If vaccination were really a dominant factor in the unexplained (non-covid) excess mortality, it should also be possible to find some predictability of vaccine mortality based on insightful rules. There are already several analyses that have shown correlations between number of vaccinations and observed excess mortality. I've been trying to boost that with graphs since August 2021. Below are some of those charts updated to now, late November 2021.
A Bayesian analysis by age group was picked up in the study of Meester and Aukema, including the text of this article, also as a Dutch version.
What already caught the eye in the summer of 2021 was the excessive mortality, especially in the group 65-80. Later, a pattern became clear: after the excess mortality increase, the death plateau, after which the increase started again, again months after the start of the booster campaign. That pattern was very similar to the vaccination curve – but there were months in between. Meanwhile, the five-month (sometimes 4-6 months) term has been signaled in several places in the world. Steve Kirsch signaled a 5-month time interval between vaccination periods and abnormal mortality increase. He received support from Professor Norman Fenton. Kirsch looked at the data from the CDC (the RIVM of the US).
In a Substack, John Dee analyses whether this observation also applies to the UK. He does this with a recognized statistical model, ARIMA. And indeed, around twenty weeks after the vaccination campaign, he sees: an 'unexplained' wave of excess mortality.
If covid increases the risk of, for example, ischemic heart disease, then a 6-month time course between spike protein exposure and death would be consistent with the underlying pathology that could lead to such a death.The Hart Group
It is noteworthy that the 6-month follow-up to the original Pfizer trial for adults showed four deaths from cardiac arrest in the vaccinated group compared to just one in the placebo group.
Australia can serve as a control group for Europe because Australia did not have prior Covid as the reason for the increase in mortality and hospital pressure since spring 2021. The results of this control group indicate that the cause of this rise in the number of deaths, especially among young people, must be something common with Australia, Europe and the US. Hypothetically, a separate factor unrelated to Covid and occurring around the world remains a possibility. The data that would disprove the hypothesis that these deaths and hospitalizations are related to the introduction of the vaccine are strangely never shared.
(Some references are at the bottom of this post)
The Steigstra method
After contact with Herman Steigstra, I ignored the numerical part myself. With his statistical background he was able to show how things can be improved and we have been in close contact ever since.
What we have been doing now is not trying to find significance again in the possible correlations. Conversely: We wanted to recreate the observed course of the unexplained excess mortality as well as possible with a few formulas, so that they draw the excess mortality line with the input of the number of. You could then use this as a forecast model under unchanged circumstances, although they are almost never there.
Our suspicion was that we would not be able to explain mortality fluctuations based on other options mentioned, such as lockdowns, long-covid, anxiety campaigns.
Herman Steigstra set to work on the short and long-term effects of vaccination, which also takes into account the under-mortality after mortality peaks due to corona. We also saw an increasing baseline, but this did not require a separate rule: this arises spontaneously in the prognosis by stacking the long-term effects. The graph that ultimately resulted is this:
The dotted line follows the course of death remarkably well. Of course, causality has not been proven, but the vaccinations appear to be a pretty reliable predictor of excess mortality.
In the graph above, the dotted line is drawn on the basis of a few functions with the vaccinations per week and the corona excess mortality as input:
- a mortality rate of 1:4,000 in the first two weeks after base series
- a mortality rate of 1:3,000 in the first two weeks after boosters
- a long-term mortality rate of 1:5,500 (4 months or more)
- after corona excess mortality peaks, the expected under-mortality is divided over two months
What the calculation model does is partly divide the excess mortality of each week over the coming months. As a result, the peak is also slightly shifted to the future. The excess mortality ultimately shown is therefore not 100% in accordance with the CBS figures.
Why spring 2022 does not follow the forecast
Where the prognosis deviates is in the period Feb-May 2022. Remember that the baseline (100%, left axis) has been estimated higher there because of the traditional flu season. After all, it is never possible to say in advance in which weeks the flu wave will take place. There was hardly any flu in February/March but that wave came later this year. That valley and that peak can be a result of that.
It is reinforced by the flu-free winter months that coincided with the under-mortality due to the Delta peak. It looks like a whole pit in the graph, but we should have expected it even deeper. This is therefore partly filled with other mortality that is virtually independent of previous excess mortality. Then came the influenza wave, just as independent of the prognosis. So the prognosis doesn't go with that. That seems to us a plausible explanation for the large deviation in spring 2022.
We are not medical professionals, but from what we have learned in recent years, typical side effects can be attributed to different periods. It is goal-oriented and associative thinking, but you will still have to form hypotheses for research. Acute heart failure due to inflammation is a cause of death that can occur within weeks. Just like dizzy spells that result in a fatal fall or an accident. Ischemic diseases have a longer, constructive character. Immune problems, which can also give cancer more chance, have a slower course and will also present themselves over a much longer period of time. It will vary by age group and may also be seasonally dependent, in response to confrontations with viruses. It is speculation as long as there are no detailed deaths. A database of causes of death and death and prick dates could easily debunk these speculations. This should have led to targeted hypotheses and research much earlier. That has not happened to date.
With a few simple calculation rules, the unexplained excess mortality can therefore be reconstructed amazingly well on the basis of only the vaccinations, taking into account under-mortality effects because vaccination damage also continues in periods of under-mortality.
What is there to gain? Willingness to vaccinate!
The willingness to vaccinate has been greatly damaged. Due to the haggling and inconsistent communication, the willingness to vaccinate among many has turned into vaccination distrust. This damage can only be repaired by transparently proving that these corona injections were introduced and administered to the wrong target groups on incorrect grounds.
How governments and pharmaceutical companies are going to solve this for themselves, I don't know. It is becoming increasingly clear that they have worked themselves into the nests. It is unlikely that the 'safe and effective' story of these 'vaccines' can be kept in the air for long. If the official communication remains as positive about these vaccines as it is about the conventional vaccines, then they have also been dismissed.
Only through transparency can it become clear that vaccines, provided that they are medically and scientifically substantiated and tested with care for a long time, can indeed be safe and still necessary. Everything calls for a thorough re-evaluation of the usefulness and necessity of vaccinations in general, with transparent evidence (which is usually there) and the role of the pharmaceutical and medical industry in promoting and even necessitating medication.
See also the Twitter thread of Herman Steigstra:
The calculation method used is a simplification of reality. The calculation model can be improved by taking into account ages and time-dependent mortality and should therefore be seen as a first step!