Were the official COVID numbers artificially inflated?
Can we find a systematic bias to make the numbers bigger than they actually were?
Freddie Sayers (UnHerd): So does that mean that you think the actual fatality
rate of this disease is much lower than the numbers that have been talked about?
Johan Giesecke: Much, much lower.1
(17 April 2020)
“U.S. Centers for Disease Control and Prevention Director Robert Redfield acknowledged during a House hearing Friday that COVID-19 data could be inflated because hospitals receive a monetary gain by reporting COVID-19 cases.”
(4 August 2020)
Professor Neil Ferguson is an Imperial College epidemic modeler—but he is so much more.
As COVID first became news, Ferguson, then directing the Collaborating Centre for Infectious Disease Modelling at the World Health Organization (WHO), made a tremendous announcement. Wielding the official support of the WHO, Ferguson claimed that in the following four months COVID would kill some 2.7 million people just in the United States and Great Britain—unless, he said, harsh lockdowns were swiftly imposed. In the UK, where Ferguson was also an official advisor to the government on medical-emergency response, harsh lockdowns were swiftly imposed. Then in the US. Then almost everywhere.
Not every epidemiologist agreed. Among the dissenters was Johan Giesecke, former Chief Scientist at the European Centre for Disease Prevention and Control.
As the first lockdowns went into effect elsewhere, Giesecke successfully advised his own Swedish government against doing the same. Then Giesecke sat for an interview with UnHerd’s Freddie Sayers on 17 April 2020, exactly one month after the publication of Ferguson’s WHO-endorsed COVID-fatality estimates and lockdown recommendations. Not beating about the bush, the Swedish epidemiologist expressed that he did not believe Ferguson’s estimates; the actual fatalities, he insisted, would be “much, much lower” than Ferguson was saying. Nor did he agree with Ferguson’s recommendations.
Was Giesecke’s position reasonable? On the basis of Neil Ferguson’s track record, certainly.
Several times before, Ferguson had shocked and awed the world with his models of pandemic catastrophe. Governments, each time, had rushed to satisfy his WHO-endorsed but frankly emotional outbursts. And that had always come at great cost to the citizens, because his predictions of pandemic catastrophe had always (and I do mean always) been (yes) completely wrong.
The worst prediction was in 2005: Ferguson’s computer model panicked the world by forecasting that bird flu would become the new Black Death and kill 200,000,000 people. And then… nothing! A Wile E. Coyote moment—total dud. Fewer than three hundred people died (worldwide).
After all that, it was at least remarkable that when COVID showed up Neil Ferguson was still around to influence anybody, let alone everybody, and using the exact same model2—wrong on bird flu by six orders of magnitude (!)—to predict a giant COVID catastrophe. And remarkable, too, that his harsh lockdown recommendations were swiftly followed (almost) everywhere, as if Ferguson’s track record were not Chicken Little’s but the True Prophet’s.
One possible explanation for the astonishing endurance of Neil Ferguson’s influence, we have argued, is that Ferguson’s boss is Bill Gates. And Bill Gates—undoubtedly the greatest power, worldwide, in the health-policy space—seems to want everyone doing whatever Ferguson says.
The following is a list of facts ( documented here ):
Bill Gates earned colossal profits—even by his fourth-richest-person-in-the-world standards—from the lockdowns.
The health bureaucracies that endorsed Neil Ferguson’s model and followed his lockdown recommendations have been getting huge grants from the Gates Foundation.
In exchange for his money, Gates has—explicitly—demanded from those health bureaucracies, on questions of medical-emergency response, tighter collaboration with the Gates Foundation.
Bill Gates usually gets what he wants.
But Giesecke, in Sweden, didn’t get the Gates memo. With perfect Nordic deadpan (his face barely twitches) he told UnHerd that he didn’t buy any of it. The lockdowns would do nothing to impede the spread of the disease (he was right). And Neil Ferguson’s numbers, he said, had to be wildly exaggerated.
He was right about that too. The official COVID death count in the US and Britain came in at barely 7-8% of Ferguson’s predicted deaths. Giesecke later got to crow about this (though without, I am sure, betraying the slightest emotion):
“Johan Giesecke … has called Ferguson’s [COVID] model ‘the most influential scientific paper’ in memory. He also says it was, sadly, ‘one of the most wrong.’ ”3
It is possible, however, that Ferguson’s model did even worse than that. In other words, perhaps the official death count has been inflated.
This possibility—though it appears to have received scant attention—is of great consequence, because we need to find out if we are run by honest (though incompetent) democrats or by clever, would-be totalitarians.
In this respect, it is of some concern that the Machiavellian hypothesis—which posits that we are governed by stealth totalitarians just waiting for their chance—is consistent with some rather important facts.
