Prof. Dr. Tim Conrad, Prof. Dr. Sebastian Pokutta, Prof. Dr. Christof Schütte, Dr. Gábor Braun, Dr. Natasa Djurdjevac Conrad, Christoph Spiegel
Zuse Institute Berlin, FU Berlin, TU Berlin
Our objective is to track and monitor infections for Berlin, Germany as well as model predictions using model-based and data-based approaches. In our model-based approach, we constructed an infection spread model based on the well-known SIR and SEIR models, but with time-dependent infection rates that allow to incorporate the different phases of spreading control implemented by the respective authorities (states, regions, cities). The model is fitted to the available data by means of classical parameter estimation as well as by Bayesian uncertainty quantification. Predictions based on the model are computed daily when new data become available. The uncertainty of the prediction reflects inaccuracy of reporting as well as sparseness of data. In our data-driven approach, we developed a simple model to monitor confirmed COVID-19 cases and we use the Facebook Prophet library.
mathematical model, parameter estimation, data-driven approach, infection dynamics, COVID-19 in Berlin