“You see, there is one very good thing about mankind; the mediocre masses make very few demands of the mediocrities of a higher order, submitting stupidly and cheerfully to their guidance”
Alfred de Vigny, from “Stello”
“Trust is earned, respect is given, and loyalty is demonstrated. Betrayal of any one of those is to lose all three.”
Ziad K. Abdelnour, from “Economic Warfare: Secrets of Wealth Creation in the Age of Welfare Politics”
I’d like to think the readers of this blog always knew this is the way it would turn out. The ‘Pandemic’, I mean. The narrative was just too clumsy, so blunt, so jackboots I’m-out-of-reasons-so-do-it-because-it’s-an-order. First, as we all remember, it was just two weeks to ‘flatten the curve’, and the public-health authorities would like you to wear a facemask, but you should understand there is really no evidence that they do anything to stop the spread of infection. Just to comfort you, like. Then masks absolutely worked, there was no end of proof; in fact, wear two! To be perfectly clear, the CDC did not ever order the population to wear two masks, or even recommend such a practice. It merely offered guidance in a manner which suggested only idiots don’t care about ‘being safe’.
Research released Wednesday from the CDC found that wearing a surgical mask underneath a cloth mask “substantially improved source control and reduced wearer exposure” to the viral particles that cause COVID-19. It marked the first time the CDC has released guidance on the most effective ways to wear masks, NBC News reported…“That’s all (the CDC is) saying,”
[Fauci} added. “One mask at least, but if you want to really be sure, get a tighter fit with a second mask.”
The ‘viral particles that cause COVID-19’. But before, it spread through ‘droplets’, and masks were effective at stopping them even though Fauci privately confided in email traffic that it spread through particles which are too small to be deterred by non-medical cloth masks. Besides, everybody had to wear a mask because of ‘asymptomatic spread’, which the CDC acknowledged publicly was not a real threat, but if something was not done the anti-maskers would never wear one, and they’d get a break, and public-health momentum would falter.
And on and on we went, full-tilt down Bullshit Boulevard. The PCR test was the ‘gold standard’, even though the guy who invented it was quite clear that it was not a test. I’d like to just let it go at that, but I can’t; the ubiquitous ‘fact-checkers’ employed directly by the pushers of the narrative claim that Kary Mullis ‘never said PCR testing couldn’t be used for testing for any diseases‘. Before we go any further, because I am prone to distraction – when you’re picking out the lampposts to swing the public-health quacks from, be sure to save some for the fact-checkers. Here’s what they said:
“He did invent PCR, which is a process used to test whether someone currently has Covid-19…He didn’t say PCR testing couldn’t be used for testing for any diseases, as some social media posts claim. Confusion dseems to have arisen from quotes of his in a 1996 article about HIV and AIDS. In this, neither the author of the article, nor Dr Mullis said PCR testing does not work or only identifies the DNA or RNA of the person being tested.”
A word here about the emergence of these ‘fact-checkers’; this is an insidious new tool used by corporate interests or others to discredit points of view which oppose The Narrative. There seem to be two primary modes of attack; argue that the person named is not the one who said what was claimed (rather than that it was never said at all), or in cases where the fact-checkers want to argue it was never said at all, pursue the point that the person claimed did not say those exact words, verbatim. Refinements on these themes include “we could find no evidence that this is true”, which could mean something so simple as ‘we didn’t look very hard”.
Anyway, in the instance we are examining, the ‘fact-checkers’ led off with the admission – which was not in dispute – that Kary Mullis did invent the PCR process…and followed it immediately with the contention that it is a process used to test whether someone currently has COVID-19. Is that a lie? Technically, no. Medical personnel did use it to test whether a person currently had COVID-19. It was just never designed for that purpose, and when used, does not yield reliable diagnosis of COVID-19. If you’re okay with unreliable results just as long as the process errs in favour of false positives, it works like a charm.
“50% is the same as random chance. In other words, this 99% specificity test can do no better than a coin flip when declaring a positive result. So screening in this scenario is not warranted because data that is no better than a coin flip is not data — it’s random chance.
However, the situation is much worse than this because neither PCR nor antigen tests are close to a 99% specificity level in practice, for various reasons (Braunstein et al. 2021). Lee 2020 performed a lab analysis of the CDC PCR test accuracy, which was widely used in the first months of the pandemic, and found it had a 70% specificity (i.e. 30% false positives) and 80% sensitivity (20% false negatives). This is because of faulty designs built in to the test from the beginning, as various news accounts from the Washington Post, NPR and ProPublica have since revealed.
