The World Economic Forum (WEF) and the “measures” (NPIs) During the Pandemic

Our new study is published

For some time now, I have been conducting expert interviews documenting how various experts from science, the media, politics and civil society assess the pandemic, what factors they see at work, what their views are. Every now and then the idea comes up, that the World Economic Forum (WEF), which Prof. Klaus Schwab set up many years ago, could play a role.

I thought for a while about whether and how this theory could be tested. In the end, I came up with the idea that one could use the number of Young Global Leaders (YGL) that the WEF has trained over the years as a parameter and relate it to the intensity of “non-pharmaceutical interventions” (NPI), popularly and hereafter referred to as “measures”. This is what we, my colleague Johannes Klement and I, then did, at two points in time: at the beginning of the corona crisis, i.e. in March 2020, and at the second peak in the winter of 2020/2021. The study is now published in the peer-reviewed online journal “Cureus” and can be freely downloaded [1]. (Cureus is an interesting journal, by the way; our immunology survey was already published there [2]. It is a journal based in California and started by physicians who proceed without “conflict of interest” and very openly. It is peer-reviewed, usually with 3, at least 2 reviews.)

Read more

Covid-19 Vaccinations Do More Harm Than Good

Now We Have It in Black And White

In July, Mörl, Günther and Rockenfeller published a high-profile paper in the peer-reviewed online journal Frontiers in Medicine [1]. They compared the number of adverse events in the five pivotal trials of the Covid-19 vaccine with the number of adverse events in the control groups, as well as the number of severe Covid-19 cases in both groups, and calculated a harm-benefit ratio. If this is less than 1, then the vaccines do more good than harm. If it is greater than 1, they do more harm than good. Only two studies had a harm-benefit ratio smaller than 1, but very close to 1 (0.9 and 0.6). The authors point out that it would probably be reasonable to expect a harm-benefit ratio much smaller than 0.1, that is, ten more severe courses among control cases than among vaccinated people.

Clearly, this is not the case. In the BioNTech study, the ratio is actually very large at 25. This means that 25 times more serious side effects are registered in the vaccination group than in the control group. In the Moderna study, the ratio of 1.1 is about the same, but also far from favourable. They do not interpret the Sputnik pivotal study because the ratio there is negative, which is hardly credible.

Read more

Why People in Germany Get Vaccinated

Our vaccination motivation study is now published in BMJ Open

On January 6th, 2022, I had referred to the preprint of our study “Why do people consent to receiving SARS-CoV2 vaccinations – A representative survey in Germany” and discussed it in detail there [1].

Now it has been published in British Medical Journal Open (BMJ Open) [2]. The content has not changed from the preprint, so I won’t go into detail about the study again now. Only one additional analysis was added at the request of a reviewer.

Read more

Without Vaccination 18 Million More Deaths Worldwide? – Really?

People like me are often accused of not taking note of the high-ranking published data on the danger of SARS-CoV2 and the effectiveness of vaccination. I do take note, and I want to take this opportunity to say a few words about it.

A recent publication in Lancet Infectious Diseases [1] estimates that Covid-19 vaccinations have prevented 18 million Covid-19 deaths. This is a steep claim given that 6.4 million deaths involving or caused by Covid-19 have been reported worldwide to date (, accessed Aug. 2nd, 22; see Fig. 1)

Figure 1 – Cumulative number of Covid-19 deaths,, accessed Aug. 2nd, 22  

Read more

Modelling and Model Building

…using the example of our study: “Identification of different factors associated with Covid-19 deaths in Europe during the first pandemic wave”

A large group of statistical techniques designed to explain past data and also to predict future data is statistical modelling. This means that for a given data set with very different variables, one finds a mathematical structure that represents this data set as well as possible, firstly in a purely formal way. This procedure can be used to examine the influence of different variables on an outcome variable. In the language of modelling, the variable that one wants to explain is the dependent variable or criterion or outcome variable, and the different variables that are supposed to contribute to the clarification of this one variable are several independent variables resp. predictors.

I use our recently published modelling study [1] as a concrete example. It was conceived by me, I calculated the first analyses, then my colleague Rainer J. Klement got involved, who as a physicist is much more nimble in dealing with such models than I am.

Read more