Bayesian Regression Model to Analyze, Predict and Control the Spreading of COVID-19 in Germany with High Spatial Resolution
Epidemiology and Medical Biometry/Statistics
Mathematics
Final Report Abstract
The Core Goal of the project was to provide a fully data-driven analysis and forecast of spatialtemporal 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, plan and adjust regulation to control the outbreak. We achieved this goal during the project time and did not have to adjust any subgoal. We demonstrated an improvement by including different scales and identified key constrains for future applications. The results and code are available to the public, and we are in the final phase of preparing the publication. This process was delayed due to the maternity leave of Laura Krieger the key scientist implementing the project. There were no surprises but several mostly expected challenges. Among these was the organization of timely and efficient interactions with the agencies of the Landkreis. This was a challenge because time was always short in times all resources were needed to manage the pandemic. Secondly, it remained a challenge to cross educate both sides, such that the administration and the research understood each other. Thirdly, data exchange on a regular basis is always a challenge. Here it was not any different. The changes in the pandemic situation required adaptations of priorities and therefore sometimes also changes in the way data way measured and made accessible. This required adaptation from both sides. In summary all these challenges were managed and the project successfully implemented.
