Selected article for: "infection recovery rate and recovery rate"
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_19
    Snippet: 3/1 3/3 3/5 3/7 3/9 3/11 3/13 Date distributions for all model parameters: estimated spreading rate λ, recovery rate µ, reporting delay D between infection date and reporting date, number of cases I0 at the start of the simulation, scale-factor σ of the width of the likelihood distribution, and the effective growth rate λ * = λ − µ. J: Log-likelihood distribution for different combinations of λ and µ. A linear combination of λ and µ y.....
    Document: 3/1 3/3 3/5 3/7 3/9 3/11 3/13 Date distributions for all model parameters: estimated spreading rate λ, recovery rate µ, reporting delay D between infection date and reporting date, number of cases I0 at the start of the simulation, scale-factor σ of the width of the likelihood distribution, and the effective growth rate λ * = λ − µ. J: Log-likelihood distribution for different combinations of λ and µ. A linear combination of λ and µ yield the same maximal likelihood (black line). White dot: Inference did not converge.

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