How Mainstream Literature Exposes the Circular Logic and Statistical Paradoxes Behind PCR Testing

The 5-year anniversary of the launch of ViroLIEgy.com was on August 18, 2026 (special offer included in the link). I began the site with two heavily censored articles on the Polymerase Chain Reaction (PCR) testing fraud. The first, The Testing Pandemic, examined how the “pandemic” was manufactured through PCR testing. The second, 20 Undeniable Facts About PCR Tests, highlighted 20 damning facts challenging the validity of PCR results. To celebrate the occasion, I thought it would be poetic to take another look at the PCR scam through the lens of several fresh, peer-reviewed studies.

Enjoy!

For those who have followed my work since the start of the “pandemic,” the problems surrounding the Polymerase Chain Reaction (PCR) test are simply old news at this point. My starting premise was always simple: Without having an actual purified and isolated “virus” available to calibrate and validate the test used to detect it, any results produced are simply meaningless noise at best and entirely fraudulent at worst. This is just simple logic. To validate a test designed to detect Bigfoot, for instance, you must first have Bigfoot available to verify that the test actually works and accurately detects the target. In medical testing, validating an assay for a specific “pathogen” or biochemical target requires a pure reference sample of that exact compound to prove the test can reliably detect it without cross-reacting with host cellular material. Without a properly established reference standard, the distinction between a true positive and a false positive cannot be reliably determined. Virology lacks that foundational standard, and this alone should be enough to call the legitimacy of any PCR test result into question.

As the Testing Pandemic wore on, more holes in the PCR boat began to appear, including from the very organizations relying on the legitimacy of the results. On October 12, 2020, I noted that, according to the CDC’s guidance on SARS testing, the likelihood of a false-positive PCR result was high when disease transmission was absent or very low. While this guidance was written for “SARS-CoV-1” testing, the underlying principle applies to any diagnostic test: when the condition being tested for is rare or prevalence is low, the probability that a positive result represents a “true case” decreases. The agency recommended that only those with a high suspicion of having SARS should be tested:

“In the absence of SARS-CoV transmission worldwide, the probability that a positive test result will be a “false positive” is high. To decrease the possibility of a false-positive result, testing should be limited to patients with a high index of suspicion for having SARS-CoV disease.”

https://archive.cdc.gov/www_cdc_gov/sars/guidance/f-lab/assays.html

This same diagnostic principle applies to “SARS-CoV-2” testing. However, it also has implications beyond the interpretation of individual test results. Positive PCR results were used not only to identify “cases,” but also to estimate disease prevalence and to calculate measures such as “transmission” and “infectivity.” Consequently, the reliability of these population-level estimates depends upon the reliability of the underlying PCR-defined case counts.

As I noted on November 13, 2020, the predictive values of PCR results are only considered reliable if the pretest probability is known. If the pretest probability is low, the likelihood of false-positive results is considered high.

In order to determine pretest probability, the CDC states that disease prevalence must be known:

“Pretest probability: Probability of a patient having an infection before the test result is known: based on the proportion of people in a community with the disease at a given time (prevalence) and the clinical presentation of the patient.”

https://www.cdc.gov/coronavirus/2019-ncov/lab/resources/antigen-tests-guidelines.html

The American Society for Microbiology (ASM) explained this same concept in June 2020, emphasizing that the interpretation of diagnostic test results depends on knowing the underlying prevalence of disease in the population being tested. They defined prevalence as “the number of known cases of the disease in a population at a given time,” noting that, in the general population, “the prevalence is also known as the pretest probability.” ASM further explained that a diagnostic test “can best be interpreted when the pretest probability (prevalence) is known” and “will not help (and will likely only lead to confusion) when the pretest probability of a disease is either very high or very low.”

​In other words, diagnostic tests cannot be interpreted in isolation because their meaning depends on how much disease is present within the population. Yet, the ASM openly acknowledged the massive uncertainties surrounding “SARS-CoV-2” testing at the time. They noted that “the sensitivity and specificity of SARS-CoV-2 tests are still being investigated” and that, as the “virus” is new, “we do not have a gold-standard test to assess the results of new SARS-CoV-2 tests.” The ASM further admitted to not knowing “the true prevalence of COVID-19” and that “it may not be possible to calculate a true pretest probability.”

They concluded:

“As disease prevalence decreases, so does the posttest probability that a patient actually has the disease, meaning the chances of getting a false-positive test are higher.”

​The points raised by ASM highlighted the central issue: interpreting a positive PCR result depends on knowing the underlying prevalence of disease within the population being tested. This naturally raises an important question: how is disease prevalence determined in the first place?

On January 11, 2021, I examined this issue further by looking at the equation used by the CDC to calculate disease prevalence:

Notice that all new and pre-existing cases must be known. As I pointed out at the time, this creates quite the conundrum. Ideally, cases would be identified through clinical assessment combined with appropriate diagnostic criteria. However, “COVID-19” was defined by nonspecific signs and symptoms that overlap with allergies, the common cold, influenza, pneumonia, and numerous other conditions. Clinical presentation alone could not reliably distinguish “COVID-19” from these other illnesses. Even the CDC acknowledged this limitation:

“You cannot tell the difference between flu and COVID-19 by the symptoms alone because many of the signs and symptoms are the same. Testing is needed to confirm a diagnosis.”

Because of this, laboratory testing, primarily PCR, became the method used to “confirm” cases, and therein lies the problem.

If the accuracy of PCR results depends upon disease prevalence being known, yet disease prevalence cannot be determined without using PCR to generate the cases, a circular confirmation loop is created. The test identifies the cases needed to validate the accuracy of the very test used to identify those same cases.

Interestingly, the WHO reinforced this same principle a few days later on January 21, 2021. In a memo to medical professionals, the WHO warned that the risk of false-positive results increases as disease prevalence decreases, regardless of the claimed specificity of the test.

“WHO reminds IVD users that disease prevalence alters the predictive value of test results; as disease prevalence decreases, the risk of false positive increases (2). This means that the probability that a person who has a positive result (SARS-CoV-2 detected) is truly infected with SARS-CoV-2 decreases as prevalence decreases, irrespective of the claimed specificity.”

https://www.who.int/news/item/20-01-2021-who-information-notice-for-ivd-users-2020-05

In other words, even a test claimed to have very high analytical specificity can produce a substantial number of false-positive results when the condition being tested for is uncommon. The WHO’s guidance therefore reinforces the same principle recognized by the CDC and ASM: the meaning of a positive PCR result depends on disease prevalence, yet disease prevalence is estimated from “PCR-confirmed” cases. As a result, PCR results are used to generate the very prevalence estimates required to interpret those same results, creating the circular confirmation loop being described.

The PCR Circular Confirmation Loop

• PCR accuracy depends on pretest probability.
• Pretest probability depends heavily on disease prevalence.
• Prevalence requires knowing how many people actually have the disease.
• “COVID-19” could not be reliably diagnosed from symptoms alone because they were nonspecific.
• PCR therefore became the primary mechanism for identifying “cases.”
• Those PCR-defined cases were then used to estimate disease prevalence.
• That prevalence was then used to determine the predictive value of PCR results.
• The predictive value was then used to justify confidence in the PCR results.

In short: the test generated the cases, the cases generated the prevalence, and the prevalence was used to validate the test.

The implications of this issue became even more apparent as mass PCR testing expanded. The CDC’s own SARS testing guidance emphasized that when disease transmission is absent or low, the probability that a positive result represents a false positive increases, which is why testing was recommended only for those with a high index of suspicion. Yet during the “COVID-19” response, PCR testing was expanded far beyond individuals with a high clinical suspicion of disease and was frequently applied to large populations, including those without symptoms.

The result was the identification of large numbers of PCR-positive individuals who had no symptoms at the time of testing, commonly referred to as “asymptomatic infections.” In August 2020, the CDC revised its testing guidance language to refer to these individuals as “healthy people.” In other words, mass testing was increasingly used to identify positive test results among individuals who were not clinically ill.

This concern was not new. In its 2011 guidance on whooping cough, the CDC previously warned:

“Testing asymptomatic persons should be avoided as it increases the likelihood of obtaining falsely-positive results.”

This was not merely a theoretical concern. A striking example was detailed in the New York Times article Faith in Quick Test Leads to Epidemic That Wasn’t. The episode occurred in 2007, over a decade before the “COVID-19 pandemic,” at Dartmouth-Hitchcock Medical Center in New Hampshire. A suspected whooping cough outbreak led to widespread PCR testing among healthcare workers, with numerous people initially identified as having pertussis. Yet subsequent investigation failed to substantiate the outbreak. Samples from 27 suspected cases were sent to state health departments and the CDC for further testing, and none showed evidence of pertussis by culture. Additional CDC testing found only one PCR-positive result among 116 retested samples, while other testing failed to establish that the individual was “infected” with pertussis. The episode was ultimately determined not to have been a pertussis epidemic.

The lesson was not lost on the medical community. Dr. Cathy A. Petti, an infectious-disease specialist at the University of Utah, summarized the episode bluntly:

“The big message is that every lab is vulnerable to having false positives. No single test result is absolute and that is even more important with a test result based on P.C.R.”

The Dartmouth episode provides a concrete “pre-COVID” example of how PCR results can shape the perception of an epidemic without independently establishing that the target “pathogen” is actually present. In both instances, the underlying diagnostic failure was remarkably similar: a laboratory result was treated as proof of disease, the resulting “cases” generated the illusion of an outbreak, and subsequent investigation revealed that the purported epidemic could not be substantiated.

Despite this warning, large-scale testing campaigns were performed on asymptomatic populations during the “COVID-19 pandemic,” resulting in substantial numbers of PCR-positive results among individuals without symptoms. This pattern was observed across multiple settings, including:

• China: 70% asymptomatic (SCMP), over 90% (VOA News), 98-99% in Shanghai (Yahoo News), and 100% in Wuhan (Nature).
• Meatpacking plants: 85% (News-Leader), 94% (Yahoo), and 100% (Daily Mail).
• Prisons: 99% asymptomatic in Connecticut (Council on Criminal Justice), over 90% in other U.S. prisons (USA Today).

Additional studies reported similar trends globally, such as 86% asymptomatic in the UK and up to 95% in Karachi. Thus, the data demonstrated a fundamental flaw: a positive PCR result did not correspond with observable illness in the vast majority. In other words, a positive PCR result does not correlate with disease, and it cannot be interpreted as identifying the cause of one. Nevertheless, these PCR-positive results were counted as “cases” and used in calculations of disease prevalence, transmission, and infectivity.

Pairing this information with the absence of a purified and isolated “virus” available to calibrate and validate the tests, the PCR narrative begins to unravel using the mainstream sources alone. We did not even need to examine the extremely high cycle thresholds (Ct values) used during testing, the lack of standardization across assays, the well-documented concerns over contamination, or the fact that PCR inventor Kary Mullis opposed using PCR as a standalone diagnostic test for “infectious” disease. These are all important pieces of information that further poke holes in the narrative, but the ship has already taken on too much water and is going down regardless.

Nor did we need to challenge the broader assumptions of genetics to establish reasonable doubt. Without a physically isolated “virus” serving as a validated reference standard, the “viral genome” itself is a computationally derived model assembled from RNA sequences whose origin and relationship to a specific disease-causing entity must be assumed. The admissions made by the very institutions promoting and relying upon PCR are more than enough to expose the significant cracks in the foundation.

While I believe the validity and interpretation of PCR results have been extensively and successfully challenged over the past six years, I continue to share peer-reviewed research that supports these concerns whenever I encounter it. In my view, some of the most compelling critiques of the prevailing narrative come from mainstream sources themselves. The CDC, WHO, and ASM established the diagnostic principles, acknowledged the uncertainties surrounding “SARS-CoV-2” testing, and provided the very information that exposes the circularity at the heart of PCR-based case determination. It is far more difficult to dismiss concerns when they originate from the very institutions, organizations, and literature that underpin the mainstream position.

