Modelling complete dynamics of SARS-CoV-2 pandemics of Germany and its federal states using multiple levels of data

Epidemiological modelling is a key method of pandemic management including that of SARS-CoV-2. New insights into epidemiologic mechanics and new data resources require continuous adaptions of modelling approaches. We here present a revised and considerably extended version of our previous SARS-CoV- 2 model implemented as input-output non-linear dynamical systems (IO-NLDS). We now include integration of age-dependent contact patterns, immune waning, and new data resources such as seropositivity studies, hospital dynamics, variant dynamics, non-pharmaceutical intervention measures and dynamics of the vaccination campaigns. With this modelling framework, we explain the dynamics of several data resources for the complete pandemics in Germany as well as its 16 federal states. The latter also allows us to investigate the heterogeneity of model parameters in Germany for the first time. To achieve this goal, we extend our estimation approach by constraining variation of parameters among the federal states. This allows reliable estimation of a few thousand parameters using hundreds of thousands of data points. Our approach can be generalized to other epidemic situations or even other areas of application, thus, supporting general pandemic preparedness.