This level of inaccuracy matches the CDC’s own internal report that found 33% false results when its PCR test was released in late February 2020, as reported on by National Public Radio (Temple-Raston 2020).
Intuitively, and in an emergency situation, we may think that a 70-80% accuracy rate is far from perfect but may still be “good enough.”
To be clear, this scenario describes the outcome when broad-based screening of a largely asymptomatic population is carried out, which is a stupid idea from the start – why would you get tested for a disease if you have no symptoms of it? And why would you believe a test that says you have it when you have no symptoms? In a population where infection has an overall low background rate, a majority of positive results are likely to be false positives. But with COVID, for the first time, positive test results were added to the database as ‘new cases’, and the higher the numbers climbed, the more panicky people got, and the more of them rushed out to get tested whether they had symptoms or not.
Anyway, back to the fascinating tale of Kary Mullis And The Fact-Checkers. If we must quote him word-for-word, then let’s do that. Here’s one – “Tony Fauci does not mind going on television, in front of the people that pay his salary, and lie directly into the camera.” You can spin and fact-check that all you like, but it sounded very negative to me, and I heard him say it. Here’s another: “If you could amplify one single molecule up to something that you can really measure, which PCR can do, then there’s just very few molecules that you don’t have a single one of in your body.” The PCR process is an augmentor which amplifies a single fragment of DNA until you can measure what is in it, but at high cycle levels the observer is likely to find material that was previously undetectable because it was present at such insignificant volume. Most people are likely to have fragments of dead virus in them, or minor virus fragments other than COVID. The PCR itself does not ‘detect’ viruses; that is up to the interpreter.
Which brings us to the usual rebuttal these days – a composite that says “Kary Mullis had one moment of brilliance when he developed the gold standard for COVID testing, a discovery he shared with others but for which he took all the credit. Apart from that he was a loon who dabbled in psychedelic drugs and thought he could talk with the dead, and who harbored any number of oddball beliefs. Oh; and he is a climate-change denier.” As the internet meme goes, “Follow the Science. But be sure to not listen to the guy who developed mRNA, or the guy who invented the PCR test”.
Be that as it may; I don’t want to get deep in the weeds on the subject of masks or PCR ‘tests’. What I wanted to talk about is the exit strategy for ‘the pandemic’, in which the miraculous vaccines will be covered with glory, and governments and the public-health quacks perfectly justified in having acted as they did. And what do we have to thank for it? Science?
Of course not. Beyond science lies…modeling.
A little background on modeling. It was modeling that got us into this mess in the first place; “Professor Lockdown” Neil Ferguson’s Imperial College model, which warned Boris Johnson that unless strict lockdown measures were adopted immediately, half a million Britons would die. He forecast 2 million dead in the USA. These apocalyptic figures came from a model, a computer program which works like a complex calculator; assuming these conditions (input value ‘x’), what effect will they have in, say, one year (value ‘y’)? In the Imperial College model, assumed replication rates of the virus were used to forecast numbers of deaths which would result in various scenarios, such as whether or not social distancing was observed, whether the public ‘locked down’ and stayed at home, and so forth. To say that it did not work very well is an understatement on the order of saying Stephen Hawking was quite smart, or that Elton John is sort of gay.
But the key here, for me, is ‘assumed’. The model, like models usually do, used arbitrary values born of assumptions to make its forecasts. And assumptions are not science. When very well-educated individuals who are well-versed in their field make predictions the result is often quite close to the forecast, certainly more so than when predictions are made by bumpkins and idiots. Predictions are what we think, what we assume will happen, based on a variety of variables, each of which might dramatically affect the outcome. Science is what we know will happen, because we did it until we proved it, and can thereafter reproduce it using the same conditions and get the same result.
The Imperial College model worked on assumptions. But it was far worse than that. It is one thing to plug in 1.6% as your R0 value for communicability, and get something like 72,506 deaths over 8 months, although you must use assumptions for the virus’s mortality rate as well, and get the same result the next time using the same values. But the Imperial College model built by Neil Ferguson got widely different results running it twice in a row using exactly the same inputs. Different results depending on what type of computer you ran it on. Different results depending on what colour shirt the programmer was wearing. All right, I made that last one up, although I bet it was never tried.
