What influences COVID-19 infection rates: A statistical approach to identify promising factors applied to infection data from Germany

Moritz Mercker; Uwe Betzin; Dennis Wilken

In this work, we present a statistical framework which is not based on specific mechanistic infection models, but is constructed to analyse current infection numbers, where the latter may show an arbitrary complex behaviour, possibly varying among different sub-regions of the considered area. The framework is optimised to reveal factors that explain differences in recent infection rates among different sub-regions. Thus, in the light of high variability of possible local political, social, environmental or infrastructural factors that may affect the infection rates, the proposed tool provides an opportunity to extract the most important factors responsible for COVID-19 propagation. This is important not only to reduce the exponential rate of infection growth in different countries, but also to decide which sanctions can be eased if the maximum of the confirmed daily new cases has been surpassed, as it is currently the case in Germany and several other countries.