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The 1% conundrum: How a simple but flawed math prediction by US Covid-19 experts caused the world to panic and order lockdowns

The 1% conundrum: How a simple but flawed math prediction by US Covid-19 experts caused the world to panic and order lockdowns
In February, US Covid guru Anthony Fauci predicted the virus was ‘akin to a severe flu’ and would therefore kill around 0.1 percent of people. Then, just 11 days later, the fatality rate predictions somehow became 10 times WORSE.

Author’s Note:

In an earlier version of this article, I stated that Dr. Fauci had mixed up the Infection Fatality Rate of Covid with the Case Fatality Rate and that this was the cause of the different predictions that he gave on February 28 and March 11. I accept that there is no evidence for that assertion and would like to apologise to Dr. Fauci unreservedly for this error. I did not properly fact check with the author of the paper, published by Cambridge University Press, from which I was quoting. It is clear that Dr. Fauci did not mix up Infection Fatality Rate and Case Fatality Rates – it is also clear that he did not use these terms, and that the meaning I attributed to them in the earlier version was therefore incorrect. As a doctor, and author, I take great care in verifying my sources. I am often highly critical of those who do not. I made the mistake of relying on second-hand information.

Editor’s Note:

In the original version of this article, the headline read “The 1% blunder: How a simple but fatal math mistake by US Covid-19 experts caused the world to panic and order lockdowns,” and the article accused Dr. Fauci of having mixed up different fatality rates when he presented his evidence to Congress on March 11. We now accept that this was not the case and apologise to Dr. Fauci for the error. We have amended the article and its headline to correct this mistake. We are grateful to LeadStories, an organisation that partners with Facebook to check the accuracy of web content, for bringing this matter to our attention. As their analysis puts it: “Did a math mistake by America's most visible pandemic fighter panic U.S. leaders into imposing strict quarantines? No, that's not true… [the] website offers no evidence that a statistic known as ‘case fatality rate’ drove mayors and governors to order quarantines. At least one governor, Jay Inslee of Washington, focused on an entirely different metric. Even if ‘case fatality rate’ were the driving force, a March 2020 editorial in the New England Journal of Medicine authored by Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, did not blunder, correctly stating what was known at that time about the ratio of fatalities to cases of both seasonal flu and COVID-19.” Their full review can be read here.

Another organisation, Full Fact, has questioned the claim in Malcolm Kendrick’s article that Covid-19 kills no more than 0.1 percent of people. It asserts that this is incorrect and that the “real death rate is probably about five or ten times higher.” They argue: “Although it is difficult to know exactly how many people have caught Covid-19, or died from it, there have been a number of studies that have attempted to estimate the death rate. So far they tend to produce rates between 0.5% and 1%.

“Looking at English data, it is clear that the death rate must be much higher than the claimed 0.1%. Researchers who conducted a survey (the REACT-2 survey) by testing blood from a sample of the population, to see how many have Covid-19 antibodies, produced a detailed analysis that estimates an overall death rate of about 0.9%.

“The RT article says there were around 40,00 Covid deaths in the UK – the actual figure is at least 44,400 in England alone, up to 30 June. If the death rate was 0.1% then some 44.4 million people – or 79% of the population in England – would have had to have caught Covid by early June. No reasonable estimate gets close to this figure.” Their full analysis can be read here.

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When you strip everything else out, the reason for lockdown comes from a single figure: one percent. This was the prediction that Covid, if left unchecked, would kill around one percent of us.

You may not think that percentage is enormous, but one percent of the population of the world is 70 million people – and that’s a lot. It would mean 3.2 million Americans dead, and 670,000 Britons.

But where did this one-percent figure come from?

To find out we have to go back to what happened when the virus first began showing up at the beginning of the year.

As cases started to mount, on February 28, Dr. Anthony Fauci, the immunologist who heads up the US Institute of Allergy and Infectious Diseases, and some colleagues published a paper predicting that Covid would be about as serious as a bad flu epidemic. Their paper, released by the Institute of Allergy and the Centers for Disease Control and Prevention (CDC), and run in the New England Journal of Medicine, stated: “the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza which has a case fatality rate of approximately 0.1%.” In other words, one person in a thousand who gets it badly, dies.

Yet, just 11 days later, at a congressional hearing, Dr. Fauci had changed his tune: Covid was now going to be TEN TIMES more lethal than flu and would kill one percent of people. He observed: “The flu has a mortality rate of 0.1 percent. This has a mortality rate of 10 times that. That’s the reason I want to emphasize we have to stay ahead of the game in preventing this."

