Marvin Schulte

• Epidemiological models have gained much interest during the COVID-19 pandemic. As the pandemic is now driven by newly emerging variants of SARS-CoV-2, the question arises how to model multiple virus variants in a single model. In this thesis, we have extended an established model for COVID-19 forecasts to multiple virus variants. We analyzed the model mathematically and showed the global existence and uniqueness of the solution as well as important invariance properties for a meaningful model. The implementation into an existing framework which allows us to identify model parameters based on surveillance data is described briefly. When applying our model to actual transitions between SARS-CoV-2 variants, we found that forecasts would have been significantly improved by our model extension. In most cases, we were able to precisely predict peak dates and heights in case incidences of waves caused by newly emerging variants during early transition phases. More severe outcomes, like hospitalizations, are found to be harder to predict because of very limited observational data regarding these outcomes for newly emerging variants.