The supposed recent "spike" in COVID-19 cases is total bs.

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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Wed Dec 02, 2020 5:32 pm

The Penn State football team has experienced roughly 100% false positive rates from what I can determine from this article.

https://www.post-gazette.com/sports/psu ... 2011180164

“One of the issues that we continue to have issues with is false positives,” Penn State coach James Franklin told reporters Tuesday. “We’ve had 39 false positives, where that means 39 people missing practice.”

“We’re at, I think, a higher rate than anybody in the conference and trying to find out why, because every time we have one of those guys, they miss a practice,” Franklin said. “We’ve had a few kids, as well as staff members, that have gotten false positives multiple days in a row.”

Franklin noted after Wednesday’s practice that the false positive number had risen to 43, and that included defensive coordinator Brent Pry and cornerbacks/assistant head coach Terry Smith, who he said missed time this week because of false positives.

How does that happen? According to the Big Ten’s website, the conference requires “student-athletes, coaches, trainers and other individuals that are on the field for all practices and games to undergo daily antigen testing. Test results must be completed and recorded before each practice or game. Student-athletes who test positive for the coronavirus through point of contact (POC) daily testing would require a polymerase chain reaction (PCR) test to confirm the result of the POC test.”
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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Wed Dec 02, 2020 6:08 pm

This scientific article argues convincingly that even for the supposed "gold standard" PCR tests. false positives are a very serious problem.

https://www.medrxiv.org/content/10.1101 ... l.pdf+html

Contrary to the practice during previous epidemics, with COVID-19 health authorities have treated a single positive result from a PCR-based test as confirmation of infection, irrespective of signs, symptoms and exposure. This is based on a widespread belief that positive results in these tests are highly reliable. However, evidence from external quality assessments and real-world data indicate enough a high enough false positive rate to make positive results highly unreliable over a broad range of scenarios. This has clinical and case management implications, and affects an array of epidemiological statistics, including the asymptomatic ratio, prevalence, and hospitalization and death rates, as well as epidemiologic models. Steps should be taken to raise awareness of false positives and reduce their frequency. The most important immediate action is to check positive results with additional tests, at least when prevalence is low.

Key messages

*The high specificities (usually 100%) reported in PCR-based tests for SARS-CoV-2 infection do not represent the real-world use of these tests, where contamination and human error produce significant rates of false positives.

*Widespread lack of awareness of the real-world false positive rates affects an array of clinical, case management and health policy decisions. Similarly, health authorities' guidance on interpreting test results is often wrong.

*Steps should be taken immediately to reduce the frequency and impacts of false positive results, including checking positive results with additional tests at least when prevalence is low.

Most tests for active SARS-CoV-2 infection use the polymerase chain reaction (PCR) to amplify and detect diagnostic sequences within the virus' RNA. According to leading health authorities, while negative results from these tests are frequently wrong, positive results are highly reliable. Accordingly, the World Health Organization (WHO) and most government health ministries diagnose SARS-CoV-2 infection on the basis of a single positive PCR result, even in asymptomatic persons without any history of exposure. For example, WHO defines a confirmed case as a person with a positive test result, "irrespective of clinical signs and symptoms."

This is a departure from historical practice. In previous epidemics case definitions required individuals to be symptomatic, and health authorities voiced concerns that false positive results from PCR-based tests could harm both the individuals tested and the ability of agencies to monitor outbreaks. National and international health agencies adopted measures to limit the occurrence of false positives, recommending that PCR-based testing be limited to individuals with a high probability of infection (those with symptoms and/or significant exposure), and often requiring confirmation of positive results by a second, independent test (Box 1). These warnings and requirements are absent from the same agencies' current guidance on SARS-CoV-2 testing.


In this Analysis we argue that basing diagnoses on unrestricted PCR-based testing freed from clinical context has created serious problems. PCR-based tests produce a significant number of false positive results, making positive results unreliable over a broad range of real-world scenarios. Consequently, the frequent assertion that positive test results for SARS-CoV-2 are more reliable than negative results is wrong most of the time, and the widespread reliance on a single positive PCR result as a sufficient basis for diagnosis has been a mistake. The general misunderstanding of the rate of false positives in SARS-CoV-2 testing affects clinical and case management decisions, and through flawed interpretations of test statistics, has affected policy decisions. As an immediate, minimum step we recommend checking positive PCR results for asymptomatic individuals with a second independent test; over the longer term, we should work on eliminating the underlying causes of false positives.

