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 two CSV files. One, JHU_DE_Mrz.csv, contains the data for March 2020, the second JHU_DE_Mrz-Apr.csv I continued to maintain.
Data from: "JHU_DE_Mrz.csv" Saturation limit: 56 Mio Dark figure: 1 f(t) = 9,088E9/(162,3 + 5,6E7*e^(-0,218*t ) Inflection point W(58,37/28 Mio) Maximum growth rate f'(xw) = 3,0584 Mio 31 Values Coeff.of determin. = 0,97570783 Correlation coeff. = 0,98777924 Standard deviation = 0,31876448
Dates from March 1st, 2020 to March 31st, 2020
Data from: "JHU_DE_Mrz-Apr.csv" Saturation limit: 56 Mio Dark figure: 1 f(t) = 4,559E10/(814,1 + 5,51E7*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
Dates from March 1st, 2020 to April 22nd, 2020
I assumed 56 million as the saturation limit. That is 70% of 80 million, the case of alleged herd immunity .
The comparison of the two results thus obtained shows how the dampening measures seems to flatten the logistic function curve and, above all, how the turning point
W (tw | f(tw)) of the curve shifts backwards and the maximum number of new infections per day f '(tw) gets smaller.
If all dampening measures had been abandoned on April 23, 2020, the new infections would have risen faster and faster according to this model and would have reached their maximum on the 88th day,