## Example 2: Corona pandemic

Of course, it makes sense to use the current data on the corona pandemic for logistic regression.
I took the data for Germany from the *Johns Hopkins University (JHU)* website,
which is updated daily, and saved it in CSV files.

Data from: "JHU_DE_Mrz-Apr.csv" Saturation limit: 56 Mio Dark figure: 1 4,559·10^{10}ƒ(x) = ————————————————— 814,1 + 5,51·10^{7}· e^(-0,112·t) Inflection point W(99,4/28 Mio) Maximum growth rate f'(xw) = 1,5688 Mio 60 Values Coeff.of determin. = 0,82574762 Correlation coeff. = 0,90870656 Standard deviation = 0,90673232

German data March-April 2020, S=56 Mio, Dark figur=1.

Data from: "JHU_DE_Mrz-Mai.csv" Saturation limit: 56 Mio Dark figur: 300 2,5497·10^{13}ƒ(x) = —————————————————— 4,553·10^{5}+ 5,5545·10^{7}· e^(-0,10581·t) Inflection point W(45,404/28 Mio) Maximum growth rate ƒ'(x_{w}) = 1,4813 Mio 92 Values Coeff.of determin. = 0,90140376 Correlation coeff. = 0,94942285 Standard deviation = 0,93956073

German data March-May 2020, S=56 Mio, Dark figur=300.

With large values, the scaling changes to the scientific number format. The following applies: 4.0E+07 = 4.0·107 = 40,000,000.

I assumed 56 million as the saturation limit. That is 70% of 80 million, the case of alleged *herd immunity*.