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Bayesian Markov Chain Monte Carlo Forecast for COVID-19
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Forecast for COVID-19 using Bayesian Markov Chain Monte Carlo

This repository provides the code behind our forecast of the COVID-19 spread in Germany. It was used for the three possible future scenarios that we discuss on the Göttingen Campus Page.

The methods are described here.

A pdf version is included in the repository.

The code and figures are found here.

Authors

Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Joao Pinheiro Neto, Michael Wilczek, Viola Priesemann

MPI for Dynamics and Self-Organization, Göttingen

Acknowledgements. We thank Tim Friede, Michael Wibral and Vladimir Zykov for carefully and promptly reviewing our work. We thank the Priesemann group - Matthias Loidolt, Daniel Gonzalez Marx, Fabian Mikulasch, Lucas Rudelt & Andreas Schneider - for exciting discussions and for their valuable comments. We thank the colleagues of the Göttingen Campus, with whom we were discussing the project and the case forecast in the past weeks very intensively, for their valuable input: Heike Bickeböller, Eberhard Bodenschatz, Wolfgang Brück, Alexander Ecker, Theo Geisel, Ramin Golestanian, Helmut Grubüller, Reinhard Jahn, Norbert Lossau & Simone Scheithauer.

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