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

Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger

The above arguments explained our believe in sigmoidal models, but we also see mechanistic problems in other type of models, such as exponentials. Many exponential models rely on doubling times [13] , which require intense preprocessing of data, such as smoothing, and are model dependent. Please refer to the methods in the appendix for further discussion of doubling times in the context of the here presented GM. Other exponential models report considerable deviations from an exponential nature, e.g. a power law behavior as the curve flattens [16] . Sigmoidal functions automatically account for exponential growth and subsequent flattening and we thus believe them to be a better predictor. We must note that we aim to be compared with descriptive models, i.e. models that work only in the statistical limit of large data. In contrast, mechanistic models for infectious diseases [4] are able to study the effect of parameters such as policies, health system or culture on the outcome of a pandemic, and thus to provide more detailed predictions and recommendations.