Then:

SARS-CoV-1

US CDC: "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...In addition, any positive specimen should be retested in a reference laboratory to confirm that the specimen is positive. To be confident that a positive PCR specimen indicates that the patient is infected with SARS-CoV, a second specimen should also be confirmed positive."

WHO: "[R]equirements for the laboratory diagnosis of SARS...almost always involves two or more different tests or the same assay on two or more occasions during the course of the illness or from different clinical sites...A single test result is insufficient for the definitive diagnosis of SARS-CoV infection."

H1N1 Influenza Virus

US CDC: Case confirmation requires presentation with an influenza-like illness in addition to a single positive PCR test.

MERS-CoV

US CDC: Requirements for testing include both specific clinical features and epidemiologic risk,15 and positive results must be confirmed by the CDC.

WHO: Testing should be limited to persons with specified symptoms and, in most cases, elevated risk of exposure.

Ebola Virus

US CDC: "CDC recommends that Ebola testing be conducted only for persons who...[have] both consistent signs or symptoms and risk factors...Any presumptive positive Ebola test result must be confirmed at the CDC...CDC considers a single diagnostic test...insufficient for public health decisionmaking."

WHO: Case confirmation requires specific clinical signs in addition to a single positive PCR test.

Zika Virus

US CDC: Testing is recommended only for pregnant women with symptoms and recent exposure, or asymptomatic pregnant women with ongoing exposure. "because of the potential for falsepositive...results, updated recommendations include [PCR] testing of both serum and urine and concurrent Zika virus IgM antibody testing to confirm the diagnosis...with more than one test."
WHO: Testing is recommended only for symptomatic patients.

Now:

SARS-CoV-2

Except for validation of a laboratory's first few results, we found no requirement or recommendation for a second confirmatory test in guidance documents from the World Health Organization, the US Centers for Disease Control and Prevention, the European Centre for Disease Prevention and Control, Public Health England, the Public Health Agency of Canada, the Pan American Health Organization, or South Korea's Centers for Disease Control and Prevention; instead these entities require only a single positive PCR result to confirm infection in symptomatic or asymptomatic persons. The Chinese Centers for Disease Control and Prevention requires clinical manifestations and usually exposure history in addition to a positive PCR result to confirm a case. On May 27 the Norwegian Institute of Public Health amended its guidance to recommend confirmatory tests of positive results in persons who are both asymptomatic and without exposure history.

In most regions testing was initially restricted to persons with specified clinical signs and symptoms and exposure history, but as more tests became available many authorities allowed broader use of PCR-based tests, including testing of individuals with no symptoms or known exposure risk.


False positives

The accuracy of a diagnostic test is measured by sensitivity, which is the proportion of infected individuals that test positive, and specificity, the proportion of uninfected individuals that test negative. Although SARS-CoV-2 PCR assays are widely reported to have 100% specificity that is, a false positive rate of 0%—this refers only to the tests' lack of reaction with substances other than SARS-CoV-2 RNA (analytical specificity), and not to the potential for incorrect results in real-world testing (clinical specificity) where contamination and human error can generate false positives during sample collection, transport and analysis.

A study of 365 laboratories in 36 countries reported 11 positive results for 1,529 negative samples, yielding a false positive rate of 0.7%. These results are generally consistent with data from 43 external quality assessments of similar PCR assays of other RNA viruses conducted in 2004-2019. Out of 10,538 negative samples, 336 (3.2%) were reported as positive. The median false positive rate was 2.3%, and the interquartile range was 0.8-4.0% (Table 1).

When prevalence is low, there are many more uninfected than infected people, so even a low false positive rate can have a larger effect than a high false negative rate.