If influenza killed 50, Covid was going to kill 500. If influenza killed a million, Covid was going to get 10 million. No wonder Congress, then the world, panicked. Because they were told Covid was going to be 10 times worse than influenza. They could see three million deaths in the US alone, and 70 million around the world.

Here’s how one paper, “Public health lessons learned from biases in coronavirus mortality overestimation”, put it: “On March 11, 2020... based on the data available at the time, Congress was informed that the estimated mortality rate for the coronavirus was ten-times higher than for seasonal influenza, which helped launch a campaign of social distancing, organizational and business lockdowns, and shelter-in-place orders.

In healthcare, the US is very much the dog that wags the tail. The figures they come up with are used globally. So this one-percent fatality was taken as writ by most countries around the world.

In the UK, the group I call the Mad Modellers of lockdown, the Imperial College experts, created much the same panic. On March 16, they used an estimated fatality rate of 0.9 percent to predict that, without lockdown, Covid would kill around 500,000 in the UK.

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Which fatality rate, do you say?

So what happened in those 11 days to change Dr. Fauci’s mind to such a large degree?

It’s hard to be definitive. It’s possible one or other of the predictions was made in mistake. A pretty major thing to make an error about, but such things occur. On September 23, 1998, NASA permanently lost contact with the Mars Climate Orbiter. It was supposed to go round and round the planet looking at the weather, but instead it hit Mars at around 5,000 mph, exploding into tiny fragments. It didn’t measure the weather; it became the weather – for a few seconds anyway.

An investigation later found that the disaster happened because engineers had used the wrong units. They didn’t convert pound seconds into Newton seconds when doing their calculations. Imperial, not metric. This, remember, was NASA. An organisation not completely full of numbskulls.

Now you and I probably have no idea of the difference between a pound second and a Newton second (it’s 0.67 – I looked it up). But you would kind of hope NASA would. In fact, I am sure they do, but they didn’t notice, so the figures came out wrong. The initial mistake was made, and was baked into the figures.

Kaboom!

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With Covid, perhaps a similar mistake happened. Medicine has various terms covering different fatality rates and it could be that one type was substituted for another, and wrongly used to predict the likely death rate – and, as with NASA, no one picked up the error.

Rather like a pound second or a Newton second, there are a couple of medical terms that are relevant here. 

First, there’s the Infection Fatality Rate (IFR). This is the total number of people who are infected by a disease and the number of them who die. This figure includes those who have no symptoms at all, or only very mild symptoms – those who stayed at home, coughed a bit and watched Outbreak.

Then there’s the Case Fatality Rate (CFR). This is the number of people suffering serious symptoms, who are probably ill enough to be in hospital. Clearly, people who are seriously ill – the “cases” – are going to have a higher mortality rate than those who are infected, many of whom don’t have symptoms. Put simply – all cases are infections, but not all infections are cases.

Which means that the CFR will always be far higher than the IFR. With influenza, the CFR is around 10 times as high as the IFR. Covid seems to have a similar proportion.

Now, clearly, you do not want to get these figures mixed up. By doing so you would either wildly overestimate, or wildly underestimate, the impact of Covid.

So a simple NASA-style mistake is one possible explanation. Another might be that, in the 11 days between his two wildly-different statements, new evidence led Dr. Fauci to dramatically change his mind. If so, we have yet to hear what this evidence was, if any.

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Let’s look at the real numbers

But what we do have is the benefit of six months of real data to look at. So which of Dr. Fauci’s two predictions about Covid’s fatality rate have proved to be more correct: the 0.1 percent of Feb 28, or the one percent of March 11?

Is the estimate that Covid was going to have about the same impact as a bad influenza season right? Or the one that said Covid was going to be 10 times as deadly?

To answer this, we need to look at the numbers. The UK had around 44,000 Covid deaths as of June 30. Significantly less than 0.1 percent, but not that far off. Of course, people will say: “We had lockdown... without it so many more would have died. Most people have not been infected…” etc.

To ascertain that, we need to know the true Infection Fatality Rate (IFR). Is it a 0.1 percent, or one percent? If it is one percent, we have more than 400,000 deaths to go. If it is 0.1 percent, this epidemic has run its course. For this year, at least.

The latest estimate for the IFR in the UK from the Office of National Statistics is 0.49 percent. However, the most recent Medical Research Council Biostatistics estimate for the IFR for Covid-19 is 0.3 percent, which is far closer to 0.1 percent than one percent. Their research can be found here.

