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.