Fortunately, two recent peer-reviewed studies have surfaced that offer new insight into the very problems described above. Let’s examine how their findings reinforce the concerns I have raised since the beginning of the “pandemic.” While mainstream literature frequently frames these problems as “false-positive” results, that label itself is a misnomer. To have a false positive, you must first have a physically validated baseline to define a true positive. Without that gold standard, the test is not occasionally making mistakes—it is generating arbitrary output across the board. By the end of this analysis, it will become clear that we are not just dealing with a high rate of test error, but with a testing mechanism where every positive result is fundamentally false.

The Missing “Viral” Reference Standard

The first study, Role of exosomes in false-positive COVID-19 PCR tests: Non-specificity of SARS-CoV-2-RNA in vivo detection explains artificial post-pandemic peaks, was published on February 14, 2024. The central argument made by the authors perfectly captures why a purified and isolated “virus” is necessary to calibrate and validate the tests designed to detect it. As noted in the opening summary, governments around the world relied primarily on PCR results to define the numbers of “infected” individuals, hospitalizations, and deaths. These statistics were generated under the assumption that PCR tests were 100% capable of accurately identifying “true infections.”

However, after analyzing published PCR data, the authors concluded that a large percentage of false-positive results had distorted these statistics. In their view, the effect was so substantial that it created artificial outbreak peaks in the second and subsequent waves of the “pandemic.” Guided by what they describe as the scientific method, the researchers proposed an alternative hypothesis: rather than detecting RNA unique to “SARS-CoV-2,” PCR was detecting RNA associated with “exosomes” released by human cells as part of a generalized “immune response” to respiratory “viral infections.” In other words, they argue that much of what was interpreted as evidence of “SARS-CoV-2 infection” may instead reflect normal cellular responses, leading to widespread false-positive PCR results, as outlined in their paper’s initial summary:

Summary

Background: The COVID-19 pandemic priorities have focused on prevention by detection and response. National governments’ prevention response decisions are
based upon detection statistics from PCR (polymerase chain reaction) tests that are used to define numbers of (i) COVID-19 infected persons, (ii) COVID-19 hospitalisations, and (iii) COVID-19 deaths. These statistics assume a priori that PCR tests are nigh 100% true detectors of COVID-19 infections. Here we will provide an alternative interpretation, along with the compelling evidence, that false positives have distorted to some degree the statistics of the primary outbreaks, and account for almost the whole of the 2nd and subsequent apparent COVID-19 outbreak peaks in various countries.

Methods: We extract from the published literature on PCR-test outcomes graphical data that reveals the evidence for a very large percentage of false positive results. We review the role of exosomes in the immune response to all respiratory viral infections and its effect on PCR tests. We hypothesise that exosomes, triggered by all viral respiratory infections, are largely responsible for positive outcomes from PCR tests for COVID-19. We test our alternative interpretation for consistency with the empirical
epidemiological trends as published by the World Health Organization (WHO). The Scientific Method is used to direct our research efforts.

Findings: We find that PCR testing data for the second and following waves of the COVID-19 pandemic indicate that these waves are mainly artefacts of false-positive results. We find that this interpretation provides a more consistent explanation of the known epidemiology of COVID-19 than the hitherto consensus notion of extremely contagious and rapidly mutating viruses.

Interpretation: The RNA (ribonucleic acid) code detected in PCR tests, previously attributed to SARS-CoV-2, belongs instead to a respiratory-virus-induced immune system response by human cells that liberate exosomes, and that vitiate PCR test results. PCR tests have zero specificity in vivo due to the exosome RNA. PCR tests exhibit excellent specificity in vitro on pure samples of other respiratory viruses. The low success rate of vaccines in preventing COVID-19 is explained by the inexact identification of the SARS-CoV-2 RNA.

This is a damning admission that directly supports what I, along with many other critics, scientists, and independent researchers, have pointed out from the very beginning: without physically isolating and purifying “viral” particles upstream, what has been computationally Frankensteined into a “viral” genome is simply, at best, genetic material originating from normal cellular processes or other biological entities.

The reseachers signalled out that the genetic material most likely comes from “exosomes,” which are considered microscopic, membrane-bound vesicles produced naturally by human cells to transport proteins, lipids, and RNA as part of routine cell-to-cell communication and cellular maintenance. “Exosomes” and so-called “viruses” share the exact same physical traits: they are morphologically, structurally, and biochemically indistinguishable in nearly every meaningful way. In fact, mainstream research readily acknowledges that because they share the exact same size, density, and physical properties, there is no known purification method—whether ultracentrifugation, filtration, or precipitation, or a combination of them all—that can actually separate “exosomes” from “viral” particles.

​When RNA is extracted from an unpurified biological sample, the material being analyzed originates from a complex mixture of cellular components, including extracellular vesicles such as “exosomes.” Without a properly purified reference material, assigning detected genetic sequences to a specific “infectious” entity becomes assumption and interpretation rather than direct observation. The distinction ultimately depends on the biological narrative applied: one particle is classified as a replication-competent intracellular parasite, while the other is described as a vehicle for cellular communication. In truth, they are the exact same extracellular vesicles assigned entirely different stories.

The authors followed up their “exosome” hypothesis by examining the WHO’s memo regarding PCR performance and disease prevalence. They reiterated the same statistical problem discussed earlier: when disease prevalence is low, the accuracy claims made by test manufacturers become completely meaningless. Not only do those technical specifications become irrelevant, but in practice, each and every positive test result becomes a false positive.

​Despite this mathematical reality, organizations like the WHO continued calling for mass testing, which effectively created a perpetual feedback loop that sustained the illusion of endless “pandemics.” The authors went so far as to criticize the foundational assumptions made by the WHO—such as the belief that PCR tests are 100% accurate and reliable in clinical settings—stating that there is no compelling evidence to support them. Furthermore, based on how influenza and other respiratory “viruses” are said to replicate and mutate, the authors argued that the WHO’s outbreak hypothesis is erroneous, even pointing out that their endorsement of masking as an effective containment measure is demonstrably false:

“This WHO publication [1] is a reminder that the disease prevalence alters the predictive value of test results. As disease prevalence decreases, the risk of false positives increases. This means (quote) “that the probability that a person who has a positive result (SARS-CoV-2 detected) is truly infected with SARS-CoV-2 decreases as prevalence decreases, irrespective of the claimed specificity” (underlined by us). The WHO alarm notification has been vindicated by a field-study investigation in the UK [2].