Figure 1 shows that even a false positive rate of 0.3% (the lowest rate from studies in real-world settings) can greatly reduce the reliability of test results. At that rate, in countries with a low test positivity rate, overly broad testing has produced results that are too unreliable to be useful (toward the right side of panel A, which shows measures of reliability calculated from countries' cumulative test data). Reliability measures calculated from daily test data contrast the time course in Italy (in Panel B), which suffered a catastrophic outbreak, with that in South Korea (Panel C), which avoided one. These calculations show that in South Korea after April 20th most of the positive test results in asymptomatic individuals could have been false positives, even as the country continued to conduct over 6,000 tests a day.

The reliability of positive results falls to near zero when the test positivity rate approaches the false positive rate. However, even with positivities up to ten times the false positive rate, a significant proportion of positive results will be false. For example, with a false positive rate of 0.3% and a test positivity rate of 1% nearly 1 in 3 positive results will be false, and with a positivity rate of 3% nearly 1 in 10 will be false. Most of these false-positive individuals would likely be asymptomatic, which could at least partially explain the reports of large numbers of asymptomatic carriers of SARS-CoV-2.

Public health authorities often state that positive results from SARS-CoV-2 tests are more trustworthy than negative results. However, over a wide range of likely scenarios, the opposite is true: for example, in figure 1 wherever the blue columns (positive predictive values) are lower than the orange columns (negative predictive values), positive results are more likely to be wrong than are negative results. This is because the false positive rate affects samples from uninfected people, while the false negative rate affects samples from people that are infected.

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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Wed Dec 02, 2020 6:14 pm

The Brookings Institute on the huge pitfalls of using crappy antigen tests

https://www.brookings.edu/blog/usc-broo ... d-why-not/

Unfortunately, the proponents of high-frequency, lower-sensitivity testing rarely consider the consequences of false-positive results, whether narrowly on the operation of clinical laboratories or more broadly on clinical practice and public health. We explore the inevitable results of high-frequency, lower-sensitivity testing and explain why implementing such an approach would result in bad public policy. ...

Probability that Patients have the Disease

Beyond the impact of testing on behavior, it is important to distinguish diagnostic testing of persons with a reasonable index of suspicion for COVID-19 from screening testing of low-prevalence populations. The most relevant difference is not necessarily in the ability to detect positive cases (sensitivity), negatives cases (specificity), or any other analytical parameter of the assay. Rather, the key point is the effect of pre-test probability – the prevalence of COVID-19 in the target population – on the proportion of erroneously positive test results. As we demonstrate graphically (Figure 1), the lower the prevalence, the higher the rate of false positives; the grey box represents target prevalence in outbreak suppression efforts.

[imghttps://i1.wp.com/www.brookings.edu/wp-content/uploads/2020/10/figure1_window.jpg[/img]

For a population with a given disease prevalence, the sensitivity and specificity of an assay crucially affect the proportion of false positives and false negatives: the positive predictive value (PPV) and negative predictive value (NPV). We model how PPV (Figure 1) and NPV (Figure 2) change with different sensitivity and specificities and over a range of COVID-19 prevalence from 0.1% to 10%. The sensitivities selected for our model (>95%) are comparable to PCR testing for SARS-CoV-2 and possibly overly optimistic. Rapid tests have much lower sensitivity, represented in our model as 80% sensitivity. Sensitivity has little impact on false positive rates (Figure 1). The sensitivities in our model (³ 95%) are representative of (or better than) most gold-standard PCR assays. Specificities of rapid assays are similar to the lowest in our model (98.5%), if not worse. The take home point is that in low-prevalence populations, even using assays with outstanding analytical performance, half or more of all positive results will be erroneous (Figure 1). By comparison, false negative results are relatively rare – especially in the low-prevalence setting – even with insensitive (rapid) tests (Figure 2).

An important real-world example comes from the <1% prevalence of SARS-CoV-2 among asymptomatic patients without known COVID-19 exposures admitted to our large, academic hospital, despite Seattle having been an early US epicenter of the outbreak. If we used an assay with sensitivity and specificity both of 99.5% to detect SARS-CoV-2 infection in these patients waiting for a hospital bed in the Emergency Room (assuming prevalence of 1%), we would expect ~1/3 of the positive results to be false! By comparison, if we used the exact same assay for our patients with respiratory symptoms (cumulative positivity rate of ~5%), we expect less than 10% of positive results to be false (Figure 1). Statisticians will recognize this difference as Bayes’ Theorem in action. In Laboratory Medicine we call this Pre-Test Probability.

