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Jan 23, 2023Liked by Martin Neil, Norman Fenton

This isn't exactly a new argument. Prof John Robison of Edinburgh in the early 1700s argued that an equation's solution set that approximated a data set could be both useful and incorrect. Prof Laplace of Paris insisted contrarily that the equation was always perfect and the measurements were flawed. Euler took the iterative solutions route between the two: For series calculations that converged, we could attain adequate accuracy with enough calculations. Digital computing has made Euler's approach practical for validating models, but three centuries later, those who insist on unknowable levels of certainty, use their imagined certainty to ignore all contradictory facts and impose their beliefs upon the world, by force.

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Very nice example. Thanks.

I don't think we are arguing that we are saying anything new, its more that some important lessons have been forgotten or side-lined. Nowadays a lot of university education is about obtaining that all important degree and not about thinking critically. Luckily this is not universally true of course, but approximately so.

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Jan 23, 2023Liked by Martin Neil, Norman Fenton

Many thanks, Martin , for your important work. The Scientific Method has always been about repeatability and proof. It's the proof that changes minds and raises new questions. We cannot maintain scientific rigor if we begin with beliefs, then ignore all contrary evidence. If we are graduating students who do not learn how a thing was proven to someone's satisfaction, those graduates are not scientists but cantors who chant a dogma repeatedly, in hopes the repetition will make a false notion true. Such graduates would have been well-suited for what Cambridge philologist Clive S Lewis called the "perfect anthill societies" of Soviet Russia, Fascist Italy and Nazi Germany...the trouble being, according to Lewis, that we humans are not ants and fit badly into anthills. The lack of critical thinking skills eventually wrecked all three totalitarian societies as they lost the capacity to innovate. So I heartily concur in your premise: Today's universities must promote critical thinking. It's the questions that motivate the quest for answers.

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Amazing article. Truly extraordinary. This is what real science and modelling looks like.

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Excellent analysis thank you. I am reminded of an economics lesson during my studies when we were discussing the alignment of Stone Henge. Our professor suggested we go out into the city and log the location of as many Zebra Crossings we could in the time allowed and analyse the data for astronomical alignment. Voila, there appeared to be a correlation of these crossings with the stars!! Must be some kind of weird hidden hand at work - like markets?

I believe that complex adaptive systems are not possible to model. Even with their super-computers, the Met Office rarely get a few days right let alone the intricacies of climate change. Confirmation Bias reigns supreme.

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I'm a simple guy. If you've developed a 95% effective vaccine that's a safe preventative for the 3rd leading cause of death in your society, then total deaths CANNOT increase. Any attempt to attribute that increase in death to something else is farcical because, sure, it's possible, but it's blatantly unreasonable. I'm an attorney, and I remind people that we don't execute criminals based on proof; we execute them based on proof BEYOND a reasonable doubt. When you start vaccinating & total deaths increase, you have no reasonable doubt - the vaccines must end. Over-intellectualizing, over-thinking are extremely dangerous trends, especially for the intelligent. It's very easy to persuade yourself that there's some universe in which these vaccines work, or there are 73 genders, or that socialism just hasn't been tried, but none of these beliefs are reasonable - they're all nuts.

It's really very simple: if you "cure" the third leading cause of death, then total deaths MUST decline. Anyone claiming there's an exception to that rule must present extraordinary evidence, not speculation. Indeed, the exception would be obvious (like CA fell into the ocean & drowned millions). It's not some BS about masks make people sad, which makes them sick...such subtle things are not plausible in light of the obvious: you've "cured" the 3rd leading cause of death but deaths increased.

Please stop taking the world "reasonable" out of your analysis/discussions. It's great that you have theories about how the vaccines might not be to blame, but are those theories reasonable? That's the threshold question we must demand our adversaries answer. (You reason from what you know to what you don't, not from what you don't know. We know deaths are increasing despite the cure; what do we infer from that? Granted, we don't know why deaths are increasing, but so what? Uncertainty drives INACTION, not action. That is, you don't vaccinate until you know it's unsafe; you STOP vaccinating until you KNOW it's safe.

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Excellent comment.

I'm not advocating that the model presented here be used as the basis for a prosecutor's legal argument! Simply saying once you commit to going down the stats. modelling route these are the implications of that commitment. Of course, an alternative course of action is not to commit to that argument if you think it overcomplicates and is unpersuasive compared to a simpler one. "Look deaths went up and they were supposed to go down".

