Outstanding Article that smashes the SCAMDEMIC to pieces - What SAGE Has Got Wrong

[jesuitsdidit]

 

 

 

 

https://lockdownsceptics.org/what-sage-got-wrong/

 

 

What SAGE Has Got Wrong

16 October 2020. Updated 21 October 2020.

by Mike Yeadon

Chief Medical Officer, Professor Chris Whitty, and Chief Scientific Adviser, Sir Patrick Vallance, give a Coronavirus Data Briefing in 10 Downing Street. Picture by Pippa Fowles/No 10 Downing Street.

“It’s Easier to Fool People Than It Is to Convince Them That They Have Been Fooled.” – Mark Twain

Dr Mike Yeadon has a degree in biochemistry and toxicology and a research-based PhD in respiratory pharmacology. He has spent over 30 years leading new medicines research in some of the world’s largest pharmaceutical companies, leaving Pfizer in 2011 as Vice President & Chief Scientist for Allergy & Respiratory. That was the most senior research position in this field in Pfizer. Since leaving Pfizer, Dr Yeadon has founded his own biotech company, Ziarco, which was sold to the worlds biggest drug company, Novartis, in 2017.

Abstract

SAGE made – and continues to make – two fatal errors in its assessment of the SAR-CoV-2 pandemic, rendering its predictions wildly inaccurate, with disastrous results. These errors led SAGE to conclude that the pandemic is still in its early stages, with the vast majority (93%) of the UK population remaining susceptible to infection and that, in the absence of more action, a very high number of deaths will occur.

• Error 1: Assuming that 100% of the population was susceptible to the virus and that no pre-existing immunity existed.
• Error 2: The belief that the percentage of the population that has been infected can be determined by surveying what fraction of the population has antibodies.

Both of these points run entirely counter to known science regarding viruses and to a significant amount of evidence, as I will demonstrate. The more likely situation is that the susceptible population is now sufficiently depleted (now <40%, perhaps <30%) and the immune population sufficiently large that there will not be another large, national scale outbreak of COVID-19. Limited, regional outbreaks will be self-limiting and the pandemic is effectively over. This matches current evidence, with COVID-19 deaths remaining a fraction of what they were in spring, despite numerous questionable practices, all designed to artificially increase the number of apparent COVID-19 deaths.

Introduction

The ‘scientific method’ is what separates us from pre-renaissance peoples, who might tackle plagues with prayer. We can do better, but only if we’re rigorous. If an important theory isn’t consistent with the findings it purports to oversee, then we’ve got it wrong. Honest scientists occasionally are forced to accept they’ve gone astray and the best scientists then go back and distinguish what they’ve assumed from what can be shown beyond reasonable doubt.

After nearly 35 years of work leading teams in new drug discovery, and trained in several biological disciplines, I like to think I’ve a good nose for spotting inconsistencies. I was once told by a very senior person who, at the time, was responsible for an R&D budget similar to the GDP of a small country that they’d noticed I did have an outstanding talent for “spotting faint patterns in sparse data, long before the competition did”. I’ll take that. Sometimes I spot inconsistencies in my own thinking (more commonly, it must be admitted, others do that for me); on other occasions it can be about others’ scientific work. This is an example of the latter – specifically, SAGE.

It is my contention that SAGE made – and tragically, continues to make to this very day – two absolutely central and incorrect assumptions about the behaviour of the SARS-CoV-2 virus and how it interacts with the human immune system, at an individual as well as a population level.

I will show why, if you’re on SAGE and have accepted these two assumptions, you’d believe that the pandemic has hardly begun and that hundreds of thousands of people will probably die in addition to those who’ve died already. I can empathise with anyone in that position. It must cause despair that politicians aren’t doing what you’ve told them they must do.

If, like me, you’re sure that the pandemic, as a ghastly public health event, is nearly over in UK, you will probably be with me in sheer astonishment and frustration that SAGE, the Government and 99% of the media maintain the fiction that this continues to be the biggest public health emergency in decades. I have written about the whole event in detail before(Yeadon et al, 2020). Mortality in the UK in 2020 to date, adjusted for population, lies in 8th place out of the last 27 years. It’s not been that exceptional a year from a mortality point of view.

It’s my view that SAGE has been appallingly negligent and should be dissolved and reconstituted properly.

Crucially, I will show that because the proportion of the population remaining susceptible to the virus is now too low to sustain a growing outbreak at national scale, the pandemic is effectively over and can easily be handled by a properly functioning NHS. Accordingly, the country should immediately be permitted to get back to normal life.

Background

A few pieces of background. In spring, membership of SAGE was initially treated like a state secret. Eventually, membership was revealed. I will say that, for myself, I was disappointed. I looked up the credentials of all the members. There were no clinical immunologists. No one who had a biology degree and a post-doctoral qualification in immunology. A few medics, sure. Several people from the humanities including sociologists, economists, psychologists and political theorists. No clinical immunologists. What there were in profusion – seven in total – were mathematicians. This comprised the modelling group. It is their output that has been responsible for torturing the population for the last seven months or so.

I cannot stress how important it is, whenever you hear the word “model”, that you ask who has the expertise in the thing that’s purportedly being modelled. It is no use whatever if the modellers are earnest and brilliant if they are not top quality experts in the phenomenon being modelled. Because you may be sure that from models come future scenarios – predictions if you will. If the model is constructed by people who are not subject-matter experts about the thing being modelled, then if they’ve constructed it in error, they will not know it. The outputs are expert-neutral, but they’ve assumed a power that is disproportionate. I think I understand why. Back to those pre-renaissance people. In times of uncertainty, those who purport to be expert leech appliers and bile colour interpreters became very important. They are seen to an extent as wizards of the modern age. In short, they are assumed to be seers – those who can foretell the future.

As an aside, it was my misfortune for a few years, while still a VP of respiratory research and new drug discovery, to have no choice but to work with a group of modellers, who had been brought in by credulous senior management. They claimed to be able to model certain pathological disease processes and, because of the insights they said their models would provide, show me new and effective ways to tackle difficult diseases, like severe asthma, idiopathic pulmonary fibrosis and the like. I smelled a rat. I spent many days with them. I would ask, “How do you know that you’ve included in your model all the important biological processes which bear on the output, the patients’ clinical condition?” No answer. I also asked, “How do you know what to assume is the starting condition for each of what you assert are the key variables?” They couldn’t adequately answer that, either. I told them that, if I put my empiricist’s reservations to one aside, and went with the flow, we wouldn’t know for a decade whether that had been the right call. Silence. I didn’t find their help much use. I hope I wasn’t too close-minded. But every one of the team, mostly mathematicians and computer programmers, were clever, earnest and really thought they could help. It’s a lesson I’ve never forgotten.

Flaws in Imperial College’s Modelling

I will now show you the two, absolutely fatal flaws in the infamous model of Imperial College. There may be other weaknesses, but these two alone are sufficient to explain why SAGE thinks the roof is about to fall in, whereas the wet science, the empirical data, says something entirely different. I believe we could, and should, lift every measure that’s in place, certainly everywhere south of the Midlands. It would probably be fine everywhere, but that’s to step into a firefight that is not needed and would detract from the force of my argument.

What are these two assumptions? They are so basic and alluring that you might need to read this twice.

If you don’t have the stomach to wade through all this, have a look at the two pie charts below.

First, the Imperial group decided to assume that, since SARS-CoV-2 was a new virus, “the level of prior immunity in the population was essentially zero”. In other words, “100% of the population was initially susceptible to the virus”.

You will be forgiven for thinking this surely doesn’t matter much and is a scientific debating point, rather than something core and crucial. And isn’t it a reasonable thing to think? I’m afraid it does matter, very much. Its not a reasonable thing to assume, either. I will come back to this first assumption in a moment.

But before that, the second fatal assumption, which was that, over time, the modellers would be able to determine what percentage of the population had so far been infected by surveying what fraction of the population had antibodies in the blood. That number is about 7%.

Surely, this too cannot be so terribly important? And isn’t it true, anyway? Again, I regret to inform the reader that yes, its absolutely central. And no, its not true.

Dr Yeadon has adjusted the size of the susceptible population in Chart 2 so it is between <30% and <40%

These charts are not intended to be mathematically perfect, as it’s not possible to convey all the subtleties of the situation. For example, we know that young children are rarely made ill by the virus and seem poor transmitters. The 10% value captures 2/3rd of those aged 0-11y. The prior immunity segment derives from work conducted entirely in adult volunteers – no children are included in that estimate of the size of the population that has prior ...

 

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[jesuitsdidit]

There is a video of his arguments here

 

https://forum.davidicke.com/index.php?/topic/12260-another-must-watch-video-delingpole-interviews-dr-mike-yeadon-blows-whole-scamdemic-to-pieces/

 

 

[jesuitsdidit]

Maybe if someone could summarise the article it would be useful for those who don't speak English well??

[jesuitsdidit]

Please please please

Either read this article or listen to the video.

This is probably the single most important article available regarding COVID 19.

Why?

Because it destroys, using verifiable science, every lie that we have been told regarding the SCAMDEMIC.

I know it is quite long but you don't need to read anything else after this. It destroys the whole thing.

If you do listen/read it and it's not as I have said IYO, let me know and we will discuss it.

This guy is right at the top of his field and he worked with Patrick Vallence in the past and he KNOWS that Vallence knows that what he is saying is correct.

It's a very very very in depth interview/article.

Read it.

[jesuitsdidit]

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