# Required team_name: Priesemann Group, MPI-DS model_name: Bayesian SIR model_abbr: DSMPG-bayes model_contributors: Sebastian B. Mohr (Max Planck Institute for Dynamics and Self-Organization), Jonas Dehning (Max Planck Institute for Dynamics and Self-Organization), Viola Priesemann (Max Planck Institute for Dynamics and Self-Organization) website_url: https://github.com/Priesemann-Group/covid19_inference_forecast license: lgpl-3.0 team_model_designation: primary methods: Bayesian inference of SIR-dynamics # Optional institution_affil: Max Planck Institute for Dynamics and Self-Organization repo_url: https://github.com/Priesemann-Group/covid19-forecast-hub-europe twitter_handles: ViolaPriesemann, JonasDehning 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."