Bayesian Regression Model to Analyze, Predict and Control the Spreading of COVID-19 in Germany with High Spatial Resolution

• Applicant: Professor Dr. Gordon Pipa
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics ; Epidemiology and Medical Biometry/Statistics ; Mathematics
• Term: from 2021 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 492350939

Aim of the proposal is to provide a fully data-driven analysis and forecast of spatial temporal dynamics, with an improved spatial resolution that will enable decision-makers to judge the current and predicted dynamics, to assess the reliability and possible variations of the predictions, to plan and adjust regulation to control the outbreak. The model will include explicit factors that model the spreading across regions that will allow visualizing the temporal spreading and the identification of driving factors of the disease. To this end, a fully datadriven Bayesian regression model will be used on two spatial scales. Firstly, on the level of counties (Landkreise), and secondly on the level of regions (~40) inside the county of Osnabrück. In the second phase of the project, Oldenburg will be included as a second high resolution spatial model. The models will be an extension of the established fully Bayesian regression model, initially developed in cooperation with the RKI, and later adapted to COVID-19 (https://covid19-bayesian.fz-juelich.de/), and that runs prediction on the level of counties since August 2020, and operates as a prototype on the finer level since January 2021.

• DFG Programme: Research Grants