However, some audiences require a different level of proof and hence a different argument. They want to know it wasn't lockdown and/or wasn't masks etc or it was ivermectin and it wasn't withdrawal of antibiotics.

Finally note the model is not just about the vaccines. This is about the whole shebang. Iatrogenic deaths. Dodgy testing etc. There's a puzzle here - "how did they pull it off?"

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Great thoughts, all. I'm inclined to think that asking the questions you ask overcomplicates the matter & leads to all sorts of problems. To illustrate my point by analogy, OJ prosecutors went awry w/ all that evidence about domestic violence; they should have focused on the fact that whoever killed Nicole did so at the one moment in time for which OJ had no alibi, on a night he just happened to be injured (like the real killer) in a way that he could not explain & that the real killer - having somehow lucked into this window of opportunity - had the good fortune to persuade OJ to tell the cops where the blood evidence would be found (or planted by them) & to thank the cops for setting him up (cause didn't he know that he was innocent? So why was he thanking the cops?) Long and short, they should have presented the physical evidence, presented the fact that he couldn't account for whereabouts & his statements to cops before resting - never would have mentioned domestic stuff at all. Same idea here: raising other factors simply distracts from the fact that vaccines went up and deaths did too. Like that's enough. At that point, you should be ready to convict. Laser focus on this issue is the best, I think, but happy to be persuaded.

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This isn't just about the vaccines. I don't say this to belittle the seriousness of the issue we face with vaccines but we also have the deaths from lockdowns and inappropriate treatment and deliberate negligence in 2021 and 2020.

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Yup - they've been a murderin' & a murderin'. At this point, hard to know which of their murderous schemes killed each of their victims.

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Yes, taking the big public health view, deaths should go down. That they haven't should indicate a major fail for all covid policies.

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What? Why that would imply that drug makers shouldn't be allowed to sell their products unless & until they've proven safety & efficacy - which would be a complete reversal of our normal policy that allows people to sell crap until the government proves it's unsafe. (Forgive my sarcasm, but I can't imagine why people struggle to recognize how you're clearly & obviously right. I mean if we think what we did for COVID was okay, why bother w the FDA approval process at all. Either we're making a special rule for COVID for some reason or we're going Libertarian on drugs, which then begs the question why bother w/ regulation at all in any case (as drugs would seem to be the best argument for regulation so if we don't regulate drugs what are we doing?)).

COVID vaccines truly are the holy of holies for which the Libs will abandon ANYTHING & EVERYTHING.

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Correlation may not prove causation but proving causation in the absence of correlation is surely even less likely?! So, I like to start with easier hypotheses to rule out, like "the vaccine saved millions of lives" kind of hypothesis in the situation where there is actually more COVID and excess death after vaccination than before. How does your model manage in that situation? Are models that prove lack of causation less wrong?

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Yes you are right. The edge case is that you can have causal determinism causing zero correlation. Variables can appear independent when they are causally connected by an, as yet undiscovered, hidden mechanism. That's where error comes in and serendipity.( I conjecture that accidental discoveries may be more valuable because they are more miraculous and reveal the previously hidden.)

On the 2nd point - Popperian falsification. Nothing wrong with that! It's not incompatible with what we've said here. Philosophically the smallest contradiction proves opposing claims for 'absolute' truth to be false.

I think it all depends on the claims and how much the available evidence supports or undermines claims. And your tolerance for error and confidence. This is where Bayes comes in. It can be used to articulate these finely balanced nuances in scientific arguments.

To be clear though, I'm not saying simple models with good joined up arguments aren't persuasive or need to be replaced! As far as I am concerned youve cracked the data to meet the legal standard of proof required against the vaccine. But to persuade others we might also have to consider lockdowns etc and attribute harms to actions and disentangle effects. This is where the model might prove helpful.

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These are interesting points. You can also have a negative or null correlation that has a causally positive component. The introduction of a mediator joining the effects then separates them into direct and indirect effects with opposite signs. In such a case, once you remove this indirect component, the direct effect appears that was formerly absent.

I particularly like the way such a finding illustrates the underlying counterfactual logic of a causal effect (the indirect showing the path through the mediator; the direct showing the path without the mediator).

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Wow.

Not once has anyone ever cottoned onto this when I've brought it up to disprove the assertion.

"With causation comes correlation".

Not even Taleb who claimed he could prove it but then went quiet!

It's in our book as a sidebar.

Anyway, very insightful comment ...thanks!

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I have your book and can confirm that it is excellent, always clear examples. I knew a lot and learned a lot more.

I will modify the George Box statement, courtesy of George Orwell: All models are wrong, but some are wronger than others. That is what you are getting at. Even causality has been a contested issue. There has been a big battle in physics for a hundred years on the interpretation of quantum mechanics with some just focused on treating it as a computational machine (the results are certainly spectacular like the fine structure constant accuracy of 10^-10 theory/experiment) and others seeking some meaning beyond, as did David Bohm. Everything we work out depends on a theory that may be limited but ultimately is set in a global weltanschauung. Even the experiments we do depend on a weltanschauung. A fascinating book illustrating that 'science' is never settled is Bruno Latour's Science in Action. I have a book by Julian Schwinger that has on the dedication page: If you can't join them, beat them. Thank you for all you are doing to cut through the muddle and deceit going on.

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Jan 23, 2023·edited Jan 23, 2023Liked by Martin Neil

I'll need some time to digest this, but on "all models are wrong", it's a bit of a meaningless philosophical point isn't it? Even well-established models of physical nature are not right for all time. I would rather say that all models are incomplete. Some have a degree of predictive ability. In general for models of social processes it is hard to say how long they will maintain any predictive ability that they might seem to have based on past data since these processes are very elastic. So there is not only omitted variable basis but the very processes which they seek to model are not necessarily of a kind that can be reliably modeled at all. We can account for omitted variables, measurement problems etc in how the model is set up but we cannot account for the endogeneity which comes from policy responses to the model and human responses to the policy, certainly not unless we observe a counterfactual. Even if the model does well for past data, we cannot be sure it will continue to do so. Models themselves change the world.

This brings us to the role of heuristics, nicely summarized yesterday by Scott Adams.

https://twitter.com/ScottAdamsSays/status/1616904705195794432

"Never trust the government" (putatively) gives better results than any model.

Such heuristics integrate relevant information which *cannot* be in the model, such as the government's known propensity for regulatory capture and the precautionary principle. The decision not to vax today is only for today and retains the real option value of doing so tomorrow. This is a major reason why most people thinking correctly about this, and without impertinent constraints, would not do so ever (even if it may be a prisoner's dilemma).

The ultimate heuristic based on the observed behavior of governments is the constitution, the Bill of Rights, and the rule of law. The entire system of restraints on governments is predicated on their observed propensity to deliver, on occasion, socially suboptimal outcomes. The beauty of this system is that it works for everything and for ever. If it constitutes an obstacle to the implementation of a certain course of action presently thought to be desirable, it is doing exactly what it was set up to do and we have no choice other than to take it on the chin, analyze it properly after the event, and propose, for next time, any relevant adjustments which do not throw out the baby with the bathwater.

This is because the system of one nation under the Constitution delivers societal welfare across the board. Even if it were to turn out that on any specfic issue it impeded a response which would have been welfare enhancing.

I really think it is high time we remembered this. We all have to live our lives making decisions under uncertainty and using heuristics to guide us. The government cannot simply override this. There is too much it doesn't, cannot and even should not know, about the problem and about our personal situations. A case needs to be made for any government intervention, and outside of the context of crisis management. It really isn't hard to grasp this. We have rights inter alia because the government has consistently shown a dreadful propensity to get things horribly wrong. Their defense is rationally worth internalizing some social cost.

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Excellent, thank you.

I found an extremely strong correlation between non-COVID excess deaths and new vaccinations in the German data (Pearson correlation coefficient is 0.98 nationally, between 0.85 and 0.97 for each federal state). Writing an article about it now.

EDIT: And here is the new article: https://vigilance.pervaers.com/p/german-excess-mortality-part-3

Non-COVID excess deaths correlate very strongly with newly administered vaccine doses in all German federal states during the time between calendar week 8/2021 and calendar week 27/2021 (Pearson correlation coefficient on the national level: 0.980).

95.8% of the variance in weekly, nationwide non-COVID excess deaths is explained by the number of administered vaccine doses in Germany per week (Standard Error: 78.7 deaths per week).

The correlation is strong enough to suggest a causal link. The insights we have gained into the pharmacodynamics of the nucleotide-based COVID-19 vaccines throughout the past years support the notion of a causal link between non-COVID excess deaths and administered vaccine doses.

One excess death occurred for every 1642 administered vaccine doses (95% CI: 1477-1808 doses).

From the time of their introduction to the market until the end of 2022 a total of 191,029,491 COVID-19 vaccine doses were administered in Germany according to RKI figures. This translates to 116,309 vaccinees who died as a direct consequence of receiving a dose of the nucleotide-based vaccines against COVID-19 in Germany (95% CI: 105,669 - 129,331).

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Missed this before. Will have a look. Thanks.

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I also developed a very simple model that explains both the Delta outbreak and its displacement by Omicron with only 2 independent variables:

- First doses administered

- Boosters administered

It's just a Gompertz function with a growth constant that is multiplied by the respective vaccination rate.

All other epidemiological models need an index case. The index case of delta occured in March 2020, so none of the models work, hence every article I find claims Delta occurred in October 2020 in India which is a very bold lie.

I am not a statistician/data scientist/mathematician, but I'm still good with numbers and am convinced that this model explains new variants perfectly.

If you are interested: https://vigilance.pervaers.com/p/modrna-products-caused-the-delta

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Thanks. The correlation that you found between number of jabs and excess deaths in Germany is impressive. That said, more numerical assessment (or more qualitative + quantitative clarification) would be helpful to account for the oddity that the number of excess deaths is greatly negative for low vaccination numbers.

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Jan 25, 2023Liked by Norman Fenton

The rationale looks so obvious when it's so clearly explained like this. Critical thinking can be hard and in a world where attention spans seem short (and are often celebrated for being so) it's not surprising that students and others are shocked to learn that models are imperfect. I follow a bit of stuff on climate change and it amazes me how much trust people put in those models. Anyway, another lovely article that helps us all think more critically. Well done chaps!

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Jan 25, 2023Liked by Norman Fenton

Thankyou for a great piece clearly investigating the system view of excess deaths. As an engineer who has spent my career on expermental model validation, and so have experienced the effects of the overall system and context on measured data. It is good to know that this analysis has been done and in the public domain even if the media ignore it.

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Jan 23, 2023Liked by Martin Neil

Very interesting article. My first reaction is to provide an alternative aphorism to your consideration:

"Some models are right but are not useful".

What I mean is that a model like yours, that takes into consideration much more variables that have an influence on an aggregate figure cannot be constructed, because many of the relationships between the variables are not quantifiable (for example, they might be not constant, varying in time or space).

For that reason it is only possible to use simplified models, that are necessarily wrong, but at least can be used to test hypothesis and, possibly, reject them. Unfortunately in some cases, simplified models are not useful because they cannot capture a complex phenomenon.

Trying to determine if the vaccines are the main underlying cause in an observed increase in all-cause death using only these two variables, proportion of vaccinated people and observed deaths in many different places and different times is not useful because the required mathematical model to capture the phenomenon is unfeasible as you brilliantly show in the article.

Therefore, in those cases we have to proceed in a different way, forget about modelling complex issues that are not appropriate to that kind of analysis and stop fooling ourselves.

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Rather than build a complete model from the data I think what we can maybe do is conjecture competing models with different assumptions, simulate the outcomes and then see which ones match the available data best. Theory led rather than data led.

We've started on this. Early days.

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Thanks, this is very instructive. Simple theory-based modelling for vaccines as cause of death has been done for several countries as you probably know. For example:

- Switzerland https://infocheckers.org /wiki/Blog:Excess_deaths_and_the_elephant_in_the_room_-_Part_2 (only short term effects)

- Netherlands https://maurice.nl/2022/12/12/vaccinatiesterfte-gemodelleerd/

(the second one in Dutch and suddenly Google Translate doesn't work for me, why?)

Perhaps you can give advice how to improve on such analyses, or how you would approach it. Notably the Dutch model becomes rather complex due to their - no doubt correct - inclusion of long-term effects, but how far should one go before one gets lost in complexity?

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Agreed. That is precisely what I tried to say on my comment. Use incomplete models to at least reject hypotheses that obviously are wrong. I am not sure it will be possible in this case but it is worth trying.