It is also generally accepted that the IFR rate falls during all pandemics. With Swine flu, the estimated IFRs varied from 0.1 percent to 5.1 percent. However, the final IFR was 0.02 percent, which was five times lower than the lowest estimate during the pandemic, as can be evidenced here. The more you test, the lower the IFR will fall.

With swine flu, remember that the IFR started at around two percent. In the end, it was 0.02 percent, which was five times lower than the lowest estimate during the outbreak. The more you test, the lower the IFR will fall.

So where can we look to get the current figures on the IFR? The best place to look is at the country that has tested more people than anywhere else as a proportion of their population: Iceland.

As of last week, Iceland’s IFR stood at 0.16 percent. It cannot go up from here. It can only fall. People can’t start dying of a disease they haven’t got.

This means that we’ll probably end up with an IFR of about 0.1 percent, maybe less. Not the 0.02 percent of Swine Flu – somewhere between the two, perhaps. In short, the 0.1 percent prophecy of Feb 28 has proved to be pretty much bang on.

Which means that we’ve had all the deaths we were ever going to get. And which also means that lockdown achieved almost precisely nothing with regard to Covid. No deaths were prevented.

Mangled beyond recognition

Yes, we are testing and testing, and finding more so-called cases. As you will. But the hospitals and ICUs are virtually empty. Almost no one is dying of Covid anymore, and most of those who do were otherwise very ill.

Instead of celebrating that, we’ve artificially created a whole new thing to scare ourselves with. We now call a positive test a Covid “case.” This is not medicine. A “case” is someone who has symptoms. A case is not someone carrying tiny amounts of virus in their nose.

Now, however, you test positive, and you’re a “case.” Never in history has medical terminology been so badly mangled. Never have statistics been so badly mangled.

When researchers look back at this pandemic, they’ll have absolutely no idea who died because of Covid, or who died – coincidentally – with it. Everything’s been mashed together in a determined effort to make the virus look as deadly as possible.

Lockdown happened because we were told that Covid could kill one percent. But Covid was never going to kill more than about 0.1 percent – max.

That’s the figure estimated back in February, by the major players in viral epidemiology. A figure that has turned out to be remarkably accurate. Bright guys. The March 11 one-percent prediction? Not so much….

We’ve killed tens of thousands – for nothing

And because we panicked, spooked by the one-percenters, we’ve added hugely to the toll. Excess mortality between March and May was around 70,000, not the 40,000 who died of/with Covid. Which means 30,000 may have died directly as a result of the actions we took.

We protected the young, the children, who are at zero risk of Covid. But we threw our elderly and vulnerable under a bus. The very group who should have been shielded. Instead, we caused 20,000 excess deaths in care homes.

It was government policy to clear out hospitals, and stuff care homes with patients carrying Covid, or discharge them back to their own homes, to infect their nearest and dearest. Or any community care staff who visited them.

We threw – to use Health Secretary Matt Hancock’s ridiculous phrase – a ring of steel around care homes. As it turned out, this was not to protect them, but to trap the residents, as we turned their buildings into Covid incubators. Anyone working in care homes, as I do, knows why we got 20,000 excess deaths. Government policy did this.

Lockdown lunacy

That is far from all the damage. On top of care homes, the ONS estimates that 16,000 excess deaths were caused by lockdown. The heart attacks and strokes that were not treated. The empty, echoing hospitals and A&E units. The cancer treatments stopped entirely.

Which means that at least as many people have died as a result of the draconian actions taken to combat Covid, as have been killed by the virus itself. This has been a slow-motion stampede, where the elderly – in particular – were trampled to death.

We locked down in fear. We killed tens of thousands unnecessarily, in fear. We crippled the economy, and left millions in fear of their livelihoods. We have trapped abused women and children at home with their abusers. We have wiped out scores of companies, and crushed entire industries.

We stripped out the NHS, and left millions in prolonged pain and suffering, on ever lengthening waiting lists, which have doubled. There have also been tens of thousands of delayed cancer diagnoses – the effects of which are yet to be seen, but the Lancet has estimated at least sixty thousand years of life will be lost.

Lockdown can be seen as a complete and utter disaster. And it was all based on the claim that Covid was going to kill one percent. A claim that can now be seen to be utterly and completely wrong. Sweden, which did not lock down, has had a death rate of 0.0058 percent.

It takes a very big person to admit they have made a horrible, terrible mistake. A catastrophic, running-into-Mars-at-5,000-miles-an-hour error. Let’s end this ridiculous nonsense now. And vow never to let such monumental stupidity happen ever again.

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The statements, views and opinions expressed in this column are solely those of the author and do not necessarily represent those of RT.

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