For example, it is consistent with the eagerness and readiness of Western bosses, in 2005, to use Ferguson’s bird-flu-is-the-new-Black-Death prediction to justify a totalitarian, bureaucratic takeover of the system. In the United States, President George Bush Jr. drew up a plan for an emergency military takeover of the country. (But then almost nobody died, and, without a proper excuse, the takeover plan fizzled.)
And yet one must, of course, consider the alternative hypothesis: incompetence. I have given consideration to that hypothesis (here and here) but have not found it convincing—it requires too much incompetence. And some of what happened seems inconsistent even if we grant infinite incompetence.
For instance, as far as I can see, all of the ‘errors’ that crept into the official statistics somehow always contributed (and dramatically) to inflate rather than deflate the COVID numbers. This remarkable consistency seems incompatible with the incompetence hypothesis. Because this hypothesis does not predict—not without some special assumptions—any particular direction for the errors of incompetent, honest democrats.
Perhaps the Machiavellian hypothesis, then, should at least be considered.
The Machiavellian hypothesis
The Machiavellian hypothesis does predict—and without any special assumptions—that stealthily totalitarian bosses in modern democracies will consistently try to inflate the official COVID numbers.
Why? Because the ‘medical emergency’ gambit to impose totalitarian controls on a democracy requires a panic. Neil Ferguson’s projections got the panic started, but to properly manage reality and keep the panic well stoked the embarrassment to Ferguson’s nonsense needed to be cushioned, and that required inflating official COVID numbers as much as possible.
But isn’t that outlandish?
It does sound outlandish. In fact, it seems to require the most radical category of conspiracy theory, where there is “a small coterie of billionaires who secretly run the world.” Worldwide bestselling history professor Yuval Noah Harari has explained to his far-flung world audiences that “such conspiracy theories never work.” The reason, in his view, is straightforward: such theories “underestimate the complexity of the system” (I have considered Harari’s views here).
The university-trained knowingly nod to such claims because in university culture it is taboo to consider “such conspiracy theories”; hence, theoretical support for the taboo is always welcome. I understand this—I do. But for the sake of argument—even just for a moment’s cheap thrill—let us consider “such conspiracy theories” here.
Imagine that “a small coterie of billionaires” wants the entire planet to go on lockdown so they can enrich themselves even more fabulously, strip us of our most fundamental rights, get us used to emergency totalitarianism, and teach us to say that it’s all for our own good.
As a purely logical exercise, ask yourself: What would these billionaires need to control in order to get away with this?
It seems to me they’d need to control—at least—the World Health Organization (WHO). Because the WHO has tremendous authority to influence health bureaucrats around the world. Wielding the WHO, these unscrupulous billionaires could then set down, as public-policy principles:
that, above a given critical value in the COVID positivity rate, lockdowns and other emergency measures must go into effect; and
that COVID diagnostic tests must be performed with a technology that, out of nothing, can generate arbitrarily high positivity rates.
In this manner, the power-hungry billionaires could artificially inflate the positivity rate above the critical threshold to extend the lockdowns indefinitely and then, again with WHO support, dangle before desperate citizens their experimental Big Pharma inoculations as the only way out of lockdown. Protected by corrupted governments from all liability for health damages to citizens, these Big Pharma companies could then proceed—with government assisting as the hired bully—to forcibly inoculate the captive citizens, subjugated now into lab rats. And then—using government as their hired tax collector—they could make the same citizens pay for the whole thing. Rent-seeking profits for Big Pharma? Yes, in the tens of billions.
But all of that is outlandish—granted.
And yet the following four are facts ( documented here ):
Billionaire Bill Gates has been showering more money on the WHO even than the US government.
Billionaire Bill Gates is one of the biggest investors in Big Pharma.
Billionaire Bill Gates is a world spokesman and leader for “a small coterie of [Big Pharma] billionaires.”
Leader-of-a-small-coterie-of-billionaires Bill Gates was all over the media energetically evangelizing the lockdowns and the experimental Big Pharma inoculations (he called them ‘vaccines’).
The following three are also facts ( documented below ):
The WHO indeed established that emergency measures should go into effect above a critical value in the COVID positivity rate.
The WHO indeed insisted on diagnostic tests that can conjure a high positivity rate out of nothing, and which massively inflated the positivity rate.
Other dramatic biases in the data also contributed to inflate the positivity rate.
Let’s do points 5-7 in order.
The WHO anchored emergency COVID measures to a critical value in the positivity rate
People find officially presented statistics tremendously convincing, which is why a dishonest official statistic can be a most effective lie. Benjamin Disraeli (according to Mark Twain) once expressed that thought with the outburst: “Lies, Damn lies, and Statistics!”
The statistic of interest here is the percentage of people infected with COVID: the rate of infection. To know it with precision, we must test the entire population simultaneously. Usually, that cannot be done. But we have an estimate in the positivity rate of our testing sample.
