Corona und die Wissenschaft

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Im Zuge der Corona-Kriese wird mit unsauberen wissenschaftlichen Studien Politik gemacht und das Ansehen der Wissenschaft untergraben. Aktuelles Beispiel: eine Studie aus Göttingen, welche bereits in vielen Zeitungen zitiert wurde. Diese Studie wollte die Einschränkungsmaßnahmen gegen COVID-19 als richtig und wirksam „beweisen“, indem sie ein SIR-Modell an die Fallzahlen der Sars-CoV-2-Positiven in Deutschland angepasst hat.

Die Studie von Jonas Dehning et al. aus der Gruppe von Viola Priesemann hat einen gravierenden Mangel. Sie ignoriert, dass die zugrundeliegende Datenbasis der bestätigten COVID-19 Fälle durch die Ausweitung der Test-Kapazitäten stark verzerrt wird. Diese Kritik an den COVID-19 Zahlen ist so offensichtlich, dass sie schon vor Monaten in der Presse besprochen wurde. Die Autoren verwendeten diese Daten dennoch, ohne die Testausweitung in ihrem Modell zu berücksichtigen. Sie gehen darauf noch nicht einmal im Diskussionsteil der Arbeit ein. Eine derart eklatante Unterlassung durch die Autoren und die Gutachter macht mich fassungslos und untergräbt mein Vertrauen in eine der führenden wissenschaftlichen Zeitschriften – Science.

Hier mein Leserbrief zum Science-Artikel:

RE: the elephant in the room – number of tests

(29 May 2020)

Why have authors and peer reviewers ignored the elephant in the room – the influence of the number of tests?

In their article, Dehning et al. use numbers of official COVID-19 cases (people positively tested for Sars-CoV-2) to fit parameters of a SIR based epidemiological model. Based on these modeling results the authors make far reaching political conclusions, which have already been quoted in public news papers. Corresponding author Viola Priesemann claims in „Der Spiegel“, that only government mandated contact restrictions have brought down case numbers [Spiegel]. In German television she argued for prolonging government restrictions of public life [AnneWill].

Thus, this article is not only contributing to new ideas for epidemiological modeling, but also participating in the political discourse about civil liberties.

Conclusions drawn from statistical analysis and modeling critically hinge on the validity of the underlying data. The authors used the number of people who had been positively tested for Sars-CoV-2 as a measure for the number of infected people. Obviously, the number of positively tested people depends on the number of tests. More tests lead to more positive results.
The rise of the estimated R value – as calculated from the Nowcast by RKI –
in early March (03.03.2020: R=1.9; 10.03.2020: R=3.3) [RKI-17/2020] is an artefact that illustrates how an increase in testing capacity distorts the dynamics of COVID-19 case numbers.

This issue was part of the public discourse prior to submission of the article
and has been raised many times. I give two examples from public news papers.

1.) Olaf Gersemann wrote on March 27th 2020 that the test capacity and the criteria that determine who is tested distort the number of positive tests [Gersemann].

2.) Julia Merlot wrote on April 4th 2020: „A share of confirmed cases of infections can be attributed to the risen number of tests“ [Merlot].

The authors even quote Lourenco et al. 2020, who used data on reported SARS- CoV-2 associated deaths to calibrate their SIR model [Lourenco] – which might be less distorted than case numbers. Yet Dehning et al. do not offer any reason or discussion why they decided to fit their model to case numbers instead.

The authors note that their „framework can be easily adapted“ e.g. to consider „subsampling effects hiding undetected cases“. I expect their model would have yielded considerably different results had they taken into account the number of tests and selection bias. They have decided not to do this, but offer no rational for this decision.

Ignoring the influence of the number of tests and selection bias
demands a thorough justification in the discussion section. While the authors note that „In countries where major changes in test coverage are expected, this will have to be included as well.“ – they hereby imply that Germany is not among those countries where test coverage has changed significantly. This is clearly wrong. Even the RKI states on April 15th 2020 that test capacities have been increased considerably [RKI-17/2020].

It appears that the authors base their model on the ASSUMPTION that in early March the epidemic outbreak was still in the „initial phase“ where the number of susceptible people is still close enough to 100 percent and therefore the dynamics „is dominated by exponential growth.“. There is reason to doubt this assumption. Calculations by Dirk Ginzel (based on death numbers rather than case numbers) suggest that in early March the epidemic dynamics has already left „exponential growth“ territory [Ginzel]. I fear that the far reaching conclusions of this study are already inherent to this possibly wrong assumption.

Not even discussing this issue is a glaring omission by the team of authors and the reviewers, which is incomprehensible to me.

Sources

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