Selected article for: "Markov chain and MCMC Markov chain Monte Carlo sampling"
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_16
    Snippet: Bayesian inference for central epidemiological parameters during the initial phase of the outbreak We perform Bayesian inference for the central epidemiological parameters of an SIR model using Markov-Chain Monte Carlo (MCMC) sampling (Fig. 1) . The central parameters are the spreading rate λ, a recovery rate µ, a reporting delay D, and the number of initially infected people I 0 . We chose informative priors based on available knowledge for λ.....
    Document: Bayesian inference for central epidemiological parameters during the initial phase of the outbreak We perform Bayesian inference for the central epidemiological parameters of an SIR model using Markov-Chain Monte Carlo (MCMC) sampling (Fig. 1) . The central parameters are the spreading rate λ, a recovery rate µ, a reporting delay D, and the number of initially infected people I 0 . We chose informative priors based on available knowledge for λ, µ, and D, and uninformative priors for the remaining parameters (Methods). We intentionally kept also the informative priors as broad as possible such that the data would constrain the parameters (Fig. 1) .

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