Firstly, the computer model. The source code behind the Ferguson model has finally been made available to the public via the GitHub website. Mark E Jeftovic, in his Axis of Easy website, says: ‘A code review has been undertaken by an anonymous ex-Google software engineer here, who tells us the GitHub repository code has been heavily massaged by Microsoft engineers, and others, in an effort to whip the code into shape to safely expose it to the public. Alas, they seem to have failed and numerous flaws and bugs from the original software persist in the released version. Requests for the unedited version of the original code behind the model have gone unanswered.’
Jeftovic believes the most worrisome outcome of the model review is that the code produces ‘non-deterministic outputs’. This means that owing to bugs, the code can produce very different results given identical inputs, making the code unsuitable for scientific purposes. Jeftovic says the documentation provided wants the reader to accept that given a ‘starting seed’, the model will always produce the same results. ‘Investigation reveals the truth: the code produces critically different results, even for identical starting seeds and parameters.’
Buddy, that ain’t science. Science is friction creates heat. Science is water expands as it freezes. We know these things because we can prove them reliably, over and over, getting the same results. Modeling which tells us the safe number for a ‘gathering’ is 15 is not science, the six feet of separation mandated for ‘social distancing’ is itself based on science from 100 years ago, when Karl Flügge was in his heyday and the disease was tuberculosis. Modeling that tells us if the reproductivity rate for viral replication is ‘assumed to be’ this, then hospitals will be overwhelmed with new cases in two weeks is not science. It is sensible to consider precautions. It is not sensible to make all of society take precautions, in advance, based on the worst-case scenario from your model. And pretty much all the public-health decision-making nowadays is based on modeling. Where has that gotten us already? A timely reminder.
Just over one year ago, the epidemiology modeling of Neil Ferguson and Imperial College played a preeminent role in shutting down most of the world. The exaggerated forecasts of this modeling team are now impossible to downplay or deny, and extend to almost every country on earth. Indeed, they may well constitute one of the greatest scientific failures in modern human history.
“The Commonwealth study wasn’t peer-reviewed, but it builds on a methodology that was. In a paper published this month in the journal JAMA Network Open, several of the same researchers estimated that Covid-19 vaccines averted more than 240,000 deaths between December 12, 2020, and June 30, 2021, before the worst of the delta variant ignited in the US.
In that same six-month window, vaccines were estimated to have prevented 1.1 million hospitalizations and halted 14 million infections, showing that more than 338 million doses had a powerful effect. “It was larger than we would’ve expected,” said coauthor Meagan Fitzpatrick, an infectious disease modeler at the University of Maryland.”
In instances in which the Keepers Of The Narrative wish to discredit a study, they simply announce coldly that it has not been peer-reviewed. Nothing to see here; move along. In this case they cannot get around the lack of peer reviewing, but frame it as merely a formality since the methodology has been proven sound as a dollar.
Consider. Many, perhaps most of the people who ‘died of COVID’ were at an age which closely approximates the age of…well, death. Life expectancy in the United States is 81 years for women, 77 for men. The actual effectiveness of the vaccines is hotly debated, but nobody will ever know with the degree of certainty that constitutes ‘data’, because both major manufacturers vaccinated nearly all their control group within a couple of months of the commencement of trials which were supposed to span years, and are still ongoing. So modeling was conducted on the effectiveness of vaccines whose actual effectiveness is unknown and still undergoing trials, based on how many people actually died of COVID when that number is demonstrably overestimated; public health does not even know the incidence figure, because everyone who tested positive was a ‘new case’, using a ‘test’ which is virtually guaranteed to ‘find’ a virus if you just turn the cyclic rate higher.
“Public health epidemiology is the science of counting to prevent disease and promote health. We count the number of new cases of a particular disease; this is the incidence. Then we count how much a disease has spread in a population; this is the prevalence.
When it comes to COVID-19, counting has been a challenge. Despite all the news articles and reports, we know very little about the incidence or prevalence of this new disease. Projections are based on models, and this uncertainty breeds fear.”
No shit? Really? So ‘modelers’ know very little about either the prevalence or incidence of COVID-19, while the age range that gets it most is far less likely to die from it, and people are now being urged to not get tested if they do not have symptoms whereas before everyone was encouraged to get tested because most ‘new cases’ were asymptomatic? Wow – it’s astounding how those modelers could estimate that the vaccines have saved a million lives.
I’d like to close with another quote, this time by Umberto Eco, from “Foucault’s Pendulum”.
“Not that the incredulous person doesn’t believe in anything. It’s just that he doesn’t believe in everything… He is nearsighted and methodical, avoiding wide horizons. If two things don’t fit, but you believe both of them, thinking that somewhere, hidden, there must be a third thing that connects them, that’s credulity.”