Editor's note: LinkedIn has another post from Herman Steigstra remote. Then we'll just place it on Virusvaria, right? Let's share! On LinkedIn you can also 😉
"Gaat oversterfte gelijk op met vaccinaties?" Die vraag wordt mij met name door de critici van my article of January 20 The Commission is not entitled to a A lot of fog is raised by referring to prevalence or the clincher Correlation is not causation.
Of course, you can try to link suspicious mortality to vaccination status, but that is an almost impossible task. It requires an autopsy and that is certainly done and yes, confirmations of this relationship are certainly found there. It is known that remnants of the mRNA can be found in all organs after death. See, for example, the Professor Burkhardt's YouTube. But now I want to talk about our calculation model, which is able to predict the unexplained excess mortality.
Vaccination mortality modelled
Already in December 2022, we published an article titled Vaccination mortality modelled. In it, we described a model that is able to predict the excess mortality caused by them based on the number of vaccinations administered. Being able to describe observations afterwards could still be based on chance, but if that prediction concerns an event in the future, then it has a much greater value. It's almost the hard proof that the relationship really exists, otherwise the predictions would be worthless. This is the prognosis for excess mortality in the Netherlands according to our calculation model:
This is the current forecast based on the 2022 calculation model. Below the main graph, a green line shows the number of vaccinations administered for the vulnerable 80+ group according to the figures from the RIVM and ECDC. The dashed line is the prognosis for the excess mortality calculated from it.
The yellow line is the "unexplained excess mortality", which is the excess mortality after deduction of mortality from corona itself (red).
Of course, there are more waves to be observed. These are attributable to influenza (e.g. December 2022) and heat waves (e.g. July 2023). At the moment, there is flu that seems to be making many victims.
Just like in every model, there are "buttons" on it. Here are two of them. The short-term damage is set at 1:3000 deaths within a week after vaccination (for the first two vaccinations the risk seems less: 1:10,000 per shot). In addition, there is a long-term risk of 1:1650 per shot of dying between 4 and 18 months after vaccination. The latter has a somewhat greater uncertainty, because other long-term effects may also play a role. That is why we always focus on short-term damage, because it is most visible as a visible consequence.
Later analysis shows that the risk is significantly higher for the 80+ group. We arrive at a value of between 1:350 and 1:500 per vaccination. Because this age group is relatively small, the uncertainty is somewhat greater than that for all ages.
Foreign country
It only becomes interesting if the model with almost the same settings of the "buttons" would be able to predict excess mortality in other countries. And we tried that out too. Read our publication for more information Excess mortality across the border. There, too, we see the same pattern in most countries, with Australia as the "icing on the cake".
Unexplained excess mortality in Australia since vaccination was introduced. No corona, so no postponed care and no "something else with corona". It was only after the end of the zero-covid policy (September 2021) that corona started to strike there too (red), despite the vaccination. But the unexplained excess mortality persisted there, following the forecast quite precisely.
The calculation model is very simple and therefore unable to predict the course in detail. But it is precisely the simplicity of the model and the synchronization during the vaccinations (short term) that is a strong indication that the model will be close to the actual processes that take place in the body.
Conclusion
Being able to predict the future using a computational model with only the number of vaccinations as a parameter, assumes a strong causal relationship. It is much more than a correlation between two quantities. Prediction is of a higher order than just establishing a correlation. It is unjustifiable that politicians continue to ignore this relationship.
Quote: "Being able to describe observations after the fact could still be based on chance, but if that prediction concerns an event in the future, then it has a much greater value."
Hi, I don't see how this reversal, from ad hoc to prediction, leads 😬 to more certainty of a possible causal relationship Is a more in-depth explanation possible in the future? Thanks in advance!
Herman Steigstra is probably referring to the 'Granger causality test'. In this approach, a single causal relationship between time series is mainly seen in relation to mutual predictability. Used quite a lot, e.g. in economic research.
See also the Bradford Hill criteria.