(Part 4 of a series of indeterminate length, covering the pandemic)
Quite apart from the dubious identification of SARS-CoV-2 (which if invalid, automatically makes any tests based on it useless), the RT-PCR (Reverse Transcriptase – Polymerase Chain Reaction) test commonly used to diagnose COVID-19 is of questionable value.
Perhaps the most fundamental problem with the RT-PCR is that it relies on “amplifying” (essentially doubling) a small amount of genetic material many, many times, rendering the test generally unsuitable for determining “viral load,” and thus whether the subject has enough of the virus to cause any observed symptoms. (“Quantitative” versions exist, but are considered useless by many experts; indeed, the inventor of the PCR, Kary Mullis, has stated that “Quantitative PCR is an oxymoron.”1) Moreover, there doesn’t appear to be a means of independent validation of the test, which is all the more problematic in that COVID-19 has such general symptoms (cough, fever, shortness-of-breath, rash).
Crucially, it really isn’t known how susceptible COVID-19 RT-PCR tests are to false negatives2 or false positives.3,4 A number of authorities have pointed out that there is a trade-off when choosing the maximum number of amplifications, in that low numbers (e.g., 25) will give many false negatives and few false positives, while high numbers (e.g., 45) will give many false positives and few false negatives. The “optimal” number of amplifications is generally in between, with the exact value depending on the specific sequences being matched, how the test kits were manufactured, the reagents (essentially, the specific chemicals that do the work) employed, and conditions in the performing labs; as well as the priorities of medical authorities. Is it more important to catch virtually every true positive, even at the risk of massive false positives? Maybe, but I certainly wouldn’t want to be quarantined based on a false positive. The MIQE, a set of standards for real-time RT-PCR testing, weighs in with, “Cq [total amplifications] values higher than 40 are suspect because of the implied low efficiency [accuracy] and generally should not be reported.” Dr. Stephen Bustin, the driving force behind creation of the MIQE, goes further, indicating reservations with Cq (or Ct) values above 35 (about 27 mins. into the interview). Seconding the opinion of Dr. Bustin is none other than the ubiquitous Dr. Anthony Fauci, the head of the National Institute of Allergy and Infectious Diseases (NIAID), and the key voice of the Trump administration’s Coronavirus Task Force. In a July 16 podcast of “This Week in Virology,” the good doctor stated, “…If you get a cycle threshold [Ct] of 35 or more…the chances of it being replication-competent are miniscule…you almost never can culture virus from a 37 threshold cycle…even 36…it’s just dead nucleoids, period” (emphasis mine). This is interesting, because labs are often using Cq limits of 35, 40 or 45. For example, the UK National Health Service (NHS) appears to advocate a Cq of 40 (see p. 8), and the CDC apparently uses a Cq of 40 (see p. 34) for its diagnostic panel. Confirmation that RT-PCR tests will overwhelmingly produce false positive results at Cq’s over 30 comes from tests done in France:5
The research group of French professor Didier Raoult has recently shown that at a cycle threshold (ct) of 25, about 70% of samples remained positive in cell culture (i.e. were infectious); at a ct of 30, 20% of samples remained positive; at a ct of 35, 3% of samples remained positive; and at a ct above 35, no sample remained positive (infectious) in cell culture (see diagram).
This means that if a person gets a “positive” PCR test result at a cycle threshold of 35 or higher (as applied in most US labs and many European labs), the chance that the person is infectious is less than 3%. The chance that the person received a “false positive” result is 97% or higher.
(Note that the exact figures depend on the test and lab in question, and that if a sample was already positive at a lower cycle threshold (e.g. 20), chances of infectiousness are much higher.)
“The Trouble With PCR Tests” Swiss Policy Research, 4 Oct 2020,
https://swprs.org/the-trouble-with-pcr-tests/#:~:text=The%20issues%20with%20PCR%20tests%20are%20numerous%3A%20There,discovered%20during%20the%20early%20phase%20of%20the%20pandemic.
Another issue with RT-PCR tests is that there is uncertainty baked right in, because the tests aren’t looking for a full virus, but rather, selected snippets of RNA that are associated with the target virus (in this case, SARS-CoV-2), and believed to be stable (that is, unlikely to change). Are these sequences really unique to the virus? Bear in mind that SARS-CoV-2 was identified piecemeal, out of a genetic soup. Further, a number of observers claim that the sequences typically being used in SARS-CoV-2 tests are resident in every human being’s body, in varying amounts, so that an amplification test, particularly one using a high Cq value, will easily pick it up and report a positive result whether the person is afflicted with COVID-19 or not. What I find particularly interesting are the claims of doctors Thomas Cowan and Andrew Kaufman, than what the original electron micrographs – said to be of SARS-CoV-2 – were really depicting were sequences of messenger RNA that the human body – any human body – produces day-to-day, but in greater quantities when under stress, called exosomes. Apparently, there is a whole field of medicine opening up, based on supplementing the body’s supply of exosomes. Anyhow, in a video interview, Dr. Kaufman showed convincingly that the micrographs the public was shown of “SARS-CoV-2” look remarkably similar to micrographs of exosomes. Cowan and Kaufman allege that the RT-PCR tests are looking for sequences of certain types of exosomes, and not a novel coronavirus. If these doctors are right, then that would explain the haphazard results of RT-PCR testing. More importantly, it would mean that the whole narrative about a killer virus is false, and raise the question of what is putting people’s body’s under such stress. Is there anything new (5G?) that might account for what appears to be some sort of epidemic, at least superficially. In the interest of fairness to the establishment perspective, here is what purports to be a debunking of the exosome theory.
Whatever their theoretical advantages or shortcomings, a number of PCR tests employed during the”pandemic” have performed scandalously poorly. For example, Abbott Laboratories much-hyped, rapid-result “Now ID” test has been found in studies to produce an unacceptable level of false negatives. It should be borne in mind that Kary Mullis intended that the PCR process be used only for manufacturing genetic material and stated – in so many words – that it was unsuitable for diagnostic testing. Now, it may be that the technology has been improved in the meantime, but along with false results, there have been numerous instances of the patients getting seemingly random results, that is, positive tests followed by negative tests, followed by positive tests, and the like. Indeed, tech honcho Elon Musk has reported that he was tested via RT-PCR four times in one day, and got 2 positive results and 2 negative.
This fall, there is another issue that is urgently relevant, given that there is so much hysteria surrounding the increasing number of positive tests. Of course, this increase is largely the result of expanded testing, not some sort of second wave, but there is another point to consider: Given that the proportion of the overall population that is actively infected at any one time is likely to be very small – no larger than one percent – unless the specificity of these RT-PCR tests is incredibly high (and thus the false positive rate is incredibly low), then once testing is expanded to the general, asymptomatic public, the vast majority of test positives will be false positives. For example, if the general public is actively infected at a rate of 1%, the sensitivity is 90%, and the specificity is 95%, less than one-sixth of test positives will be true positives; that is, there will be more than 5 false positives for each true positive. If the specificity is 98%, and the other values remain the same, there will still be more than two false positives for every true positive. Really, it’s unlikely that anywhere close to even 1% of the public is infected at a given time, the true figure being perhaps 0.2%. If so, even with a specificity of 98%, if the sensitivity is 90%, mass testing of the general public will produce 11 false positives for every true positive.6 There are signs that this is what’s going on, coming out of the testing imposed on professional athletes. For example, this summer, Washington Nationals baseball star Juan Soto was isolated and kept from competition for 10 days following a single positive RT-PCR test (which was bracketed by a slew of rapid RT-PCR tests that were negative). Given that only the one test was positive, and no other members of the organization tested positive at about the same time, it is highly probable that Soto was victimized by a false positive. This quarantine was on top of another, lasting 14 days, simply based on “coming in contact” with a supposedly COVID-positive individual. Another National, reliever Fernando Abad, lost his chance at a big league roster spot after a single positive test – again, the only one in the organization around that time – in early July. Similarly, the starting quarterback of the Detroit Lions, Matthew Stafford, and his family were stigmatized after what was admittedly a false positive test. Portuguese soccer superstar Cristiano Ronaldo ripped the RT-PCR as “bullshit” after he tested positive for the third time and had to miss action yet again, when he’s never had any overt symptoms. What really bothers me as a journalist, is that top health officials that are briefing the media must know about this dynamic wherein low prevalence combined with wide testing of asymptomatic individuals will automatically lead to a preponderance of false positives. Why aren’t they properly informing the media? Or perhaps they are,7 but media leadership are ensuring that this vital context is not shared with the public. Either way, it’s outrageous. There is no moral authority behind labeling COVID skeptics (including those that think there’s a real pandemic but that its severity has been exaggerated) as “conspiracy nuts” when this kind of misinformation is being fostered at the highest levels.
It’s certainly interesting to contrast the massive scrutiny and hand-wringing over potential false negatives, with the deafening silence about false positives. No less than The Lancet has highlighted this discrepancy, and enumerated the under-appreciated costs of rampant false positives. For individuals, these include patients exposed to COVID-19 or other infectious agents after unnecessary hospital admission; income losses related to quarantine; psychological damage due to isolation; cancellation of travel plans; and delay of elective medical procedures. For society, these costs include misspent funding and human resources for test and trace activities; increased financial burden in the workplace related to temporary replacement of false positive workers; a depressed economy and its follow-on effects; overestimation of extent and severity of pandemic; inappropriate imposition of draconian shutdown/lockdown policies; and increases in depression and domestic violence. These are not trivial.
Moreover, aside from basic issues with the nature of the RT-PCR test, There have been quite a few episodes of contamination of test kits, some of which caused rampant false positives.
For a fuller treatment of the RT-PCR test, I recommend off-guardian.com’s “COVID19 PCR Tests are Scientifically Meaningless,” and Swiss Policy Research‘s “The Trouble with PCR Tests.”
[In my next post, I’ll look at the astonishingly liberal criteria that are being used to document COVID-19 as a cause of death. Thus far in my investigation of the pandemic, it’s become quite clear that every shading, every distortion, every mistake is in one direction, that is, making the “crisis” appear more severe than it really is. It’s not likely that this is accidental.]
1 John Lauritsen, for his part, ripped the whole idea of quantitative PCR in no uncertain terms, although this was some years ago and he was referring to testing for HIV rather than SARS-CoV-2:
PCR is intended to identify substances qualitatively, but by its very nature is unsuited for estimating numbers. Although there is a common misimpression that the viral load tests actually count the number of viruses in the blood, these tests cannot detect free, infectious viruses at all; they can only detect proteins that are believed, in some cases wrongly, to be unique to HIV. The tests can detect genetic sequences of viruses, but not viruses themselves.
What PCR does is to select a genetic sequence and then amplify it enormously. It can accomplish the equivalent of finding a needle in a haystack; it can amplify that needle into a haystack. Like an electronically amplified antenna, PCR greatly amplifies the signal, but it also greatly amplifies the noise. Since the amplification is exponential, the slightest error in measurement, the slightest contamination, can result in errors of many orders of magnitude.
To make an analogy: using the viral load tests to gauge viral activity would be like finding a few fingernail clippings; amplifying the fingernail clippings into a small mountain of fingernail clippings mixed in with other junk; and then having an “expert” come along and interpret the pile as representing a platoon of soldiers, fully armed and ready for battle.
In short, the viral load tests are a scam.
Lauritsen, John. “Has Provincetown Become Protease Town?” New York Native, 9 Dec 1996, http://www.virusmyth.org/aids/hiv/jlprotease.htm.
2 A “negative” is simply a finding that the test subject does not have COVID-19. A “false” negative is a negative finding when the test subject actually does have COVID-19. The false negative “rate” is the complement of what is termed “sensitivity” – the probability that a true positive tests as such; for example, if sensitivity is 0.85 (or 85%), then the false negative rate is 1 minus 0.85, or 0.15 (or 15%).
3 A “positive” is simply a finding that the test subject does have COVID-19. A “false” positive is a positive finding when the test subject actually does not have COVID-19. The false positive “rate” is the complement of what is termed “specificity” – the probability that a true negative tests as such; for example, if specificity is 0.94 (or 94%), then the false positive rate is 1 minus 0.94, or 0.06 (or 6%).
4 Not to belabor the issue, but if the test isn’t properly testing for the virus, or the virus isn’t responsible for the illness being called COVID-19, then all of this analysis I’m doing is beside the point; the test is irrelevant. At the time of publication, I found this article, which is largely based on this piece in the Spanish journal DSalud, noting recent inquiries and research that raise serious doubts about the reality of a novel coronavirus having actually been isolated and proven to cause illness. I’ll likely spend some time on this topic in a later post.
5 The results of Raoult et al. imply that one should strongly suspect a false positive any time it takes anywhere near 30 cycles to get a positive result. It is encouraging that doctors are often sharing the number of cycles that were run. For my part, I would certainly want to know this, because it’s a far different matter when it took, say, 18 cycles versus, say, 32.
6 For an understanding of sensitivity, specificity, positive predictive value (PPV), and why prevalence is crucial in calculating what percentage of positive or negative tests will be false, see this NIH paper. You can also play around with this app (using “Pre-test probability” to specify prevalence), to get an understanding of how the relative numbers of positive and negative tests, true and false, behave as key inputs are changed. Note that the app doesn’t accept fractional input values. To see exactly how to calculate PPV (and NPV) for yourself, consult this wiki page.
7 The Lancet, at least, notes this issue in the same piece wherein they examine the false positive problem. See the second paragraph following the panel in which they list the potential problems associated with false positives: “The current rate of operational false-positive swab tests in the UK is unknown; preliminary estimates show it could be somewhere between 0.8% and 4.0%. This rate could translate into a significant proportion of false-positive results daily due to the current low prevalence of the virus in the UK population, adversely affecting the positive predictive value of the test.” (emphasis mine) It should be borne in mind that the low prevalence of the virus would hold true throughout the world, once the first wave of the pandemic had subsided.
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