October 16, 2020
by Mike Yeadon
The Chief Medical Officer, Professor Chris Whitty, and the Chief Scientific Adviser, Sir Patrick Vallance, are delivering a coronavirus data briefing at 10 Downing Street. Picture by Pippa Fowles / No 10 Downing Street.
"It is easier to deceive people than to convince them that they have been deceived." – Mark Twain
Dr. Mike Yeadon has degrees in biochemistry and toxicology and a research-based doctorate in respiratory pharmacology. He was director of new drug research for some of the world's largest pharmaceutical companies for over 30 years, and left Pfizer in 2011 as Vice President & Chief Scientist for Allergy & Respiratory. That was the highest research position in this area at Pfizer. Since Dr. Yeadon left Pfizer, he started his own biotech company Ziarco, which was sold to the world's largest pharmaceutical company Novartis in 2017.
SAGE made – and continues to make – two serious mistakes in assessing the SAR-CoV-2 pandemic, which made its predictions extremely inaccurate and catastrophic. These mistakes 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 without further action, a very high number of deaths will occur.
Mistake 1: Assume 100% of the population were susceptible to the virus and had no pre-existing immunity.
Mistake 2: The assumption that the percentage of the population infected can be determined by looking at what proportion of the population contains antibodies.
Both of these points contradict known scientific evidence about viruses and a significant amount of evidence, as I'll show. The more likely situation is that the vulnerable population is now sufficiently depleted (now 28%) and the immune population is large enough that there will not be another major national outbreak of COVID-19. Limited regional outbreaks will self-limit and the pandemic is effectively over. This is in line with current evidence, with COVID-19 deaths being a fraction of what it was in the spring, despite numerous questionable practices, all of which aim to artificially increase the number of apparent COVID-19 deaths.
The "scientific method" distinguishes us from the pre-Renaissance peoples who could fight plagues with prayer. We can do better, but only if we are strict. If an important theory is inconsistent with the results it claims to monitor, we have misunderstood it. Honest scientists are sometimes forced to accept that they have lost their way, and the best scientists then go back and distinguish what they have assumed from what can be shown beyond doubt.
After nearly 35 years of leading drug discovery teams and training in various biological disciplines, I like to think that I have a good nose for spotting inconsistencies. I was once told by a very senior person who was in charge of an R&D budget at the time that was close to the GDP of a small country that she had noticed that I had an outstanding talent, “weak patterns in sparse data to be recognized long before the competition ”. I take that. Sometimes I notice inconsistencies in my own thinking (more often you have to admit, others do this for me); on other occasions it may be the scientific work of others. This is an example of the latter – specifically SAGE.
It is my claim that SAGE made two absolutely central and incorrect assumptions about the behavior of the SARS-CoV-2 virus and its interaction with the human immune system in an individual – and tragically does so to this day, as well as a population size.
I'll show why, when you're on SAGE and you've accepted these two assumptions, you believe that the pandemic has barely started and that hundreds of thousands of people are likely to die in addition to those who have already died. I can empathize with everyone in this position. It must be desperate that politicians don't do what you tell them to do.
If, like me, you are certain that the UK pandemic is almost over as a horrific public health event in the UK, then you will likely be with me in amazement and frustration that SAGE, the government and 99% of the media are keeping the pandemic up fiction that it remains the biggest public health emergency in decades. I have already written extensively on the entire event (Yeadon et al., 2020). The UK's 2020 population-adjusted mortality rate ranks 8th for the past 27 years. It wasn't such an exceptional year from a mortality standpoint.
I believe that SAGE has been excruciatingly negligent and should be properly resolved and restored.
Crucially, I will show that the pandemic is effectively over and easily dealt with by a properly functioning NHS, as the proportion of the population that remains vulnerable to the virus is now too small to see a growing outbreak on a national basis Maintain level. Accordingly, the country should be allowed to return to normal life immediately.
Flaw in the modeling of the Imperial College
I am now going to show you the two absolutely deadly shortcomings of the infamous Imperial College model. There may be other weaknesses, but these two alone are enough to explain why SAGE believes the roof is about to collapse, while wet science, empirical data, says something else entirely. I believe we could and should repeal any measure that is in place, certainly anywhere south of the Midlands. It would probably be fine anywhere, but this is a firefight that is not needed and would detract from the strength of my reasoning.
What are these two assumptions? They are so simple and alluring that you may have to read them twice.
If you don't have the stomach to wade through all of this, check out the two pie charts below.
Initially, the Imperial Group decided to assume that since SARS-CoV-2 was a new virus, "the level of previous 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 that this certainly doesn't matter and is more of a scientific discussion point than something core and crucial. And is it not reasonable to think? I am afraid it is very important. It is also not a reasonable thing to assume. I'll get back to that first assumption in a moment.
Before that, the second fatal assumption was that, over time, modelers could determine what percentage of the population had been infected by looking at what part of the population had antibodies in their blood. This figure is around 7%.
Surely this can't be so terribly important either? And is it not true at all? I regret to re-inform the reader that this is absolutely crucial. And no, it's not true.
Read the full article here
HT / Photios