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