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_57
Snippet: The SIR-Int model has high predictive accuracy based on data collected from March 2 to April 7 for both NC and the CRI and is able to demonstrate clear, compelling evidence of the efficacy of a stay-at-home order. By modeling estimated latent prevalence as we have done here, instead of observed prevalence, a lag delay in projecting peak infection can be avoided, reducing the consequences to leaders who require an accurate timeline for planning pu.....
Document: The SIR-Int model has high predictive accuracy based on data collected from March 2 to April 7 for both NC and the CRI and is able to demonstrate clear, compelling evidence of the efficacy of a stay-at-home order. By modeling estimated latent prevalence as we have done here, instead of observed prevalence, a lag delay in projecting peak infection can be avoided, reducing the consequences to leaders who require an accurate timeline for planning purposes (e.g., surge planning of hospital beds, supplies, and personnel).
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