Zusammenfassung
During the spread of SARS-Cov-2, Germany imposed various restrictions, including the closure of schools on March 16 2020, and an extensive lockdown on March 23 2020. In this paper, we show how the influential simulation of the purported beneficial effects of this lockdown in Germany was based on wrong data, but nevertheless played a decisive role in shaping the future by allegedly producing ...
Zusammenfassung
During the spread of SARS-Cov-2, Germany imposed various restrictions, including the closure of schools on March 16 2020, and an extensive lockdown on March 23 2020. In this paper, we show how the influential simulation of the purported beneficial effects of this lockdown in Germany was based on wrong data, but nevertheless played a decisive role in shaping the future by allegedly producing evidence for the effectiveness of these measures, lending scientific credibility to policies. We point out that the evaluation of the success of such policies depends critically on data quality. Using publicly reported confirmed cases for the calculation of time series statistics is apt to produce misleading results because these data come with unknown variable time lags. Using data on incident cases, i.e., dates of the onset of symptoms, produces results that are much more reliable. Using this method demonstrates that previous analyses stating that the mitigation strategies of the German government were necessary and effective are indeed flawed. This in turn shows that model simulations and dissimulations are very close neighbors.
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