Say you tested 10,000 people. The question is: How many of those give you a COVID-positive result? If the answer is 1000, then the positivity rate in your sample is 10%. If that sample of 10,000 is properly representative of the overall population, and if the positives and negatives of the test are good enough for distinguishing the infected from the uninfected, both BIG ifs, then you can say—bracketing by the corresponding margin of error—that the rate of infection in the population is at or just around 10%.
Now, as the Johns Hopkins Bloomberg School of Public Health explains, emergency COVID measures were predicated on the positivity rate. Certainly the lockdowns:
“the World Health Organization recommended in May  that the percent positive remain below 5% for at least two weeks before governments consider reopening.”4
Put another way, lockdowns would remain in place while the positivity rate remained above 5%. The functional result of artificially spiking the positivity rate above 5%, therefore, would be to keep the world indefinitely on lockdown. So the question is: Did the powers that be mess around with the quality of the test, or of the samples, to spike the estimated positivity rate above 5%?
Let’s begin with the tests, then I’ll move on to the samples.
The PCR test, false positives, and pseudo-epidemics
To diagnose COVID, medical authorities in every country in the world have been using so-called molecular tests. They are all the rage. According to the US Centers for Disease Control (CDC), the best kind of test is the Nucleic Acid Amplification Test (NAAT), and within this category the absolute king, the “gold standard,” as they call it, is the Polymerase Chain Reaction (PCR) test.
But if the PCR test is the “gold standard,” that really makes you wonder about those other molecular tests. Because PCR—at least in practice—has turned out to be disastrous as a diagnostic tool.
Did I say “PCR is bad”? I did not. PCR can do wonders. It all depends on what you use it for. Please follow me on this.
PCR is tremendous, and its inventor, Kary Mullis, deservedly won a Nobel Prize for it. But PCR was a solution to a problem of molecular construction, not diagnosis. As Wikipedia states, PCR allows us “to rapidly make millions to billions of copies (complete or partial) of a specific DNA sample.” Millions to billions. Rapidly! The more times you cycle the output back into the reaction, the more copies you get.
If a given molecule is in the sample, even if very rare, PCR can find it by tuning the knobs so that you are replicating that molecule. After just a few cycles (rapidly) your needle in the haystack becomes several billion needles cascading out of your haystack—you can’t miss them.
But this is precisely what makes PCR dangerous as a medical diagnostic tool. Why? Because, by the power of instantaneous amplification, PCR can make it seem as though a healthy patient is infected.
To properly say that a patient ‘is infected’ with a virus, a doctor needs to see a viral load large enough for that patient. Mere presence of the virus is not enough, because, so long as copies of the virus are below a certain numerical threshold (specific to the quality of that patient’s immune system), the patient’s body can stop the virus from making trouble. And therein lies the problem: the PCR test does not tell you how abundant the virus is in the patient; it just tells you that it found at least some virus in the swab taken from the patient. Since PCR does that by taking any stray copy (or fragment thereof) of virus DNA in the patient’s sample and growing it into an arbitrarily large population, one must try to infer the viral load in the patient from how many PCR cycles were necessary to begin seeing the virus in the sample.
But the virus population in the sample is made to grow exponentially fast with each turn of the PCR cycle, it’s like a mini Big Bang, and that is too fast.
To help you perceive “exponentially fast,” here’s an illustrative image: picture a car with an accelerator that takes you from 0 to 10 mph if you depress it one centimeter, and to 100 mph if you give it a second centimeter, and to 1000 mph on the third centimeter. Could you drive that without killing someone? No. You’d always be shooting beyond the spot where you wanted to go. Neither, apparently, can anyone pick the number of PCR cycles without shooting past the spot needed for a medically relevant diagnostic signal: you get too many false positives.
“[With a PCR test] you can find almost anything in anybody. It starts making you believe in the sort of Buddhist notion that everything is contained in everything else, right? I mean, because if you can amplify one single molecule up to—to something that you can really measure—which PCR can do—then … There’s just very few molecules that you don’t have at least one single one of them in your body, okay? So that could be thought of as a misuse of [the PCR test], just to claim that it’s [a] meaningful [viral load] …”
—Kary Mullis, Nobel-laureate inventor of PCR technology, explaining how PCR might be misused (or ‘misinterpreted’) in medical diagnosis.
Is it really that bad? It is according to Kary Mullis, Nobel-laureate inventor of PCR (see pullout, above).
Anyway, the beauty of presuming extreme conditions is that they make extreme predictions, and extreme predictions are clear cut: if the predicted outcome occurs, you can’t miss it. To wit, if PCR diagnosis really does have the extreme problems I claim, then bringing PCR into widespread use will get you superabundant false positives, and hence pseudo-epidemics.
