team_name: Priesemann Group, MPI-DS
model_name: Bayesian SIR
model_abbr: DSMPG-bayes
model_contributors:
  - name: Sebastian B. Mohr
    affiliation: Max Planck Institute for Dynamics and Self-Organization
    email: sebastian.mohr@ds.mpg.de
  - name: Jonas Dehning
    affiliation: Max Planck Institute for Dynamics and Self-Organization
    email: jonas.dehning@ds.mpg.de
    twitter: JonasDehning
  - name: Viola Priesemann
    affiliation: Max Planck Institute for Dynamics and Self-Organization
    email: viola.priesemann@ds.mpg.de
    twitter: ViolaPriesemann
website_url: https://github.com/Priesemann-Group/covid19_inference_forecast
license: lgpl-3.0
team_model_designation: primary
methods: Bayesian inference of SIR-dynamics
repo_url: https://github.com/Priesemann-Group/covid19-forecast-hub-europe
data_inputs: JHU CSSE (confirmed cases; reported fatalities)
citation: https://science.sciencemag.org/content/369/6500/eabb9789
methods_long: 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.