Author: Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams
Title: Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale Document date: 2020_4_18
ID: j5o8it22_19
Snippet: whereand . are estimates taken from the model fit. Since the SIR model is fully parameterized by β and γ, we also obtain predictionsandover all time t. The percentage of infected at peak prevalence was computed by dividing , by the population size N, while the final percentage of infected was computed as the limit of 1 − -(∞)/N. To estimate doubling time and compute a 95% confidence interval, we modeled incidence growth by fitting a logline.....
Document: whereand . are estimates taken from the model fit. Since the SIR model is fully parameterized by β and γ, we also obtain predictionsandover all time t. The percentage of infected at peak prevalence was computed by dividing , by the population size N, while the final percentage of infected was computed as the limit of 1 − -(∞)/N. To estimate doubling time and compute a 95% confidence interval, we modeled incidence growth by fitting a loglinear model as a function of time t using the incidence package. Figure 3 shows time plots of prevalence, cumulative deaths, incidence, and daily deaths for NC from the start of the outbreak on March 2 up to and including April 7. The first death was recorded in NC on March 24.
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