In layman’s terms, technical specifications provided by test producers become irrelevant at low SARS-CoV-2 prevalence. The producers may claim their test is 100% specific to the SARS-CoV-2 virus, but in practice, if COVID-19 prevalence in the population is low or zero – as may well happen seasonally – each and every positive result will be a false positive, reducing the amount of information contained in it to zero [3]. Note, however, that since the start of the pandemic, the WHO has been proclaiming that more extensive testing is necessary [4], which could conceivably generate ever-repeating unreal ‘COVID-19 outbreaks’.”

The WHO interpretations require the following hypothetical assumptions for which there is presently no compelling evidence, thus going against the principle of Occam’s razor:

(i) Transmission by asymptomatic COVID-19 carriers;

(ii) Tests really working and being 100% specific in clinical in vivo practice;

(iii) Vaccines are effective, while vaccinated people are getting ‘infected’.

(iv) Effectiveness of vaccines is determined by PCR tests;

(v) The same virus generates completely different outbreak dynamics in the first and subsequent waves, due to fast
evolution.

The OPS explanations only assume established scientific knowledge that has been conclusively demonstrated:

(Vi) PCR Tests produce false positives both on different respiratory viruses, and on patients having no respiratory viruses at all;

(Vii) Contact PCR testing amplifies the number of tests and false positives;

(Viii) Classic, or nearly Gaussian, dynamics of the first (SARS-CoV-2) wave in 2020 (March-May in Western Europe), and the slower-skewed testing–generated dynamics in the subsequent waves.”

Considering items 2, 9, and 11, SARS-CoV-2 is physically akin to other flu viruses, and its RNA mutates at a similar rate due to RNA reproduction errors in human cells. Therefore, it is unable to produce significantly different variants capable of overcoming the existing immunity much faster than the common cold and other seasonal respiratory viruses, where such major variants come up once in a few years at least. Indeed, dozens of variants of influenza A exist, with immunologically different updates to those major variants still taking many years to appear, which explains large intervals between severe outbreaks of influenza A. Therefore, the explanation provided by the WHO-consensus hypothesis must be erroneous.

Moreover, considering items 6 and 10, any public health measures cannot affect SARS-CoV-2 any differently from the common cold and other seasonal respiratory viruses, for the simple reason that all these viruses are physically
indistinguishable and can only propagate within aqueous
droplets, losing virility once the droplets dry out. Therefore, if a surgical mask can stop one of the viruses because it stops all droplets, it will similarly stop all others. In fact, we have known for more than 100 years that masks do not affect influenza propagation [10]. Therefore, the explanation provided by the WHO is quite demonstrably untrue.”

While the authors still operate under the mainstream assumption that “pathogenic viruses” exist, their analysis exposes the exact same fundamental flaws in the evidence. The RNA sequence attributed to “SARS-CoV-2” was never proven to originate from a novel physical “virus”—it could just as easily belong to host “exosomes” or any number of unisolated cellular contaminants in the sample. Without a purified reference standard, PCR cannot reliably identify a “viral” cause, and at low disease prevalence, the output degrades into 100% false positives. The test results are functionally meaningless, confirming that the perceived “pandemic waves” were manufactured through testing mechanics rather than biological reality.

Based on their analysis, the authors argue that avoiding a perpetual cycle of apparent “COVID-19 outbreaks” and ongoing mass revaccination efforts requires immediately ending mass testing for “SARS-CoV-2” and its “variants,” as well as abandoning all attempts to identify asymptomatic carriers.

​To prove their point, the researchers propose potential solutions, such as retesting patients diagnosed with “SARS-CoV-2” for common seasonal respiratory “viruses” to demonstrate that what is erroneously interpreted as “COVID-19” is simply the result of false-positive PCR tests. Of course, this proposed solution still relies on the flawed assumption that PCR tests for seasonal “viruses” are accurate and that seasonal respiratory illnesses can be reliably distinguished clinically. In reality, this simply perpetuates the circular loop of using unvalidated PCR tests to establish cases in order to evaluate the validity of other PCR test results.

​Regardless of those remaining mainstream assumptions, the researchers conclude that the clinical use of PCR has zero specificity, directly contradicting the claims made by test manufacturers. They attribute this failure to a foundational flaw: the genetic material claimed to belong to “SARS-CoV-2” was constructed entirely through computational methods without first obtaining a purified isolate of the corresponding “viral” particles or physically separating them from other sources of genetic material present within biological samples. As a result, they conclude that the alleged genetic codes for both the original “SARS-CoV-2 virus” and its subsequent “variants” were completely misidentified:

Discussion

Our revised explanation for the various observations of the COVID-19 epidemiology, along with predictions for the development of the COVID-19 pandemic are so far entirely consistent with all known experimental observations as evidenced by published statistical data. We have been unable, as yet, to conduct clinical experiments to confirm the reality of the MDSCV, however, researchers in St. Petersburg have obtained experimental evidence that a large fraction of COVID-19 patients have additional respiratory viruses in their system [11]. To avoid an infinite sequence of pseudo-COVID-19 outbreaks and constant mass revaccinations, mass testing for SARS-CoV-2 and its variants should be immediately discontinued, along with futile attempts to identify asymptomatic carriers, and the respective resources used to provide remedies for chronic patients with other conditions, who were left without medical help by reorienting all medical service towards COVID-19 pandemics.

MDSCV phenomenon may be tested for in the laboratory by infecting human volunteers with known respiratory viruses other than SARS-CoV-2 and using standard PCR tests for SARS-CoV-2 on those who develop symptoms, in a strictly quadruple-blind experimental design. In an approach not requiring volunteers, patients diagnosed with SARS-CoV-2 may be retested for seasonal respiratory viruses, which will be present in most cases producing what is erroneously interpreted as COVID-19 due to false-positive results of PCR tests.

We are finally left with a striking contradiction between the excellent specificity of SARS-CoV-2 tests demonstrated on de facto samples of other respiratory viruses in vitro as per the information provided by the test producers, and the apparently zero specificity of the same tests revealed in vivo in clinical practice, as demonstrated here. To address this contradiction, note that all tests (and vaccines) were produced using the genetic information published by the SARS-CoV-2 discoverers in the appropriate databases.

Consulting the respective seminal publications [12], we find that the respective genetic material had been identified computationally without preparing an isolate of the respective virus particles, and without separating them physically from other carriers of genetic material that may be present in the biological samples [12]. Noting that tests apparently produce false positive results in people carrying some respiratory virus different from SARS-CoV-2, we must conclude that the alleged genetic code of the SARS-CoV-2 virus had been wrongly identified, belonging instead to something generated by human airway epithelial cells challenged with respiratory viruses and containing RNA, for instance, to exosomes, as explained above.

It is no surprise, therefore, that the tests are totally nonspecific in the clinical practice while demonstrating excellent specificity in vitro: samples of other respiratory viruses used for in vitro trials were not contaminated with products of human cells, whereas all biological samples used to identify RNA code of SARS-CoV-2 have been in contact with such cells [12]. Note also that SARS-CoV-2 RNA had been found similar to that of another virus, which casts reasonable doubt on that previous identification.

It appears also that the RNA codes of SARS-CoV-2 variants, very similar to that of the original COVID-19 virus, have also been wrongly identified. Given that the alleged SARS-CoV-2 RNA should be in fact generated by human airway epithelial cells used for virus culturing [12], it is possible to explain high rates of false negative results in COVID-19 patients. RNA induced by the virus in challenged human cells may vary from patient to patient, due to individual genetic differences, making it not recognizable by the test.

Having inferred an erroneous identification of the genetic
material belonging to SARS-CoV-2, we can interpret the low success rates of all the existing vaccines, requiring multiple doses to produce a reasonable immune response. Indeed, the vaccines are based on the genetic material, probably of exosomes, generated in human airway epithelial cells challenged by respiratory viruses, and not on the genetic material of the SARS-CoV-2 virus itself. Immunity generated by such vaccines will suppress the own exosomes, and therefore delay the immune system response in COVID patients. These vaccines may also exacerbate problems in patients with other diseases that induce cell responses akin to that generated by respiratory viruses, probably explaining some of the adverse reactions to vaccination amongst younger recipients.

Ultimately, while the authors stop short of abandoning germ “theory” altogether, their findings, published in a peer-reviewed journal, offer a devastating challenge that validates the foundational critiques raised from the beginning. First, by demonstrating that clinical PCR tests suffer from zero in vivo specificity, the study confirms that the “SARS-CoV-2” genetic target was computationally assembled from unpurified cellular fluids containing indistinguishable host extracellular vesicles—meaning normal cellular responses were re-labeled as a “novel virus.” Second, by applying basic diagnostic probability, the authors show that when disease prevalence is low or absent, technical claims of test specificity become meaningless, causing the positive predictive value to collapse and turning 100% of positive results into false positives. Together, these admissions dismantle the official narrative: without physical isolation and purification upstream to establish a true reference standard, and with diagnostic output degrading into arbitrary noise at low prevalence, the perceived “outbreak waves” were purely artifacts of testing mechanics rather than biological reality.

The Missing “Immune” Response

While the 2024 study is very damning to the mainstream narrative in and of itself, directly reinforcing arguments made by critics of virology, it is not the only recent peer-reviewed study to do so. The October 12, 2025 paper A calibration of nucleic acid (PCR) by antibody (IgG) tests in Germany: the course of SARS-CoV-2 infections estimated delivered another damaging blow. In this study, researchers compared the trajectory of cumulative PCR-positive results with IgG “antibody” prevalence over time to determine how well PCR-defined cases aligned with an independent measure of prior “immune response.” In other words, the researchers wanted to see if positive PCR results correlated with positive “antibody” results, as described in the introduction:

1 Introduction

Public reporting of weekly polymerase chain reaction (PCR) test results provides a time-resolved signal of the detection of viral genetic material in a population, but does not directly quantify cumulative exposure. Here, we ask: To what extent can a summed PCR-positive signal be calibrated to reproduce the observed IgG seroprevalence trajectory (i.e., the IgG-positive signal)? We address this with two complementary, minimal models: (i) a least-squares fit that scales the cumulative weekly PCR-positive fraction to match positive IgG fractions and (ii) a literature-parametrized conversion from counts of positive PCR tests to an estimated number of infected in the population. These approaches are simple by design to maximize transparency and interpretability.

The authors note that PCR became the “gold standard” test to establish “COVID-19” cases. However, as noted by the British Medical Journal (BMJ) in May 2020, “tests need to be evaluated to determine their sensitivity and specificity, ideally by comparison with a ‘gold standard,’” but the lack of a clear-cut “gold-standard” for “COVID-19” testing “makes evaluation of test accuracy challenging.” As researcher and author of Virus Mania Torsten Engelbrecht noted in his excellent article COVID19 PCR Tests are Scientifically Meaningless, “it is downright absurd to take the PCR test itself as part of the gold standard to evaluate the PCR test.” Regardless, this circular problem became the foundation upon which PCR-defined case counts were built.

The researchers also make an important distinction that appears regularly throughout the medical literature: PCR does not directly detect a whole, “infectious virus” or prove the presence of an “active infection.” Instead, it detects fragments of genetic material interpreted as belonging to “SARS-CoV-2.” As noted in the test insert for the CDC’s PCR test, the detection of “viral” RNA “may not indicate the presence of infectious virus or that 2019-nCoV is the causative agent for clinical symptoms.” The insert further acknowledges that the test cannot “rule out diseases caused by other bacterial or viral pathogens.” Despite these severe technical limitations, PCR-positive counts were widely treated as proxies for actual “infections” and became the basis for public health decisions.

The authors emphasize that studying the relationship between PCR results and “antibody” (IgG) data is crucial because of these pervasive false positives. Yet this comparison introduces another fundamental problem: one unvalidated test cannot independently validate another unvalidated test. If PCR results are used to establish “infection,” while IgG results are then used to determine how accurately PCR identified those “infections,” the analysis risks creating another circular reference system. Neither test independently establishes the underlying biological truth. Nevertheless, the authors proceed with the comparison and explicitly highlight two well-documented sources of false-positive PCR results:

1. ​Assay Contamination/Artifacts: The Charité PCR assay—which served as the global template endorsed by the WHO—produced positive results on plain water controls (a fatal flaw also shared by early CDC test kits).
2. ​Bayesian Statistical Fallacy: Under Bayes’ theorem, the rate of false positives increases dramatically as disease prevalence declines due to test specificity falling below 100%.

​The researchers further point out the absurdly high cycle thresholds (Ct) used to execute these PCR tests globally. While Ct values above 30 are widely acknowledged in the literature as “non-infectious,” clinical laboratories routinely ran tests up to 40 cycles—and in some cases, as high as 45 cycles—amplifying background genetic noise into artificial “cases:”

After the emergence of SARS-CoV-2 in late 2019, PCR testing (8) for virus-specific genetic material in nasopharyngeal mucus became the global diagnostic gold standard. It is noteworthy that PCR tests merely detect the presence of fragments of viral genetic material, not necessarily an active infection. Nevertheless, it can be assumed that the detection of viral material at the epithelial–mucosal barrier correlates with a certain likelihood of infection. Therefore, the population-level IgG-positive fraction at any given testing time should be approximately proportional to the cumulative sum of fractions of individuals who tested PCR-positive until 2 weeks before the IgG test. This approximation only holds correctly if each PCR-positive person is tested positive just once during the analyzed period; in other words, PCR-positive cases should closely approximate persons. Indeed, strong quantitative evidence from a prior study by some of the present authors (9, Section 2.4) indicates that multiple testing was not widespread before late summer 2021, i.e., beyond the time frame primarily analyzed here (see Section 2).

Studying the relationship between PCR and IgG results is crucial, since PCR-positive counts were widely interpreted as proxies for actual infections and served as the basis for public health policy decisions. It is therefore important to highlight two known sources of false-positive PCR results. First, a study (10, ) found that the Charité’s PCR assay produced positive results on water controls at cycle threshold (CT) values between 36 and 38. Second, according to Bayes’ theorem, the rate of false positives increases when disease prevalence declines, owing to test specificity below 100%. In addition, individuals whose PCR tests require CT values above 30 are commonly not to be considered infectious (11, 12), whereas in practice, many tests were conducted with CT values up to 40 (13, 14), (15, Suppl.: CT ≤ 37), (16, CT ≤ 38), and even higher (8, CT=45).

In short, a PCR test provides a snapshot of an individual’s current exposure to viral genetic material at the outermost layers of the body. In epidemiological terms, the PCR-positive fraction can be interpreted as a (proportional but not equal) proxy for the normalized incidence of viral infections, specifically SARS-CoV-2 in this case. Contrary to the definition of “incidence” in the German Infection Protection Act § 28a(3) (“Infektionsschutzgesetz”), this fraction does not depend on the absolute number of tests conducted (assuming invariant testing conditions, though selection effects may occur, e.g., by targeting), and is therefore a more robust indicator of infection frequency. In other words, when normalized to the number of tests, the incidence identifies the denominator as the varying number of people tested, rather than a fixed group size (e.g., a district population). Although this adjustment does not address pre-selection biases (e.g., symptom-based testing), such biases affect the representativeness of any test-positive fraction; this issue is discussed below. In contrast, virus-specific IgG tests assess whether a specific virus has previously (within a memory window spanning months to years) infected the individual; vaccination, too, typically induces both IgM and IgG production. Accordingly, the IgG-positive fraction reflects the share of the population that has previously been infected or vaccinated, and thus serves as evidence of collective immune response. Mathematically, the IgG-positive fraction at a given time should be proportional to the accumulated PCR-positive incidence, at least during the first year after the virus’ emergence. This ignores the small error margin due to IgG test sensitivity limitations (17), which range from 80% to 81% (18), 83% to 86% (7), 91% (19), 97.5% (6), and up to 100% (20), and also accounts for “negative” seroconversion events (16, 21, 22), with both factors causing underestimation of true infection rates.

To examine the relationship between PCR-positive and IgG-positive (”antibody”) results, the researchers obtained data from the Akkreditierte Labore in der Medizin (ALM) and analyzed the week-by-week relationship between cumulative PCR-positive fractions and corresponding IgG-positive fractions in Germany from mid-March 2020 through the summer of 2021. Interestingly, the authors noted that not only was this critical data collection abruptly halted by health authorities, but the webpage hosting the dataset was quietly removed from the internet:

2 Materials, methods, and results

We examined the week-resolved relationship between the cumulative sum of the fraction of positive PCR test counts and the corresponding fraction of positive IgG test counts over the specific period from mid-March 2020 until the end of 2021 in Germany. The data were obtained from a webpage (23), where a medical laboratory consortium (Akkreditierte Labore in der Medizin e.V., ALM, Berlin, Germany) reported weekly PCR and IgG test results from German test laboratories, including both absolute numbers and proportions of positive outcomes. The ALM dataset consists of weekly aggregated counts, that is, the demographic information reported was not stratified by, for example, age or sex. It is noteworthy that the online data source (23) is no longer available. Only parts of the IgG dataset are still accessible via (24), primarily in the form of printed tables occasionally included in press briefings, and only up to the final calendar week (#53) of 2020. However, we previously extracted and saved the full dataset as displayed in interactive online graphic panels (23); it is provided in terms of two separate files (PCR and IgG data, respectively) as Supplementary Material (25).

Based on their analysis of the ALM data, the researchers concluded that only approximately 14% of individuals who tested PCR-positive were actually “infected” with “SARS-CoV-2:”

As a result, whether one assumes that the IgG-tested individuals were drawn from the PCR-tested population or that they were broadly population-representative, the outcome remains the same: only approximately 14% of all PCR-positive individuals were actually infected with SARS-CoV-2, according to ALM data. This holds regardless of the intransparency of the pre-selection criteria for those PCR-tested (e.g., preceding an antigen test, contact-traced, or with clinical symptoms) and of those IgG-tested very likely being general practitioners’ patients who enquired about their immune status, yet, evidently being close to population-representative (see Section 3.1).

More strikingly, the researchers argued that even this estimate may have overstated the proportion of “true infections.” They suggested that, after accounting for potential biases in the data, the actual figure may have been as low as 11%—meaning that only about one in nine PCR-positive individuals represented a “true infection” according to their model.

In summary, any pre-selection bias in the ALM-observed IgG-positive fraction would have being tending to overestimate the proportion of truly infected individuals in the German population. Consequently, if the proportion of infected had in fact been lower than that observed by the ALM, then the PPCR value estimated from the fit by Equation 1 would likewise have to be even lower than 0.14. Accordingly, a more conservative interpretation of our results suggests that as few as one in eight or even in nine PCR-positive individuals, i.e., approximately 11% (PPCR<0.105), may have actually been infected, rather than one in seven (14%, PPCR=0.14).

Driving a dagger directly into the heart of the Testing Pandemic, the researchers stated flatly that a positive PCR result alone cannot confirm an “infection” at the individual level. They outlined three confounding factors that systematically corrupt the diagnostic output:

1. Time-varying and non-standardized pre-selection criteria for testing
2. Non-uniform CT thresholds applied by laboratories in PCR analysis
3. Varying detection methods and thresholds in IgG testing

Naturally, a PCR-positive test alone can by no means confirm infection at the individual level. The fitted proportionality factor PPCR=1/7.15≈0.14 indicates that only a minority of PCR-positive individuals were actually infected. This factor, PPCR, is in itself the net result of multiple multiplicative influences—most notably: (i) time-varying and non-standardized pre-selection criteria for testing (e.g., symptomatic screening), (ii) non-uniform CT thresholds applied by laboratories in PCR analysis, and (iii) varying detection methods and thresholds in IgG testing (e.g., reagent concentrations, optical density cut-offs). All of these effects—beyond pre-selection—are generally subsumed under the two core parameters of diagnostic testing: sensitivity and specificity [see summary in Watson et al. (37)]. In essence, PPCR reflects the net probability that a person will become serologically IgG-positive (i.e., has been infected) if SARS-CoV-2 genetic material is detectable by PCR at the epithelial–mucosal barrier. This probability is estimated to be approximately 14% (CI: 13.5%–14.6%), with a conservative lower bound of 10.5% if ALM IgG data are assumed to overestimate the population-representative level.

The test performance parameter most relevant to our findings is the specificity of PCR mass testing in Germany during 2020 and 2021. Regardless of PCR sensitivity (which we may, for argument’s sake, assume to be 100%), the combination of observed parameters allows for an estimation of specificity. The mean weekly PCR-positive fraction in Germany between cw11(2020) and cw21(2021), i.e., the ALM IgG testing period, was approximately 7%. Meanwhile, the fitted PPCR=1/7.15 implies that only approximately 1% of those tested per week were actually infected. Assuming 1% of tested individuals were true positives, a specificity of 94% explains the remaining 6% of PCR-positive results as false positives among the 99% who were not infected. This estimate is in excellent agreement with direct assessments of PCR specificity in the literature (38, ).

In summary, our finding that PPCR=1/7.15 is entirely consistent with both the observed low PCR-positive rates and an overall PCR specificity of 94% in Germany. This interpretation provides a coherent picture of the relationship between infection status and test positivity during mass testing.

Upon completing their analysis, the researchers concluded that only about 14%—and potentially as few as 10%—of individuals identified as “SARS-CoV-2-positive” by PCR had actually been “infected,” as indicated by detectable IgG “antibodies.” They criticized the German public health authorities (such as the Robert Koch Institute, RKI) for effectively burying and failing to acknowledge or act upon the large-scale IgG serology data, arguing that it could have provided a more reliable measure of population exposure. Instead, officials continued relying on weekly PCR-positive case counts—the so-called “7-day incidence”—as the primary indicator of the “pandemic.” Because raw PCR case counts are directly determined by the sheer volume of tests performed, the authors concluded that the metric was a scientifically meaningless, artificially manufactured figure used to justify sweeping public health restrictions:

4 Summary and conclusion

The principal finding from our analysis of ALM data on both nucleic acid amplification (PCR from mucosal swabs) and IgG antibody (serological) testing for SARS-CoV-2 in Germany between mid-March 2020 and summer 2021 is this: only 14%—and possibly even fewer, down to 10%—of individuals identified as SARS-CoV-2-positive via PCR testing were actually infected, as evidenced by detectable IgG antibodies.

Our conclusion is twofold. First, the IgG testing conducted by ALM laboratories was commissioned by the RKI, itself subordinate to the BMG. Nonetheless, data acquisition evidently ceased after cw21(2021) or, at the very least, public reporting of the data on the ALM website (23) stopped. The IgG results observed and published by ALM have not been acknowledged or communicated by the RKI to date, despite the fact that transparency in reporting such data should be mandatory, both scientifically and in terms of public accountability. Second, the proportion of the German population with a detectable immune response to SARS-CoV-2 was already substantial by the end of 2020. Approximately one-quarter of the population carried IgG antibodies at that point, following a trajectory determined almost exclusively by natural infections. By the end of 2021, practically the whole German population could be considered IgG positive.

Evidently, from March 2020 onward, a national German serological antibody cohort study was conducted—initiated and overseen by the RKI and BMG—though it was never publicly communicated as such, nor has it been adequately analyzed to this day. In consequence, German authorities had timely and reliable access to data tracking the course of IgG seropositivity—data that were, in fact, close to being population-representative. These data could have served as an objective metric for monitoring the proclaimed “epidemic situation of national significance” (“Epidemische Lage Nationaler Tragweite”).

Instead, this evidence-based and representative serological signal was disregarded in favor of relying on the weekly absolute number of positive PCR tests—the so-called “7-day incidence” (“Sieben-Tage-Inzidenz”). Unequivocally, this definition of incidence yields a scientifically meaningless figure in the context of infection dynamics, as it depends entirely on the arbitrary (or imposed) number of PCR tests performed. It is therefore not an objective indicator of epidemiological reality, but an administratively imposed figure—more reflective of political will than scientific rigor. Yet, incomprehensibly, this 7-day incidence metric was even incorporated into the German Infection Protection Act (“Infektionsschutzgesetz”) as the quantitative foundation for imposing highly restrictive public health measures. The methodological shortcomings and institutional processes that enabled its elevation to policy status demand critical re-evaluation—not only to prevent similar errors in the future, but to restore trust in evidence-based public health governance.

What this paper ultimately demonstrates—beyond confirming the crucial role of disease prevalence in interpreting PCR results—is that while PCR was used to declare “infections” based on positive test outputs, the corresponding IgG “antibody” data failed to track those purported “infections.” In mainstream diagnostics, PCR is alleged to detect an active “infection,” whereas “antibody” tests are claimed to reflect a systemic “immune response” following that “infection.” Yet, when the two were compared over more than a year of testing in Germany, their trajectories diverged dramatically. Based on this discrepancy, the researchers concluded that only about 14%—and potentially as few as 10%—of PCR-positive individuals had actually been “infected,” as indicated by detectable IgG “antibodies.” In other words, by their analysis, roughly nine out of ten PCR-positive results represented false positives, suggesting that the high case counts used to drive public health policy were largely artifacts of PCR testing rather than reflections of widespread “infection.”

But this finding must also be placed in its proper methodological context: an unvalidated test cannot, by itself, validate another unvalidated test. The IgG results therefore do not independently establish which PCR-positive individuals were truly “infected.” Rather, the comparison exposes a deeper problem: neither test provides an independently established reference standard capable of resolving the discrepancy. The PCR results cannot validate the IgG results, and the IgG results cannot, by themselves, validate the PCR results.

Positively False

When you step back and look at the picture constructed by the mainstream literature itself, the collapse of the PCR paradigm is complete.

​It began with a fundamental violation of the scientific method: deploying a diagnostic test worldwide without a physically purified and isolated “viral” reference standard to calibrate it. Without that physical baseline, there was never any way to distinguish a true positive from a false positive. A self-referential mathematical trap was constructed on top of an unvalidated foundation.

​As early as 2020, agencies like the CDC, WHO, and ASM admitted the basic diagnostic principle that test accuracy depends entirely on knowing disease prevalence. Yet, in practice, health authorities used raw PCR case counts to establish the very prevalence needed to validate the PCR tests. This created the circular confirmation loop discussed throughout this article where the test generated the cases, the cases defined the prevalence, and the prevalence was used to justify the test. When mass testing was pushed onto healthy, asymptomatic populations at sky-high cycle thresholds, the diagnostic output degraded into pure statistical noise.

This problem was compounded by the fact that “COVID-19” lacked a unique clinical signature. The signs and symptoms used to define the disease overlapped with numerous other respiratory conditions, meaning that clinical presentation alone could not reliably distinguish “COVID-19” from influenza, the common cold, or other illnesses. PCR therefore became not merely a confirmation tool, but the primary mechanism used to define the disease itself. In effect, the same test that generated the case numbers also became the basis for determining the existence and prevalence of the condition it was intended to detect.

The two peer-reviewed studies examined here further reinforce these concerns. The first study challenged the assumption that PCR signals necessarily represented detection of a unique “infectious” agent, proposing that biological material from normal cellular processes, such as extracellular vesicles, could contribute to positive results interpreted as evidence of “SARS-CoV-2.” While the authors maintained mainstream assumptions about respiratory “viruses,” their analysis highlighted a fundamental problem: without a properly purified and validated reference material, the origin and meaning of detected genetic sequences remain dependent upon assumptions and interpretation.

The second study approached the issue from a different angle by comparing PCR-positive trends with IgG seroprevalence data in Germany. Rather than examining the source of the detected genetic material, the researchers asked a simpler epidemiological question: did the number of PCR-defined cases correspond with independent evidence of prior “immune” exposure? Their analysis found a dramatic mismatch, estimating that only a minority of PCR-positive individuals corresponded with detectable IgG “antibodies” consistent with the prior “infection” narrative.

Together, these findings expose the same underlying weakness from two different directions. One study questions whether the PCR signal was correctly attributed to a unique “infectious” entity in the first place. The other demonstrates that PCR-defined case counts did not correspond with the expected biological evidence of widespread “infection.” Both point toward the same conclusion: the interpretation of PCR results depended upon assumptions that were never adequately validated.

Mainstream commentary often frames these failures as a high rate of “false-positive” results. But as we have seen, the term “false positive” is far too generous. A false positive implies a minor technical error in an otherwise functioning system that is capable of distinguishing true positives from false ones. When a test lacks a physical gold standard, targets unpurified host cellular RNA, and operates within a circular logic loop where test volume dictates case numbers, it is not occasionally making mistakes. It is generating arbitrary output by design.

The evidence presented here comes primarily from the mainstream scientific literature itself, including publications whose authors continue to accept the existence of “pathogenic respiratory viruses.” Yet, when their findings are examined collectively and followed to their logical conclusion, they expose fundamental problems with the assumptions underlying PCR-based diagnosis. The central contradictions do not arise from outside the prevailing paradigm—they emerge from within its own published evidence.

​Once this is understood, it becomes clear that the Testing Pandemic was never established on a sound diagnostic foundation. It was an administrative phenomenon built around an unvalidated, over-amplified, and non-specific laboratory tool. When positive test results are allowed to define cases, prevalence, and outbreaks simultaneously, the same framework can generate the appearance of future “pandemics” through testing mechanics alone. Without an independently validated reference standard and reliable clinical correlation, there is no objective mechanism for determining where a true positive ends and a false positive begins. The cycle can therefore repeat indefinitely, producing new waves, new variants, and new “cases” whenever enough testing is performed.

Every case count, wave, and policy measure built upon that diagnostic foundation inherits the same fundamental uncertainty. The problem was never simply that PCR produced too many false positives. The deeper problem is that the system never established an independent basis for determining what a true positive actually was.

That is why the appropriate conclusion is not merely that PCR was occasionally wrong.

It is that the entire system was positively false.