Et Tu, John?

Ioannidis pushes circular 'safe and effective' vaccination assumptions in model to show vaccine saved millions of lives

Imagine that we want to test the claim that a special diet - let’s call it D - that consists of consuming 10,000 calories a day without any exercise will lead to weight loss of 50 pounds in 5 weeks. To make such a bold claim you would expect us to examine the evidence by comparing the recorded weight of people before and after being on the diet.

But suppose instead our ‘evidence’ is nothing other than following mathematical model:

Weight loss after n weeks in pounds = n x efficiency_D

where efficiency_D is the average weekly weight loss of diet D.

Suppose further that we assume:

efficiency_D = 10

Then, with these assumptions we compute

Weight loss after 5 weeks in pounds = 5 x 10 = 50

QED! We can now trust the claim that the special diet D does indeed lead to weight loss of 50 pounds in 5 weeks and we can believe this with full certainty (hand on heart).

Would you be convinced by this argument? Or would you be very uncomfortable and highly suspicious that there is some sleight of hand here? Bearing in mind the objective of the study is to test the claim don’t you find it odd that the main assumption in the model (namely efficiency_D = 10) is essentially the very claim we are endeavouring to test?!

Isn’t it all a bit circular and self-serving?

Well, it turns out that a study claiming to test the claim that the covid vaccine has saved millions of lives (just like a previous study from Imperial) is based not on any comparison of empirical mortality data between populations of vaccinated and unvaccinated, but rather on the above ‘scientific’ methodology.

This scientific study applied the same highly sophisticated logic and similar circular assumptions used to test the claims about the special diet, D, namely that:

• Without vaccination Covid infects and kills a lot of people.

• The vaccine is effective; specifically, people who do not get the vaccine are four times more likely to get covid than those who do.

• The vaccine is perfectly safe, i.e. nobody dies as a side effect from the vaccine.

Here is the paper. Take special note of the identity of the first named author:

The first named author of this paper is none other than one of the most highly respected scientists in the world, John Ioannidis of Stanford University, who used real-world data in 2020 to demonstrate that Covid was nothing like as deadly as was being claimed.

And here, in its entirety, is the mathematical model contained within the paper which was used to claim that 14.8 million life years were saved by the vaccine:

For each population age group, the number of lives saved L in that age group is:

L = N x PI x IFR x VE

Where:

• N: number of people in that age group

• PI: number of people in that age group who (absent a vaccine) would be infected with covid

• IFR: infection fatality rate in that age group (i.e. the proportion of people infected who die)

• VE: the vaccine efficacy in the age group (which is one minus the ratio of the percentage vaccinated who get covid and the percentage unvaccinated who get covid)

With the exception of N, each of the actual values assumed in the equation are fanciful, being based on a myriad of unproven the assumptions, such as that Covid ‘case’ numbers were accurate. Specifically:

• PI is assumed to be 20% (i.e. in the absence of the vaccine it is assumed 20% of the population are assumed to have got Covid).

• IFR in each age group is based on Ioannidis’s work which showed that the elderly were at much higher risk; but the values are still based on the (flawed) assumptions that those classified as being Covid cases did in fact have the disease and those that died did indeed die from Covid disease.

• The VE is based on the assumption that those classified as Covid cases did in fact have the disease. More importantly, the data used is based on studies that we have shown are systemically flawed. Likewise, the paper assumes (pre-Omicron) that VE = 75%, i.e. that an unvaccinated person is four times as likely to get covid than a vaccinated person. This is nonsense.

While a number of people have publicly criticised the paper, one point that nobody seems to have raised is that it does, unwittingly, demonstrate an extremely important point, namely:

Since no real-world data provide evidence that the vaccines saved lives then we know that the estimates of vaccine effective - such as the 75% assumed in the study - are, empirically, wrong.

Perhaps this was the real discrete conclusion that Ioannidis hoped would be inferred from the paper? If not, why would he wish to be associated with garbage work operating under the misnomer that it is genuine ‘research’ is inexplicable if not mysterious.

The front cover of the paper states that there was no funding or conflicts of interest associated with the paper, although it states that “The work of John Ioannidis is supported by an unrestricted gift from Sue and Bob O’Donnell to Stanford University.”

Although this Ioannidis paper has not yet been peer-reviewed it is already being used as ‘overwhelming evidence’ of how great the vaccines were. The 2022 Imperial paper, which claimed 20 million lives saved by the vaccine, was published in no less a journal than the Lancet despite being based on similar circular ludicrous assumptions. It has since been used in multiple reports and high-level forums as justifying the motivation to push for the continued roll-out of the vaccine.

As explained in this brief video we cannot simply ignore or laugh at such nonsense:

Update: Jessica Hockett has written an excellent article about Ionannidis’s questionable record of covid papers.

Update: The comment by Francesco Pansera on this article provides some very interesting background on the Italian co-authors of the Ioannidis article.