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

Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger

The model is so simple that it can be reproduced and applied without detailed knowledge of epidemiology, statistics or programming languages. There are many countries not yet drastically affected by Covid-19, which will likely change for many in the coming weeks, and the GM could for example be used to apply it to such countries as soon as sufficient data is available. Using the recipe presented here, interested readers are in the position to obtain estimates for the shape of the Gaussian curve for their country, state, community, and use this model to compute more quantities of interest, such as our sketch of how to estimate the maximum number of required respiratory machines and the date of this maximum demand. Knowing the time of maximum rush days of SSPs, the maximum number of SSPs and width could help the government and medical agencies in these countries to optimize the managing of the disease wave by appropriate drastic actions for limited time. Moreover, fortunately, as our study here demonstrates, the time of peak of the disease wave differs among countries. Knowledge of these peak times and their durations allows other countries to help those who undergo the peak of the wave at a significantly later time, with breathing apparati and trained medical personal for a brief predictable time.