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The effective reproduction number is a key figure to monitor the course ofthe COVID-19 pandemic. In this study we consider a retrospective modellingapproach for estimating the effective reproduction number based on death countsduring the first year of the pandemic in Germany. The proposed Bayesianhierarchical model incorporates splines to estimate reproduction numbersflexibly over time while adjusting for varying effective infection fatalityrates. The approach also provides estimates of dark figures regardingundetected infections over time. Results for Germany illustrate that estimatedreproduction numbers based on death counts are often similar to classicalestimates based on confirmed cases. However, considering death counts proves tobe more robust against shifts in testing policies: during the second wave ofinfections, classical estimation of the reproduction number suggests aflattening/ decreasing trend of infections following the "lockdown light" inNovember 2020, while our results indicate that true numbers of infectionscontinued to rise until the "second lockdown" in December 2020. Thisobservation is associated with more stringent testing criteria introducedconcurrently with the "lockdown light", which is reflected in subsequentlyincreasing dark figures of infections estimated by our model. These findingsillustrate that the retrospective viewpoint can provide additional insightsregarding the course of the pandemic. In light of progressive vaccinations,shifting the focus from modelling confirmed cases to reported deaths with thepossibility to incorporate effective infection fatality rates might be ofincreasing relevance for the future surveillance of the pandemic.

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts