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

该提案的目的是提供完全由数据驱动的时空动态分析和预测，并提高空间分辨率，使决策者能够判断当前和预测的动态，评估预测的可靠性和可能的变化，规划和调整管制以控制疫情。该模型将包括对跨地区传播进行建模的明确因素，这将使人们能够可视化疾病的时间传播和确定疾病的驱动因素。为此，将在两个空间尺度上使用完全数据驱动的贝叶斯回归模型。首先是县一级(Landkreise)，然后是奥斯纳布吕克县内的地区一级(~40)。在该项目的第二阶段，奥尔登堡将作为第二个高分辨率空间模型包括在内。这些模型将是已建立的完全贝叶斯回归模型的扩展，该模型最初与RKI合作开发，后来适应新冠肺炎(https://covid19-bayesian.fz-juelich.de/)，)，该模型自2020年8月起在县一级运行预测，并自2021年1月起作为更精细级别的原型运行。

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.