Pseudo-epidemic: When lots of doctors are falsely convinced that a contagious disease is spreading like wildfire.
In a pseudo-epidemic, the institutional gears are thrown, emergency measures come into place, people are medicated on the fly, and frantic research efforts are jump started. This can go on for months. And then, at some point, everybody abruptly realizes that, no, there is no epidemic. Everyone’s been running around in panicked circles for no good reason.
It is all quite ridiculous, but PCR has indeed made pseudo-epidemics common. As far back as 2007, long before the COVID crisis, the New York Times ran an article on the problem. This is what doctors were telling the NYT back then:
“There are no national data on pseudo-epidemics caused by an overreliance on such molecular tests, said Dr. Trish M. Perl, an epidemiologist at Johns Hopkins and past president of the Society of Health Care Epidemiologists of America. But, she said, pseudo-epidemics happen all the time.”5 (my emphasis)
Wait. What? All the time…!
Back in that 2007 piece, the New York Times focused on a pseudo-epidemic that had just taken Dartmouth-Hitchcock Medical Center (in New Hampshire) by storm, when doctors there—for months—panicked that everybody was getting whooping cough (spoiler: nobody was). This was just the latest pseudo-epidemic, the NYT explained: “there was a similar whooping cough scare at Children’s Hospital in Boston last fall.” Pseudo-epidemics were indeed cropping up everywhere.
But Dartmouth-Hitchcock will do for an illustrative case study.
Dartmouth-Hitchcock Medical Center, 2007
Here’s the executive summary. At Dartmouth-Hitchcock, wrote the New York Times,
“for months, nearly everyone involved thought the medical center had had a huge whooping cough outbreak, with extensive ramifications.”
And why? Because, wrote the New York Times,
“At Dartmouth[-Hitchcock] the decision was to use a test, [called] P.C.R., for polymerase chain reaction.”
And, as it turned out, the PCR tests diagnosed hordes of people—who didn’t have it—with whooping cough.
That caused a major panic. People make bad decisions in a panic. And the first bad decision can tip a whole chain of them, like falling dominoes. Speaking of which, as explained by infectious disease specialist Dr. Kathryn Kirkland,
“ ‘Because we had cases [at Dartmouth-Hitchcock that] we thought were pertussis [whooping cough] and because we had vulnerable patients at the hospital, we lowered our threshold.’ ”
Meaning this: they began giving the test to people who didn’t yet show clear symptoms of possible whooping cough.
“Anyone who had a cough got a P.C.R. test, and so did anyone with a runny nose who worked with high-risk patients like infants.”
By giving lots more people PCR tests, they got lots more positive diagnoses of alleged whooping cough, and their confidence grew that an epidemic was indeed afoot. So they went into emergency mode.
“Nearly 1,000 health care workers at the hospital in Lebanon, N.H., were given a preliminary test and furloughed from work until their results were in; 142 people … were told they appeared to have the disease; and thousands were given antibiotics and a vaccine for protection. Hospital beds were taken out of commission, including some in intensive care.” (emphasis added)
I know this is redundant, because the quote says it right below, but I feel called upon to emphasize this point: this level of activity and alarm was sustained for eight months.
“Then, about eight months later, health care workers were dumbfounded to receive an e-mail message from the hospital administration informing them that the whole thing was a false alarm.
Not a single case of whooping cough was confirmed with the definitive test, growing the bacterium, Bordetella pertussis, in the laboratory. Instead, it appears the health care workers probably were afflicted with ordinary respiratory diseases like the common cold.” (emphases added)
Doctors used their medical authority to order people about and give them treatment (thousands got antibiotics and vaccines) because doctors got scared that a whooping-cough epidemic was in progress, when in fact there was not one single case of whooping cough.
PCR had conjured an epidemic out of thin air.
I get the chills again when I read this sentence: “epidemiologists say one of the most troubling aspects of the pseudo-epidemic is that all the decisions seemed so sensible at the time.” Yes… The doctors were in fact completely, absolutely, entirely wrong—yet they were quite sure of themselves. And for eight whole months.
Or consider what Dr. Elizabeth Talbot, deputy state epidemiologist for the New Hampshire Department of Health and Human Services, said about this:
“ ‘You cannot imagine,’ Dr. Talbot said. ‘I had a feeling at the time that this gave us a shadow of a hint of what it might be like during a pandemic flu epidemic.’ ” (emphasis added)
Are you feeling an icy tingle crawling up your spine? Is your skin shriveling up in goosebumps? (Or is it just me?)
Indeed, what if some new kind of flu virus, a coronavirus of moderate lethality, were to show up some day? And what if the doctors became very confident this new virus was a massive problem because they were using PCR tests for diagnosis? The doctors, in a panic, might start ordering everyone about, making all sorts of important decisions, and thinking that all of those decisions were sensible. They might even agree with lockdown recommendations from health bureaucrats. And people would follow the doctors because—well, because they’re doctors.
PCR in the COVID crisis
You can see where I am going with this. But still: Why would anyone recommend that PCR technology be used in the COVID crisis?
I ask that because I cannot shake two quotes from that New York Times article on Dartmouth-Hitchcock.
One quote concerns the total lack of standards for these supremely delicate PCR tests: “each laboratory may do them in its own way.” And since they may, most technicians calibrate to find something, and so the tests give lots of false positives, which are then interpreted as epidemics, yielding pseudo-epidemics.
Which brings me to the second quote:
“Of course, that leads to the question of why rely on [these molecular tests] at all. ‘At face value, obviously they shouldn’t be doing it,’ Dr. Perl said.”
That does strike me as a reasonable position.
What could possibly be the argument in favor of these tests? Well, that they are fast. Yes, but, for medical-diagnostic purposes, what they give you fast is nonsense. So isn’t that like congratulating yourself for buying a ‘car’ without a motor because it was cheap?
So why did the bosses choose to measure the progress of the COVID pandemic with a technology known to conjure false epidemics out of nothing? Why would they use that technology to make policy decisions on a world scale? That was either very stupid or very smart. You, the reader, will have to decide which (neither is palatable…).
One thing, at least, should be clear: by the time COVID came along, the problems with PCR-based diagnosis had most certainly not been fixed. This was recognized even by the New York Times, which paper otherwise applauded almost everything decided by the WHO and the health bureaucrats.
In a piece from August 2020, the New York Times wrote that PCR tests were generating giant numbers of false COVID positives. The piece was titled: ‘Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.’6
The root of the problem was a communiqué from the WHO that read:
“WHO requests users to follow the instructions for use (IFU) when interpreting results for specimens tested using PCR methodology.”
In “most tests,” explained the New York Times, the “instructions for use (IFU)” of the manufacturer “set the limit”—that is, the cycle threshold or CT value—“at 40, a few at 37.” Meaning what? “This means that you are [considered] positive for the coronavirus [COVID-19] if the test process required up to 40 cycles, or 37, to detect the virus.”
“Tests with thresholds so high may detect not just live virus but also genetic fragments, leftovers from infection that pose no particular risk—akin to finding a hair in a room long after a person has left, Dr. [Michael] Mina said.”
Let’s say you are a spy and you need to track the movements of this person P, so you need a gadget that will tell you, from outside, if P is in any given room that you walk by. And suppose this gadget will beep at you that ‘P is in there’ if so much as a strand of hair from P’s head is in that room. If this is P’s place of work, his hairs (and other sheddings) are everywhere. Your gadget is useless: it is absurdly sensitive and almost always gives you a false positive. Well, that’s exactly what PCR is like when the CT value is set at 40, the WHO’s recommended level, according to Dr. Michael Mina, epidemiologist at the T.H. Chan School of Public Health, at Harvard.
Others concurred with Dr. Mina’s opinion.
“Any test with a cycle threshold above 35 is too sensitive, agreed Juliet Morrison, a virologist at the University of California, Riverside. ‘I’m shocked that people would think that 40 could represent a positive,’ she said. A more reasonable cutoff would be 30 to 35, she added. Dr. Mina said he would set the figure at 30, or even less.”
It appears from these interviews that doctors were having a hard time agreeing on what the proper CT (cycle threshold) value needed to be, which means that PCR, in 2020, was still an artisanal process where “each laboratory may do them in its own way.”
And it is from this context, precisely, that we can gauge the extremity of the WHO’s behavior. Because the CT value recommended by the WHO was so high that it could get even members of this community—a community without standards—to agree that it was nonsense (“I’m shocked,” Dr. Morrison said). With the WHO’s recommended threshold, these doctors told the Times, the test was too sensitive by two or three orders of magnitude.
That’s how you get pseudo-epidemics. It was happening in the COVID crisis.
And to make matters worse, only a yes-no answer (‘patient is positive’ or ‘patient is negative’ for COVID) was being reported. The labs were not sharing the number of cycles they were using, as if presence and not abundance in the body were the medically relevant concept!
Doctors complained about that too.
“ ‘We’ve been using one type of data for everything, and that is just plus or minus — that’s all,’ Dr. Mina said. ‘We’re using that for clinical diagnostics, for public health, for policy decision-making.’ But yes-no isn’t good enough, he added. It’s the amount of virus that should dictate the infected patient’s next steps. ‘It’s really irresponsible, I think, to forgo the recognition that this is a quantitative issue,’ Dr. Mina said.”
The policy that Mina—with understatement—called “really irresponsible” amounted to calling a person ‘infected’ if PCR could find any amount of SARS-COV-2 (SARS2 for short), which causes COVID-19 when sufficiently abundant, in their bodies. That such medical nonsense became public-health policy really is just kind of mind-blowing, but there I go again with my redundancies.
“ ‘It’s just kind of mind-blowing to me that people are not recording the C.T. values from all these tests—that they’re just returning a positive or a negative,’ said Angela Rasmussen, a virologist at Columbia University in New York. ‘It would be useful information to know if somebody’s positive, whether they have a high viral load or a low viral load,’ she added.” (my emphasis)
Now, recall that lots of people who merely had a cough or a runny nose (or nothing at all) were diagnosed with whooping cough at Dartmouth-Hitchcock in 2007. That’s because the PCR test is amplifying genetic fragments of a virus, and different viruses can and do have some fragments in common, so PCR will often say you’ve got the X virus when you’re really infected with a related virus that is not X. Or perhaps you are not even infected—you just have some leftover genetic debris from a previous infection of a different virus.
That should remind you of the COVID crisis. According to our health bureaucrats, many COVID cases supposedly look like colds or influenza or nothing at all (the latter are called ‘asymptomatic’). But since they were using PCR—and other, even less accurate—molecular tests for diagnosis, couldn’t it be that many such alleged COVID cases were instead colds, or influenza, or nothing at all?
If so, we should see a dip in the statistics for colds and flu in the 2020-2021 season.
Now, governments do not carefully track rhinovirus (common cold) statistics because it cannot be done, nor does anyone much care. As the name implies, colds are just too common, and their effects are too mild. Influenza is another matter: it gets tracked. So the place to look for a signal that colds, flu, and nothing at all were getting misdiagnosed as cases of COVID is in the influenza statistics. We should find a significant dip—worldwide—in reported influenza cases for the 2020-2021 flu season.
And we do. According to the US Centers for Disease Control (CDC):
“Flu activity was unusually low throughout the 2020-2021 flu season both in the United States and globally … the cumulative rate of laboratory-confirmed influenza-associated hospitalizations in the 2020-2021 season was the lowest recorded since this type of data collection began in 2005.” (emphasis added)
Now consider the question of responsibility.
It was already well known—at least as far back as 2007—that PCR and other molecular tests produced lots of pseudo-epidemics. PCR was therefore not a good pandemic-response tool. But it might be good for something else.
Say you were the billionaire leader of a “small coterie of billionaires” ruling over Big Pharma. And say you could get the WHO to do what you want, because nobody showers more money on the WHO than you. Then, after getting the WHO to decree that lockdowns (and the lifting thereof) would depend on the COVID positivity rate, you could spike that rate artificially, by…
having the WHO insist on the PCR and other molecular tests, which the WHO did; and
having the WHO insist on an absurdly high CT value, which the WHO did.
The reporting on the testing samples was corrupted
One way to inflate the positivity rate artificially even beyond what the PCR tests and other molecular tests were already doing on their own was to ask labs—at least some labs—to report to the State only the positive test results, as if they had found a 100% positivity rate (everyone is infected!). Many labs were doing precisely that.
For example, in mid-July 2020, a local CBS affiliate reported that a review of Florida’s State data found that “many small, private labs have been reporting only their positive results to the state—skewing the positivity rate higher.” Every one of those labs was reporting a 100% positivity rate, and the skewing effect of that is gigantic.7
Say you have 10 labs, all of them doing roughly the same number of tests, and 9 of them on average report a 10% positivity rate. But the remaining one reports a 100% positivity rate. The resulting positivity rate in the aggregated data will then be 19%. So in this example, if just 10% of the labs misreport the positivity rate as 100%, that almost doubles the presumed positivity rate in the aggregated data.
The skewing effect is larger as 1) the positivity rate in labs that report correctly gets smaller; and/or 2) the share of tests done by labs incorrectly reporting 100% positivity gets larger. That share may have been considerable. An article on this issue in the South Florida Sun-Sentinel reported that just one lab, Lab24 in Miami, which had reported a 100% positivity rate, had tested 10,000 people. But if we can believe the spokesman for Lab24, then the problem was truly systemic, for this spokesman claimed that health bureaucrats in Florida had been asking only for the positive results. Other labs claimed the same.8
It was apparently a difficult problem to fix. The CBS article stated: “Even after this issue came to light earlier this week, several dozen labs are still reporting 100% positivity rates, according to a review of Friday’s DOH [Department of Health] data.” And it quoted Jon Taylor, a PhD student at Florida Atlantic University who was looking at this problem: “ ‘It should be concerning,’ he said. ‘We are basing decisions off of the positivity rate, and we need to know why some labs are reporting 100 percent positive tests.’ ”
Indeed, as noted above, lockdowns were predicated on a positivity threshold of 5%. The functional consequence of spiking the positivity rates above that, in States with lockdowns, was to keep them in lockdown.
Now, overreporting as the specific cause for the spike was documented in Florida in mid-July 2020. But Florida was not the only State with a suspicious spike in the positivity rate. Just a few days earlier, a Fortune article from 7 July had remarked that “Arizona, Florida, South Carolina, and 25 other states all are seeing spikes in their ‘positivity rates’ ” (my emphasis).9 The authors were sure the spikes were artificial, due to problems with testing and/or reporting.
It is perhaps not coincidence that the cause was identified in Florida, for Governor Ron DeSantis—who, judging by the recent election, seems to be wildly popular there—had consistently questioned and bucked the interpretations and recommendations coming from the NIH and the CDC. The atmosphere of greater political and policy controversy about COVID that DeSantis created in Florida no doubt influenced the journalists there to examine more closely the obviously anomalous positivity-rate spike taking place across the entire United States.
(As an aside, the artificial spike in Florida’s positivity rate did not influence DeSantis to impose lockdowns. As another aside, those insisting on extended lockdowns tried to make political hay from Florida’s artificial spike. In Pennsylvania, Secretary of Health Rachel Levine, in a joint press conference with Pennsylvania Governor Tom Wolf, described Florida as “what happens when you don’t do any mitigation efforts and you basically let the virus burn.” When the dust settled, however, it was clear that Florida had not done worse than lockdown states, even though Florida is the Mecca for retirees and therefore has a much higher percentage of elderly residents.10 )
People who died with COVID reported as dying from COVID
On 7 April 2020 President Donald Trump’s Coronavirus Task Force gave a press briefing. Dr. Deborah Birx, the White House Coronavirus Response Coordinator, was asked the following question:
Reporter: Can you talk about your concerns about deaths being misreported by coronavirus because of either the testing standards or how they’re characterized?
This reporter was asking about two different sources of possible bias in the official numbers. But Dr. Birx ignored completely the issue of “testing standards” (which I have considered above) and answered only about “how [deaths] are characterized.”
Dr. Birx: So I think in this country we’ve taken a very liberal approach to [reporting COVID] mortality […] There are other countries that if you had a preexisting condition and let’s say the virus caused you to go to the ICU [intensive care unit] and then have a heart or kidney problem, some countries are recording that as a heart issue or a kidney issue and not a COVID-19 death. [… In the US] the intent is right now that if someone dies with COVID-19 we are counting that as a COVID-19 death.11 (my emphasis)
If someone died with a positive COVID diagnosis, no matter how sick from other things, or how severe or mild the COVID symptomatology, Dr. Birx said, “we are counting that as a COVID-19 death.” As she said herself, it was “a very liberal approach to [reporting COVID] mortality.” And it was medical nonsense.
It seems Dr. Birx may have agreed that this was nonsense, from how the reporter made reference to “your concerns … about deaths being misreported by coronavirus,” and indeed, by the style of her response. Apparently, Dr. Birx was not happy that health bureaucrats in the Trump administration were overreporting COVID deaths.
How bad did that get? Consider this: It was discovered in Florida that even people who died in motorcycle accidents were getting listed as ‘COVID deaths.’ When a local FOX affiliate investigated and asked the Florida Department of Health (DOH) to clarify what the criterion for classifying a death as a ‘COVID death’ was, the DOH replied that, if there was a COVID-positive test for that person (and the PCR tests dramatically raised the probability that there would), it would be called a ‘COVID death’ unless there were exclusion criteria such as “trauma, suicide, homicide, overdose, motor-vehicle accident, etc.”12 So the policy that Dr. Birx (perhaps ironically) called a “liberal approach” was being officially followed, but there may have been an unofficial directive to count even those deaths meeting the exclusion criteria as ‘COVID deaths.’
Again, this was caught in Florida, where skepticism of health bureaucrats was running rampant. How bad was it in other States?
Monetary incentives to report ‘COVID’ deaths
But there’s more. In May 2020, it was reported that:
“Birx and others were frustrated with the CDC’s [Centers for Disease Control] antiquated system for tracking virus data, which they worried was inflating some statistics—such as mortality rate and case count—by as much as 25 percent…”13
Birx was criticizing—to his face—then CDC Director Dr. Robert Redfield (some called it a “heated exchange”).
In context this is positively dramatic. For Dr. Birx had already pointed out that people dying with COVID were being classified as dying from COVID, so she was now complaining that the CDC was overcounting above the regular overcounting—and by 25%…! What was the CDC doing? Counting people as COVID deaths who didn’t even have a COVID-positive test result?
That is not a preposterous hypothesis. Two months later, in August 2020, Redfield publicly confessed to an additional reason that COVID deaths might be overreported: hospitals were getting more money if they reported more COVID cases.
