Introduction
“Dance with the tiger” - that’s how leading health politicians, epidemiologists and virologists described the situation when the first, cautious steps were taken on April 20, 2020 to revive social and commercial life after the lockdown introduced in Germany one month before. Meanwhile more than nine months have passed after the first COVID 19 cases were detected in China and many countries have at least started to alleviate their restrictions. There can be no reasonable doubt about the novelty of SARS-COV-2 and its’ potential to cause severe viral pneumonia with unusual features, a high fatality rate and a substantial risk for severe residues. However, this is not so much different from other respiratory viruses and classical risk analysis is based on severity AND likelihood of a potential hazard. For the latter, several simulation studies have been published , trying to justify the measures taken by comparing actual numbers against hypothetical scenarios and coming up with impressive effect estimates. These models share two important disadvantages: they are no valid replacement for empirical data, and they depend on presumptions, estimates and model parameters, that are mostly derived from the early phase of monitoring infection rates. As far as I know, those have never been re-assessed or re-validated in the light of increasing knowledge on aspects like unreported cases, infection pathways, and prevalence estimates based on antibody testing. It is my firm believe that it is time to leave those models behind and return to empiric, evidence-based science. This is my personal attempt to make a start by looking at the publicly available data from daily infection monitoring by the German institute for disease control, the Robert Koch Institut (RKI), from a slightly different angle. My best hope is to inspire some broader discussion and research on these topics, which I think is urgently needed to make the right decisions on future strategy.