Disclaimer: These are estimated figures and measures, rules of thumb and general dynamical properties of COVID-19. No guarantee, no liability from my side.

Scientific Publications

Anbei finden Sie eine Reihe Artikel, Radiointerviews und TV Auftritte, zu denen ich beigetragen habe. Meine wissenschaftlichen Publikationen finden Sie auf googe scholar, einige auch hier.

Deutschlandfunk: “Je früher wir handeln, desto kürzer ist der Lockdown” – ein ausführliches Gespräch mit Ralf Krauter. Zu unserer Studie zur Kontaktnachverfolung eine gute Zusammenfassung von Sophie Stiegler: “Welche Lockerungen können wir uns erlauben?”

Alle Artikel mit mir in “DIE ZEIT” können hier abgerunfen werden.
Aktuell interessant: “Je früher der Lockdown, desto kürzer” zur derzeitigen zweiten Welle.

Artikel mit meinen Beiträgen in Spiegel können hier abgerufen werden. Aktuell interessant ist insbesondere der Artikel “Letzte Chance vor dem Lockdown”.

Artikel in der Süddeutschen Zeitung können hier abgerufen werden.
Aktuell interessant: “Die Wucht der kleinen Zahl”.

Ausführliches Interview in der taz: “Eine Vollbremsung machen”. Bisher ist die Vollbremsung noch nicht gelungen, aber noch haben wir Zeit.

Pressemitteilungen der Max Planck Gesellschaft (MPG) zu unserer Arbeit sowie die Stellungnahmen der ausseruniversitären Forschungseinrichtungen finden Sie auf der Website der MPG.

Talk Show Teilnahme und Fernsehauftritte z.B. bei Anne Will im Mai, November und Dezember 2020.


2020//04/28 We developed a strategy to overcome the corona crisis:

With three great colleagues,  we collected and evaluated all current options to overcome the corona crisis: Conclusion: We have a unique opportunity now to lower new cases even further, and get the spread under control. Our work was supported by many colleagues, and finally endorsed by all four presidents of the different German research organizations. Here you find a link to the German statement:
Stellungnahme: “Adaptive Strategien zur Eindämmung der COVID-19 Epidemie” 

2020//04/04 (updated)
Our SIR model with Bayesian inference of spreading rates
is now on arxiv – and accepted at Science

Our model enables us to infer the magnitude of  the change points in the spreading rate, related to the governmental interventions around March 8, 15 and 22 (closing large events; closing schools/child care/small gatherings, and  contact ban, respectively). While we make inference and forecasts for COVID-19 spread in Germany, our model and the code on github can easily be adapted to any other country or region.

A model to forecast
the case numbers of the COVID-19 spread.

We developed a SIR model to forecast the case numbers of COID-19 for the next few weeks. We implemented Bayesian parameter estimation using MCMC importance sampling. We make use of the model to explore potential future scenarios, assuming different degrees o future social distancing. You can find the results on the university’s webpage, and the method and Python code on our github repository. A arxiv paper draft is in preparation.

A simple rule of thumb
to predict tomorrow’s expected number of cases:

- Tomorrow, we have about 25 % more cases than today [1,2]
- Within 3-4 days the case numbers double [1,2]
- In 10 days, we have about 10 times more cases than today [1-4]
- This holds in any category: total cases, new confirmed cases, hospitalization, intense care, deaths [1]
- We will only see the effect of strict isolation in 1-2 weeks

The equation is F = A*(1+r)^N
F: number of cases in the future; A: number of cases today; r: growth rate per day (about 0.25), and N: number of days in the future
[1] As long as we are in the acute, exponentially growing phase!
[2] 25 % has been a typical growth rate in the past weeks in Germany,
Italy, France... This number is on the lower end.
[3] The longer one predicts into the future, the stronger the number
obviously depend on the assumed growth rate.
[4] Assuming that behavior has not changed

What can each person do to reduce growth?
A few simple measures.

You find many advises online (e.g. [1]). I want to add a few.
To understand the measures: The key aspect is to reduce the number of people you infect in case you should catch the disease without knowing. This is the key parameter that “flattens the curve”. Thus in brief: Meet as few different people as possible. And keep a distance.

  • Go for groceries only once a week, not every day or two. Thereby you make contacts to these people only once a week, and not every day.  This obviously reduces the number of people you meet and might infect, and to get infected yourself.
  • Supermarkets will stay open and will continue to serve you [2]. Thus no worries that you will run out of food.
  • Keep a list of contacts you had. Write that up every day. In case you might get tested positive, you can then inform the contacts you had in the past 1-2 weeks about your test result. Thereby they know whether to quarantine themselves.
  • Older people, and people at risk:  Please self-isolate as rigorously as possible: If the people at risk self-isolate, the case number of people needing hospital care at the same time.


A few links I found interesting. As usual, no guarantee for the content of linked pages: