Puppet Masters
The Atlantic, May 21, has the story, headlined, "How could the CDC make that mistake?"
I'll give you the key quotes, and then comment on the stark inference The Atlantic somehow failed to grasp.
"We've learned that the CDC is making, at best, a debilitating mistake: combining test results that diagnose current coronavirus infections with test results that measure whether someone has ever had the virus...The agency confirmed to The Atlantic on Wednesday that it is mixing the results of viral [PCR] and antibody tests, even though the two tests reveal different information and are used for different reasons."
"Several states — including Pennsylvania, the site of one of the country's largest outbreaks, as well as Texas, Georgia, and Vermont — are blending the data in the same way. Virginia likewise mixed viral and antibody test results until last week, but it reversed course and the governor apologized for the practice after it was covered by the Richmond Times-Dispatch and The Atlantic. Maine similarly separated its data on Wednesday; Vermont authorities claimed they didn't even know they were doing this."
"'You've got to be kidding me,' Ashish Jha, the K. T. Li Professor of Global Health at Harvard and the director of the Harvard Global Health Institute, told us when we described what the CDC was doing. 'How could the CDC make that mistake? This is a mess'."
"The CDC stopped publishing anything resembling a complete database of daily [COVID] test results on February 29. When it resumed publishing test data last week [the middle of May]..."
First of all, the CDC's basic mission is publishing disease statistics on an ongoing basis. Reporting partial data flies in the face of what they're supposed to be all about.
But the big deal, of course, is combining results from two different tests — the PCR and the antibody — and placing them in one lump.
I've read the Atlantic article forwards, backwards, and sideways, and it appears the experts believe only PCR viral tests should be used to count the number of COVID cases.
So here is a takeaway I find nowhere in the Atlantic article: COMBINING THE TWO TESTS WILL VASTLY INFLATE THE NUMBER OF CASES.
I'm not talking about categories like "rate of infection" or "percentage." I'm talking about plain numbers of cases.
Some PCR tests will indicate COVID and some antibody tests will indicate COVID, and adding them together will pump up the number of cases. You know, that big number they flash on TV screens a hundred times a day.
"Coronavirus cases jumped up again yesterday, and the grand total in the US is now..."
THAT number.
The number media and government and related con artists deploy to scare the people and justify lockdowns and use to stop reopening the economy.
The brass band circus with flying acrobats and elephants and clown numbers.
Therefore, I'm not characterizing what the CDC is doing as a mistake. They've managed to create the illusion that absolute case numbers are higher than they should be.
Somehow, these "mistakes" always seem to result in worse news, not better news. The "errors" are always on the high side rather than the low side.
Case in point: the computer prediction of COVID deaths in the UK and US made by that abject failure, Neil Ferguson, whose track record, going back to 2001, has been one horrendous lunatic exaggeration after another. His 2020 projections of 500,000 COVID deaths in the UK and two million in the US were directly used to justify lockdowns in many countries.
The CDC, back in 2009, stopped reporting the number of Swine Flu cases in the US — while still claiming that number was in the tens of thousands. I've written in great detail about the scandal, which was exposed by then-CBS investigative reporter, Sharyl Attkisson. The CDC stopped counting cases, because the overwhelming percentage of tissue samples from patients was coming back from labs with no sign of Swine Flu or any other kind of flu. And yet, in a later retrospective "analysis," the CDC claimed that, at the height of the "epidemic," there were 22 MILLION cases of Swine Flu in the US.
Going all the way back to 2003 and SARS, the CDC and other public health agencies around the world hyped the dangers to the sky; the final official death count, globally, when the dust cleared? 800.
There is a tradition of lying on the high side, blowing up figures in order to create the illusion of destruction.
CDC? Mistake? The agency is certainly incompetent. But that's just the beginning of the story.
The only time they say there is no danger is when they're lying about the effects of vaccines.
My headline for the Atlantic article would read: SO HOW MANY COVID CASES SHOULD WE SUBTRACT TO GET THE ACTUAL NUMBER?
And the first paragraph would go this way: "Just when governors are trying to reopen their economies, a gigantic case-counting deception at the CDC is taking the wind out of their sails. The millions of Americans suffering financial devastation could be pushed back into a hole. Who is screaming to high heaven about THAT on the nightly news? No one. Why not?"
SOURCES:
'How Could the CDC Make That Mistake?'
"CDC Admits Mistakes in Covid Case Numbers" - Banned Video
Reader Comments
I'm starting to lean towards some'thing'. Something that needs sunlight and death.
So, back in 2015, Billy Boy does a vid on summer reading. Would you look at the book on top:
“How to Lie with statistics”.
There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton
Just imagine if this captured the imaginations of journalists and became the subject of alarmist articles.
Imagine further that the Health Dept then began testing for cases of the common cold and every day, headlines read "20,000 more cases of cold virus detected" - increasing every day as testing increased.
That is basically analogous to what is happening with Covid 19.
It is hard to resist the notion that something or someone is driving this irrational panic.