Covid-19 predictions using a Gauss model, based on data from April 2

Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger

Nowadays, numerous models to predict the spreading of infectious diseases like Covid-19 are available, for example the actively discussed susceptible-infected-removed (SIR) model [9] [10] [11] . Many of these models are either toy models that cannot make reliable predictions or they are so complex, by taking into account a wide range of factors, that simple predictions are not possible. In times of the coronavirus epidemic, predictions such as the maximum number of fatalities per day or the date of the peak number of newly seriously sick persons per day (SSPs) are valuable data for governments around the world, especially those facing the beginning of an exponential increase of casualties, and we hope to serve the people in charge with the here presented approach. In particular, fast predictions on the course of the coronavirus disease are crucial for policy makers to optimize their managing of the disease wave. To feed into the current debate on infectious disease models, we would like to propose a Gauss model (GM) as a simple, but effective description of fatalities caused by Covid-19 over time, similar to recent studies for the US [12] and for Germany [13] . In contrast to this previous work, we choose to use the logarithm of the reported daily death rates [2] , instead of cumulative infections, as monitored input data and we also do not rely on doubling times.