Selected article for: "dynamic behavior and epidemic model"
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_44
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint .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 the SIR-Post model. Eventually, both such models will provide a poor fit to the data ( Figure 5 , Figure 6 , Table 1 ). Because the behavior of any epidemic is dynamic, any model requires constant monitoring, assessment of fit to local data, and evaluation of efficacy as new data are collected. Our SIR-In.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint .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 the SIR-Post model. Eventually, both such models will provide a poor fit to the data ( Figure 5 , Figure 6 , Table 1 ). Because the behavior of any epidemic is dynamic, any model requires constant monitoring, assessment of fit to local data, and evaluation of efficacy as new data are collected. Our SIR-Int model provides an example where this attention to model fit and incorporation of regional influences allows for appropriate model adaption and careful calibration, thus generating the most accurate predictions available to guide regional decision making at the time.

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