Vaccines & Death (part 9)
Further investigation into delayed death following vaccination
In part 8 of this series I discovered no less than 4 enigmas, the first being seeming evidence of instant vaccine harm followed by seeming evidence of vaccine benefit 1 week and 23 weeks after combined dosing in relation to all cause death in England. The second being seeming evidence of instant vaccine harm followed by seeming evidence of vaccine benefit 1 week and 23 weeks after combined dosing in relation to non-COVID death in England (surely a medical impossibility!). The third being lack of evidence of seeming vaccine harm and/or benefit for certified COVID death in England (jabs do not do what it says on the tin). The fourth being seeming evidence of instant vaccine harm followed by seeming evidence of vaccine benefit 1 week after combined dosing in relation to the 2015-2019 baseline series for all cause death England… I beg your pardon!!? If that doesn’t raise an eyebrow nothing will because COVID and the jab didn’t exist back then! Wergh is once again the word.
Get On Yer Bike!
Before we proceed any further I want us to think about the mechanics of cycling. Your back wheel turns because it possesses a gear set that is connected to the front gear set by a chain with holes that passes over lots of gear teeth. Traction relies on the integrity of the chain, the holes being holey and the gear teeth being spiky.
What would happen if you filed all the gear teeth flat? Exactly! You’d pedal like crazy and the front gear would slip under the chain, with the back wheel going nowhere fast.
Now think of the front gear as combined weekly vaccine doses and the back wheel gear set as all cause death or non-COVID death or certified COVID death. When things have teeth (spiky raw data) the rotation of the front gear over time (more and more doses) rotates the back gear of death data and we call this either correlation that doesn’t prove causation or correlation that proves causation depending on how strongly we feel about things.
Now imagine filing those toothy data gears down until they are smooth, so smooth in fact that they are T4253H smooth. Would we see any traction? Would the back wheel of death turn? Would we observe correlation in either guise?
No we wouldn’t!
…and here are three slides to prove it. Please compare them with the three equivalent slides presented in part 8:
All that I’ve done is take the very spiky raw data for all cause death, non-COVID death, and the 5-year (2015-2019) all cause baseline death and subject it to T4253H smoothing in order to flatten out several substantial spikes that originate from administrative delays. When these hiccoughs are removed all that seeming evidence of instant vaccine harm and delayed vaccine benefit disappear: my models have been zonked by admin artefact!
There is now no hint of statistically significant correlation (spinning wheels going nowhere) out to lags of 28 weeks (and yes, I checked out to 56 weeks - nothing out there either). No benefit and no harm but, of course, this doesn’t take account of background factors like disease prevalence a.k.a. CDR, so what I need to do is attempt to run those ARIMA models again but with T4253H smoothed death data and CDR as a covariate for starters. I’ll also need to account for mounting ill health and excess death due to closure of services in 2020 - more on this in a future article
In a nutshell we have a situation where we can use data to seemingly prove vaccine benefit when there is none. All depends on our personal integrity, honesty and thoroughness as analysts. Right now I could produce a rather slick and most convincing paper on vaccine benefit if I was paid to do so, or if such a paper sat better with my ego, career or carefully-cultured but institutionalised system of beliefs.
Biscuit Break
At this point I suggest we grab a stiff drink and sit down, for those lovely ARIMA models that I crafted for parts 1 to 6 of this series are troubled by artefact. Administrative hiccoughs in the raw death data are why the coefficients for vaccine dosing were negative (an indicator of vaccine benefit); this is also why a delay of precisely 23 weeks featured so strongly.
After wearing a hair shirt and eating stale bread as suitable penance I shall return with yet more models, though I’ll need to more thinking on the matter since we are wading in confounding factors up to our eyeballs. The one finding that stands is the inexplicable excess in deaths that is being reported around the world, but pinning this on vaccines using statistical methods is proving to be problematic.
Kettle On!
Thank you for your candour - this is really useful demonstration that statistically significant results can still occur as from artefacts in the data sets.
Given the vagaries of the data recordings, I wonder if it is really possible to use the high frequency parts of the signal at all?
If not, then aren't we back to comparing rates over discrete time periods between the vaxed and unvaxed. Then we have the problem of what denominators to use in each group, let alone trying to match the two groups to avoid potential biases.
Data recording vagaries (noise and time shift) should be roughly the same for both vaxed and unvaxed. So could we either use the ratio of the two (thus cancelling the noise) or perhaps look at CCF of deaths and jabs of the vaxed and unvaxed groups separately? If there is a vax effect, then the unvaxed results should serve as a reference.
I really appreciate that you put the English translation in every so often so I can kind of track what you are talking about.
I find the excess death data extremely curious and don't know what to make of it. I was hoping that this 9 part thread would be able to shed light on what might be causing the excess deaths. But alas, it has not. (through no fault of yours)
Your attempts to tackle the problem went down seemingly promising paths each of which then didn't work out because the data is "not clean" or "has hidden features that are just artifacts of how society functions". This is my summary of your nine super interesting posts. :-)
I have a question, which I think I know the answer to, but maybe not. You had a hypothesis you were testing: were there excess deaths five months after the jabs? Your conclusion seems to be that you currently have no data to support the hypothesis. Is the inverse true as well, that there is no data to suggest that the vaccines are mitigating the excess deaths? I am not talking about specific results that you kept on seeming to find showing the vaccines seemed to be working, but then the models blew up. Rather I am talking about the messiness of the data not allowing one to make either assertion: the vaccines are causing excess deaths nor the vaccines are mitigating excess deaths.
Does this make sense?