| Name: | Bayesian SIR |
|---|---|
| Abbreviation: | Priesemann-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 |
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