Preventing the health system from collapse has been repeatedly stated as one of the main objectives for the German containment policy for SARS COV 2. The exact relation between infections recorded in the public surveillance system maintained by the German center for disease control (RKI) and data on hospital occupation published by the German association for intensive care an emergency medicine (DIVI) has not been analyzed to date. Using a stepwise approach as described in the paper a linear regression model based on recorded infections with known disease onset was found to be the most suitable predictor for the number of ICU patients with a positive test for SARS COV 2 one month later. The model showed an excellent model fit with nearly 90% explained variance and reliable prediction of the maximum when applied to data beyond the construction dataset. Still, the number of additional patients with a diagnosis of COVID 19 does not necessarily mean a reduction of ICU capacities in the same dimension. Based on a examination of interrelations between parameters published in the DIVI registry it is concluded that a temporary reorganization of hospital care for SARS COV 2 positive patients would probably help to mitigate the risks coming with increasing infection rates.

If and how the increasing numbers in confirmed COVID 19 cases are driven by the simultaneous expansion of test capacities and tests conducted is a question under continuous debate. Building on the hypothesis, that the impact of increased testing might not be constant over time but determined by the national test policy applied, the relationship between testing and number of cases was investigated in different phases of the pandemic with a focus on the time before and after a change in German legislation on May 22, 2020 that opened the door for more tests in asymptomatic persons for prevention and containment purposes. Based on linear regression models it is estimated that about 60% of the cases tested positive after May 22 can be attributed to increased testing, while eliminating the test effect does not substantially impact the numbers before that date. Likewise, the clinical presentation of cases registered after May 22 is significantly different, with a hospitalization rate of 8.12% (before 18.17%) and a case fatality rate of 0.63% (before 5.17%). It is concluded that expanding the number of tests as it was done in Germany did not lead to a better understanding of the epidemiology of COVID 19. It is recommended to separate test results from preventive testing and testing based on a clinically defined test strategy, and to only use the latter for surveillance and as a basis for political decisions.

There is a large reservoir of publicly available data that could be used to better understand the population risk caused by the novel corona virus SARS-COV 2. However, important questions subject to public debate have not been sufficiently addressed with empirical data so far. Based on data published by official sources covering the period from March 02 to May 31, the impact of general testing activity in Germany on the number of confirmed cases was investigated with a linear regression model. The model yielded an adjusted R square of .07, which was statistically significant but numerically too small to explain a substantial part of the variation observed. For the same period, the relationship between changes in public mobility and the number of confirmed cases was analyzed. A strong correlation (-.51) was found for mobility and confirmed cases on the same day, which decreased with an increasing time lag. The correlation was stronger (-.68) when the date of reporting was used as a basis for confirmed cases rather than the date of first symptoms. These findings suggest that public mobility decreased in response to infection numbers reported rather than mobility restrictions having an impact on case numbers. Two important sources of bias are discussed that should be considered for disease modelling based on public surveillance data. The strong initial increase of case numbers observed in some countries might be an artifact of the national testing policy. Furthermore, the numbers are subject to a strong negative selection bias which does not allow for valid conclusions on the population. There is a continued and growing demand for representative data to arrive at a more realistic picture of the true population-based hazard potential of this novel virus