and Final % Infected columns. Moving from the Pre to Post to Int models within a location, the drop in percentage infected is more pronounced in the CRI. In fact, for the SIR-Int model, the percentages are virtually the same for both locations; that is, the CRI has "slowed down" to the state as a whole. Second, the date of peak prevalence was initially 8 days earlier for the CRI compared to NC. However, using the current SIR-Int model, although both locations showed their infection curves shifting forward in time, the date of peak prevalence is now 3 days later in the CRI (Table 3 ). To put this into context, for NC, the time duration from the start of the outbreak to the peak prevalence has gone from 49 days to 70 days (43% increase). However, for the CRI, the time duration from the start of the outbreak to the peak prevalence has gone from 32 days to 64 days (100% increase).