Model: Priesemann Group, MPI-DS - DSMPG-bayes

    Name: Bayesian SIR
    Abbreviation: DSMPG-bayes
    Owner: epiforecasts
    Team name: Priesemann Group, MPI-DS
    Description: Bayesian inference of SIR-dynamics
    Contributors: Sebastian B. Mohr (Max Planck Institute for Dynamics and Self-Organization)<sebastian.mohr@ds.mpg.de>, Jonas Dehning (Max Planck Institute for Dynamics and Self-Organization)<jonas.dehning@ds.mpg.de>, Viola Priesemann (Max Planck Institute for Dynamics and Self-Organization)<viola.priesemann@ds.mpg.de>
    License: GNU Lesser General Public License v3.0
    Notes: primary
    Citation: https://science.sciencemag.org/content/369/6500/eabb9789
    Methods: This model simulates SIR-dynamics with a log-normal convolutions of infections to obtain the delayed reported cases. Parameters of the model are sampled with Hamiltonian Monte-Carlo using the PyMC3 Python library. We assume that the infection rate can change every week, with a standard deviation that is also an optimized parameter. When new governmental restrictions are enacted or lifted, we include a small prior to the change of the infection rate.
    Home: https://github.com/Priesemann-Group/covid19_inference_forecast
    Auxiliary data: https://github.com/Priesemann-Group/covid19-forecast-hub-europe

    Forecasts (17)

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