Selected article for: "forecasting understanding and SIR 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_49
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint .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 here, we can easily leverage pre-existing R functions to incorporate a changepoint that modifies the probability of transmission to acknowledge an important public health intervention. It is also possible to customize the SIR model within R to define more advanced and different transition processes, and t.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint .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 here, we can easily leverage pre-existing R functions to incorporate a changepoint that modifies the probability of transmission to acknowledge an important public health intervention. It is also possible to customize the SIR model within R to define more advanced and different transition processes, and then parameterize and simulate those models. The SIR model is simple to understand and easier to fit, as opposed to other deterministic compartmental models (e.g., SEIR) or stochastic individual contact models [31] . However, these more advanced models will play an increasingly important role in forecasting and understanding the dynamics of this evolving pandemic.

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