Deep learning-based parameter estimation of high spatial resolution mathematical models for the spread and control of COVID-19 in Germany

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The COVID-19 pandemic has made clear which complex patterns the spatio-temporal dynamics of an infectious disease outbreak can follow. Operational decisions for intervention measures to control and mitigate an outbreak require a rapid risk assessment of the disease spread. The proposed project will provide and use computational data-based evaluation and prediction tools of infectious disease spread on multiple spatial scales. Mathematical techniques developed and applied by our research groups during the first year of the COVID-19 pandemic constitute a solid methodological basis for the proposed scientific project. Importantly, the methods do not restrict to COVID-19 but can be adapted to describe the spread of different diseases. All data sources that will be used are secured for the project time and have been already used by the team in the past.   Ranging from small clusters of infection to diffuse evolution, controlling an infectious wave calls for intervention measures on a local (e.g. a specific district of a county or a city) as well as on a larger (e.g. regional or national) scale. Mathematical models for the spread of infectious diseases on high spatial resolution, enriched with reported surveillance and mobility data, will be used in this project to describe an ongoing outbreak and to enable short term forecasts. The project is designed to describe the temporal disease spread on several spatial scales with a large improvement in spatial resolution. The first spatial scale is provided by well-established mathematical models (Barbarossa group, that use data on the level of Federal States of Germany (weekly forecast, see ). The next finer level will use the same disease dynamics models on the level of counties (Landkreise). The finest level with the highest spatial resolution will be on the level of districts of the counties of Osnabrück (and Oldenburg in the later second phase of the project). To this end the challenge is threefold. Firstly, the mathematical (mechanistic) models must be adapted to account for socio-economic and demographic features over multiple spatial scales and interacting geographic areas. Secondly, the adaptation of such complex models requires data efficient model fitting and the ability to deal with incomplete knowledge. Thirdly, we need to assess the performance against a fully data driven approach. To tackle these challenges, we will use machine learning tools to estimate model parameters and combine mechanistic models predictions (Barbarossa group) with fully data-driven Bayesian regression by the Pipa group.