Based on reported cases we fit and functions to make predictions into the near future. More info and explanation

Germany
Exponential function: 2671688.24 exp (0.01 t) |
Quality of fit 99.41%
Logistic function: 1121321.46 / ( 1 + exp (0.04 (t -
31.0) ) ) | Quality of fit 95.97%
|
Cases |
Cases per 100k capita |
Exponential prediction |
Logistic prediction |
Apr-18 |
3,155,522 |
3,805 |
3,154,166 |
|
Apr-19 |
3,167,137 |
3,819 |
3,172,923 |
|
Apr-20 |
3,198,534 |
3,857 |
3,191,791 |
|
Apr-21 |
|
|
3,210,772 |
|
Apr-22 |
|
|
3,229,865 |
|
Apr-23 |
|
|
3,249,072 |
|
Apr-24 |
|
|
3,268,393 |
|
Apr-25 |
|
|
3,287,829 |
|
Explanation
The blue bar describes the reported cases. They are used to fit
two functions using statistical-mathematical methods so that the distance between the function and the data is
minimal. The red curve is an exponential function, uncontrolled growth
would follow this course. The purple curve is a logistic function (
S-shaped). Here, an initial part with exponential growth is followed by a decline, which is what was observed in
China.
Nobody knows what the outcome of future cases will be. Even if a turning point were to be reached, the
expected cases would continue to rise thereafter, only more slowly than with the exponential function. But every
estimate is inaccurate and the number of data points is limited. Therefore, the results can only be interpreted
using confidence intervals (box above). Only values outside of these shaded areas show a statistically significant
deviation. In general, any function can be fitted to the data. Whether a function matches and follows the data can
be assessed by a so-called quality of fit. In the following, a
linear and a
exponential function was fitted to
example data.
You can see with the naked eye that the exponential function follows the data better. In other words, the
exponential model is better suited to describe the data. This can also be expressed by the quality of fit (97
compared to 77 percent for the linear fit).