Selected article for: "compartmental model and SIR compartmental 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_2
    Snippet: Rationale. As one of the key epidemiological parameters, we infer the spreading rate λ from confirmed COVID-19 case numbers at the example in Germany by combining Bayesian inference with an SIR (Susceptible-Infected-Recovered) model from compartmental epidemiology. Our analysis characterizes the temporal change of the spreading rate and, importantly, allows us to identify potential change points and to provide short-term forecast scenarios based.....
    Document: Rationale. As one of the key epidemiological parameters, we infer the spreading rate λ from confirmed COVID-19 case numbers at the example in Germany by combining Bayesian inference with an SIR (Susceptible-Infected-Recovered) model from compartmental epidemiology. Our analysis characterizes the temporal change of the spreading rate and, importantly, allows us to identify potential change points and to provide short-term forecast scenarios based on various degrees of social distancing. A detailed, educational description is provided in the accompanying paper, and the model, inference, and prediction are available on github. While we apply it to Germany, our approach can be readily adapted to any other country or region.

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