# Occurrence miscalculated? Mathematics students accuse RKI of distorted Covid figures

“18 instead of 78 – why the incidence values ​​are calculated incorrectly”. This is the title of the YouTube video that mathematics student Patrick Schönherr uploaded a little over a week ago. In this, the 23-year-old from Berchtesgadener Land accuses the Robert Koch Institute (RKI) of making a gross error in the calculation of the 7-day incidence.

Note: The video has since been deleted. FOCUS Online explains which theory the mathematics student puts forward in it.

The incidence figure indicates the detected corona cases per 100,000 inhabitants. According to Schönherr, however, the RKI omits an important point: There is not the same amount of testing everywhere in Germany.

If, on the other hand, the incidence was compared to the tests, a completely different value would emerge. This would have serious effects, especially in counties that test significantly more than average.

“It is enough to have math knowledge from middle school to understand the problem,” Schönherr introduces his video.

He gives an example:

County A and County B each have 50,000 inhabitants and one percent infected each.

• County A: tests 5000 residents, finds around 50 infected. that occurrence is here 100.
• County B: tests 2000 residents, finds around 20 infected. that occurrence is here 40.

The countries are therefore in the same pandemic situation, but have different occurrences. “There are more tests. More positive cases are being discovered. The incidence is increasing,” the mathematics student concludes. “So this incidence value does not currently allow drawing any conclusions about the pandemic.”

Schönherr therefore suggests including the ratio between the tests and the total population in the incidence calculation, “so a kind of test-positive rate”.

For this, it would only be necessary to standardize the number of tests and orient them to the national average. Since November, the average would about 1.5 percent of the population tested per week. A new, normalized incidence figure could then be calculated based on the “test positive rate”.

## In Berchtesgadener Länd, the incidence would only be 18

As an example for his calculations, he uses the occurrence in Berchtesgadener Land. This was included at the time of publication in week 9 78.3this week 83 people tested positive.

In total was 6817 of the 106,000 inhabitants tested for Sars-CoV-2, which corresponds to 6.4 percent of the population. 83 tests were positive, that is one “Test positive rate” of 1.2 percent.

that previous incidence calculation is: 83 x (100,000 / 106,000) = 78.3

The calculation for the new instance works as follows:

1. Normalization: 1.5 percent of the population corresponds to 1590.new” case number with only 1590 tests would therefore at 19.08 (equivalent to 19 positive tests).
2. These positive tests are now used as usual to calculate incidence. 19.08 x (100,000 / 106,000) = 18.0
3. The incidence would therefore be 18 instead of 78.

## According to the mathematics student, the calculation method would have several advantages

For this great discrepancy there is, according to mathematics students two reasons:

1. Berchtesgadener Land tests significantly more than the rest of Germany, on average four times as much.
2. The rate of positive tests of 1.2 percent is well below the German average of 6.1 percent most recently.

According to Schönherr, this calculation method would have more advantages:

• Negative tests will also be included in the “test positive rate”.
• The national German average would hardly change.
• Higher test numbers would not increase the incidence.
• The counties were easier to compare with each other.

According to “Traunsteiner Tagblatt”, the mathematics student hopes that “the problem will be discussed more in politics and the media”. He therefore sent his calculations to politicians in the Bavarian state parliament. However, he does not want his invoices to be “abused”. He is not concerned with criticism of the corona measures, but solely with a correct calculation of the incidence values ​​that are comparable for all districts.

Schönherr’s video was shared by many and did not go unnoticed by top German researchers. For example, physicist Viola Priesemann of the Max Plank Institute commented on Twitter.

According to her, the video “raises an important topic”. Increased testing is “punished” in the short term, as more chains of infection are then discovered. In the long term, however, it will be worth it, the physicist points out. “Because it stops the chains.”

But Priesemann countered Schönherr’s theory: “The video sounds logical, but makes a wrong assumption. So the conclusion is wrong.”

In his video, Schönherr assumes that tests are done randomly. “That’s not the case,” says the physicist. “People are not tested randomly, but mostly because there is a suspicion.” These include symptoms, contacts or a positive rapid test.

## “Follow-up results are not relevant”

According to Priesemann, you have to go in between two causalities distinguish:

• Case A: More cases are found because more are tested.
• Case B: More tests are being done because there are more suspected cases.

“At the end of the day, both contributions play a role,” she explains. “However, in the video, it is assumed that only and only A applies.” All further calculations after this slide are based on this incorrect assumption. So the subsequent results are not correct.

“According to the calculation in the video, the occurrences in the district can be easily reduced,” Priesemann also warns. For each suspect test, do a test on people who are highly likely to be negative (or a random test). “The incidence is already (almost) halved.”

Priesemann then also proposes a solution: “It would be best if, like the UK, we had screening, that is, around 100,000 random samples that give an objective picture of the outbreak every week. – Then we wouldn’t have to discuss here.”