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

Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger

Fitting & errors -Using a second order polynomial fit to the data we obtained the coefficients c 0 , c 1 , c 2 as well as their confidence intervals. For this, the Matlab R function [P,S,M]=polyfit(t,log(I m ),2) on the natural logarithm of the monitored death rates ln I m (t) yields the coefficients P=[c 2 ,c 1 ,c 0 ] of the fit as well as information about the confidence intervals. We made use of the function polyparci that uses only core Matlab R functions and does not require the Statistics Toolbox. It uses the procedures outlined in the polyfit documentation to calculate the covariance matrix, and uses functions betainc and fzero to calculate the cumulative t-distribution and the inverse tdistribution for a given probability and degrees-of-freedom. Within the limited amount of time we had to prepare this document, we were unable to compare error estimates from different approaches.