Bayesian Regression Modell zur Analyse, Vorhersage der Ausbreitung von COVID-19 in Deutschland mit hoher örtlicher Auflösung zur und Vorbereitung der Intervention und Regulation

• Antragsteller: Professor Dr. Gordon Pipa
• Fachliche Zuordnung: Statistische Physik, Nichtlineare Dynamik, Komplexe Systeme, Weiche und fluide Materie, Biologische Physik ; Epidemiologie und Medizinische Biometrie/Statistik ; Mathematik
• Förderung: Förderung von 2021 bis 2023
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 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-Verfahren: Sachbeihilfen