“U.S. Centers for Disease Control and Prevention Director Robert Redfield acknowledged during a House hearing Friday that COVID-19 data could be inflated because hospitals receive a monetary gain by reporting COVID-19 cases.”14
It is not hard to imagine how some people—with, let us say, flexible scruples—might have reasoned on this: Well, the hospital needs more money (because which hospital doesn’t?). If they are giving out money for reporting COVID cases, then who cares if we have COVID-positive tests? Let’s just call them COVID cases.
A possible objection
One possible objection that I anticipate is: What about all the ‘excess deaths’?
The ‘excess deaths’ statistic is the higher numbers of dead in the COVID pandemic years 2020-2022 when compared to the average of the immediately preceding years. Indeed, there were ‘excess deaths.’ And it is true that this statistic does not have the problems mentioned above, because it is simply counting how many people died.
So, one might counter the above with: If COVID was not killing so many people as we were told, then why were there so many ‘excess deaths’ during the COVID pandemic years?
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My reply is that the ‘excess deaths’ numbers are an ‘all-cause mortality’ statistic. It doesn’t say anything about why people died. Some died from the COVID virus—no question. But how many? And how many died from bad policies? The ‘excess deaths’ statistic, all by itself, is silent on that question.
But we can compare ‘excess deaths’ between countries. Remember Johann Giesecke and no-lockdown Sweden? Here is how Sweden did in ‘excess deaths’ during 2020-2022 compared to other countries:
Sweden’s ‘excess deaths’ are nothing to write home about. And many of those are also due to bad policies, because Sweden made serious mistakes in the beginning with its nursing-home policies (later corrected).
This comparison loudly argues that the lockdowns and other nonsense policies killed way more people than COVID did (see here for an extended argument concerning nonsense COVID policies).
I left out one category of COVID overreporting: many deaths likely caused by the so-called ‘vaccines’ were classified as COVID deaths. But this point deserves a dedicated piece and I will deal with it later.
Even without that, it already seems, on the above, that the official COVID statistics might be dramatically inflated. In which case the true number of COVID deaths did not come in at 7-8% of Neil Ferguson’s nonsense predictions, but much, much lower.
Thus, even if one were to grant that lockdowns are a reasonable pandemic-response policy (a false assumption), even so there was never an argument for the lockdowns.
Could it be, then, that this was a Machiavellian operation? Might the Western bosses be stealth totalitarians waiting for their chance to abolish our rights and liberties?
The Machiavellian hypothesis, as I present it, recognizes that the bosses need to do things according to the rules of our political grammar: they need to say, convincingly, that their emergency measures are for our protection. First, therefore, they need us to panic. Neil Ferguson’s nonsense forecast got the panic started. But to keep the panic going indefinitely, the embarrassment to his ridiculous numbers needed to be cushioned. And that meant inflating the COVID statistics as much as possible.
I think the evidence considered here is entirely consistent with the hypothesis of a deliberate, multi-pronged effort to inflate the COVID statistics.
The alternative incompetence hypothesis has the problem that, though it predicts errors, it does not predict the direction of those errors. Yet here the ‘errors’ do all seem to go (most energetically) in the same direction, don’t they?
Neil Ferguson himself clarified that it was the same model—the same model he had been using for some 13+ years—in a tweet.
‘Professor Lockdown’ Modeler Resigns in Disgrace; National Review; 6 May 2020; by John Fund
‘COVID-19 Testing: Understanding the “Percent Positive” ’; Johns Hopkins Bloomberg School of Public Health; 10 August 2020; By David Dowdy, Gypsyamber D'Souza.
‘Faith in Quick Test Leads to Epidemic That Wasn’t’; The New York Times; 22 January 2007; by Gina Kolata.
‘Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be’; The New York Times; 29 August 2020; by Apoorva Mandavilli.
‘Dozens of Florida labs still report only positive COVID tests, skewing positivity rate’; CBS 12 News (West Palm Beach, Florida); 17 July 2020; by Danielle Waugh.
‘Florida changes COVID reporting requirements, saying labs did not share negative results’; South Florida Sun-Sentinel; 15 July 2020; by Cindy Krischer Goodman.
‘The coronavirus positivity rate is too high in 28 states’; Fortune; 7 July 2020; by Maria Aspan and Nicolas Rapp.
‘Three years later, who was right about the correct approach to COVID?’; Los Angeles Daily News; 16 June 2023; by John Stossel.
‘Donald Trump Coronavirus Task Force Briefing April 7’; 7 April 2020.
‘FOX 35 INVESTIGATES: Questions raised after fatal motorcycle crash listed as COVID-19 death’; FOX35 Orlando; 16 July 2020; by Danielle Lama.
‘As deaths mount, Trump tries to convince Americans it’s safe to inch back to normal’; The Washington Post; 9 May 2020; by Josh Dawsey.
‘CDC director agrees hospitals have monetary incentive to inflate COVID-19 data’; Christian Post; 4 August 2020; Blake Fussell.