  The effective reproduction number is a key figure to monitor the course of
the COVID-19 pandemic. In this study we consider a retrospective modelling
approach for estimating the effective reproduction number based on death counts
during the first year of the pandemic in Germany. The proposed Bayesian
hierarchical model incorporates splines to estimate reproduction numbers
flexibly over time while adjusting for varying effective infection fatality
rates. The approach also provides estimates of dark figures regarding
undetected infections over time. Results for Germany illustrate that estimated
reproduction numbers based on death counts are often similar to classical
estimates based on confirmed cases. However, considering death counts proves to
be more robust against shifts in testing policies: during the second wave of
infections, classical estimation of the reproduction number suggests a
flattening/ decreasing trend of infections following the "lockdown light" in
November 2020, while our results indicate that true numbers of infections
continued to rise until the "second lockdown" in December 2020. This
observation is associated with more stringent testing criteria introduced
concurrently with the "lockdown light", which is reflected in subsequently
increasing dark figures of infections estimated by our model. These findings
illustrate that the retrospective viewpoint can provide additional insights
regarding the course of the pandemic. In light of progressive vaccinations,
shifting the focus from modelling confirmed cases to reported deaths with the
possibility to incorporate effective infection fatality rates might be of
increasing relevance for the future surveillance of the pandemic.