I have more doubts that you can use such competing models to "prove" hypotheses.

In any case, please continue your excellent work. I look forward to see what you can do.

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Good point.

It depends on the country. In the UK everyone was deprived of decent healthcare regardless in order to protect the NHS (when it's supposed to be there to protect us). I appreciate that in other countries things were very different with vaccine mandates etc. and discrimination was one way.

Disentangling these issues is the proverbial Gordian knot.

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Writing from France. It's a very curious thing, as I happened to look at some stats for excess death in the EU here:

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Excess_mortality_-_statistics

They summarize as follows:

"In November 2022, excess mortality varied across the EU. Bulgaria, Italy, Romania and Slovakia recorded little or no excess deaths. The most affected countries, Cyprus and Finland recorded excess mortality rates of 23.8 % and 20.5 % respectively, followed by Germany (15.6 %)."

As the UK is no longer a part of the EU (so sad!) the Eurostat is mum on those numbers. But those numbers just baffle me. I'm trying to make sense of them in terms of the causal model of excess death, but I clearly just don't know enough about what the nitty-gritty details of each particular country to make sense of why Finland should have such a high number of excess deaths, but why Italy should have so little. Some of these countries are highly vaccinated (like Italy) but others not so much (Romania for example). It's as if whatever causal story might work for one country is just falsified by another. At any rate, their excess mortality map is quite nice - nicely color-coded, except for sad little UK just in grey, excluded from "our" club!

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I recall that Italy and Spain were very hard hit during the pandemic; consequently the "culling effect" may be stronger there (that is, they should perhaps still show a reduced mortality rate, which is lacking).

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I wonder if batch variance is an issue. This has now been well documented. We have "hot" and "cool" batches. Storage, methods of delivery, etc. all play in as well. As the good professors have pointed out--damn complicated!

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Understanding a bi-variate model is a stretch for most policy makers. Even then they prefer to have someone identified as an 'expert' to summarise a conclusion. It is surprising just how often that summarised conclusion corresponds to the projected result desired by the policy maker!

So, I disagree. Not all models are 'wrong'. Many models give precisely the 'right' answer.

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Thanks for this great explanation. Just by going through the exercise of enunciating the various nodes it really highlights the uncertainty. I'm sure no-one in any government health department has gone through such an exercise. As you point out you can't assign probabilities to the nodes because we don't have data. If doing experiments, in say manufacturing, you could adjust a single variable and see what happens. But say we stopped vaccination now, and it was true there were adverse effects, we wouldn't know for how long that was occurring. There could be a long lag time in the system. It's the ultimate "wicked problem".

Another thought I had was, taking it a level higher, if one were really doing risk engineering, you would look at the outcome, and at some point you would have to say things aren't getting better. We better stop whatever we are doing. No-one is prepared to set a limit. Eg if excess mortality gets to this level we know we have done something wrong. Its like in a clinical trial where you are supposed to set the endpoints in advance of the trial. When excess non-COVID mortaility got to some level like 10% there should have been a pause. Instead a new explanation is pulled out like everyone needs statins. Seems hopeless when noone in authority has any concept of systems thinking.

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Whether and how the "vaccines" trigger cancer is covered here by Marc Giradot. "...some critical cells are naturally protected from immune attacks; stem cells and progenitor cells. At the core of the cellular foundation, these “royal” cells are too valuable to the body to be scuttled: They are indispensable for blood, for immunity and for regeneration. Moreover, they can replicate considerably given they are the closest to our original foetal cells.

This is a major loop-hole vaccinologists haven’t apparently considered: What happens if such a cell survives vaccine transfection? Inevitably, given their high replicability, they could trigger cancer - in a form or another".

https://covidmythbuster.substack.com/p/are-vaccines-triggering-cancers-vaccine

And Sasha Latypova says: 'Nobody Knows What is in the Vials. Covid-19 injections are dangerous, non-compliant biological materials. Their production must be stopped until a full investigation can be done'. So that is another factor to take in to account. They may be varying wildly. https://sashalatypova.substack.com/p/nobody-knows-what-is-in-the-vials

I remember that initially the Pfizer vaccine had to be administered within 5 days of being removed from ultra-low temperature freezers. Then that went out the window. As no proper trials were carried out or even legally required, the whole thing is a charade of safety and science.

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