Selected article for: "model parameter and SIR model"
Author: Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann
Title: Inferring COVID-19 spreading rates and potential change points for case number forecasts
  • Document date: 2020_4_6
    • ID: c8zfz8qt_50
    Snippet: As a basis for our forecast scenarios, we use the differential equations of the well-established SIR (Susceptible-Infected-Recovered) model. Case data comes from the COVID-19 data repository maintained by the Johns Hopkins University Center for Systems Science and Engineering [41] . While the model dynamics is well understood in general, here our main challenge is to estimate model parameters specifically for the COVID-19 outbreak. To that end, w.....
    Document: As a basis for our forecast scenarios, we use the differential equations of the well-established SIR (Susceptible-Infected-Recovered) model. Case data comes from the COVID-19 data repository maintained by the Johns Hopkins University Center for Systems Science and Engineering [41] . While the model dynamics is well understood in general, here our main challenge is to estimate model parameters specifically for the COVID-19 outbreak. To that end, we combined a Bayesian approach -to incorporate prior knowledge -with Markov Chain Monte Carlo (MCMC) sampling -to explore the parameters. Put simply, we first estimate the parameter distribution that best describes the observed situation, and then we use many samples from this parameter distribution to evolve the model equations and thus forecast future developments.

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