Adverse Consequences of False Positives

False-positive SARS-CoV-2 results harm individuals, strain limited laboratory and public health resources, and risk long-range harm by undermining confidence in clinical and public health efforts. We have seen false positive SARS-CoV-2 test results delay life-saving surgeries. We also know first-hand how confirmatory testing and investigation of unexpectedly positive results strain the laboratory, consuming scarce reagents, adding to the workload of overtaxed lab staff/health care providers, and delaying turnaround time for test results. Deploying assays en masse that would yield so many falsely positive results raises an important question: do all of the positives need confirmation by gold-standard PCR assays? The potential need for confirmatory testing risks markedly increasing the strain on already stressed supply chains upon which clinical laboratories depend. Similarly, a high proportion of false positive results will substantially complicate (if not overwhelm) contact tracing efforts. Another unexplored question is how would a high false positive rate interact with policies around reopening schools or other “normal” socioeconomic activity?

False-positive results may have another, more insidious, longer term consequence: erosion of trust in diagnostic testing. Imagine the public reaction to national headlines describing “tens of thousands of false positive results.” Given that the United States has struggled with widespread adoption of masks, disinformation, and conspiracy theories, we question the ability of doctors to satisfy public concerns by explaining conditional probability and shudder to imagine the sociopolitical consequences of widespread “phony” test results.

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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Wed Dec 02, 2020 6:25 pm

Again, this article is originally from August, and it is sounding the alarm on the false positive rates of the supposed "gold standard" PCR tests. But its suggestion that the solution lies in far less accurate but far more rapid antigen tests is highly suspect to say the least.

https://www.nytimes.com/2020/08/29/heal ... sting.html

Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.

The usual diagnostic tests may simply be too sensitive and too slow to contain the spread of the virus.

Some of the nation’s leading public health experts are raising a new concern in the endless debate over coronavirus testing in the United States: The standard tests are diagnosing huge numbers of people who may be carrying relatively insignificant amounts of the virus.

Most of these people are not likely to be contagious, and identifying them may contribute to bottlenecks that prevent those who are contagious from being found in time. But researchers say the solution is not to test less, or to skip testing people without symptoms, as recently suggested by the Centers for Disease Control and Prevention.

Instead, new data underscore the need for more widespread use of rapid tests, even if they are less sensitive.

“The decision not to test asymptomatic people is just really backward,” said Dr. Michael Mina, an epidemiologist at the Harvard T.H. Chan School of Public Health, referring to the C.D.C. recommendation.

“In fact, we should be ramping up testing of all different people,” he said, “but we have to do it through whole different mechanisms.”

In what may be a step in this direction, the Trump administration announced on Thursday that it would purchase 150 million rapid tests.

The most widely used diagnostic test for the new coronavirus, called a PCR test, provides a simple yes-no answer to the question of whether a patient is infected.

But similar PCR tests for other viruses do offer some sense of how contagious an infected patient may be: The results may include a rough estimate of the amount of virus in the patient’s body.

“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 PCR test amplifies genetic matter from the virus in cycles; the fewer cycles required, the greater the amount of virus, or viral load, in the sample. The greater the viral load, the more likely the patient is to be contagious.

This number of amplification cycles needed to find the virus, called the cycle threshold, is never included in the results sent to doctors and coronavirus patients, although it could tell them how infectious the patients are.


In three sets of testing data that include cycle thresholds, compiled by officials in Massachusetts, New York and Nevada, up to 90 percent of people testing positive carried barely any virus, a review by The Times found.

On Thursday, the United States recorded 45,604 new coronavirus cases, according to a database maintained by The Times. If the rates of contagiousness in Massachusetts and New York were to apply nationwide, then perhaps only 4,500 of those people may actually need to isolate and submit to contact tracing.

One solution would be to adjust the cycle threshold used now to decide that a patient is infected. Most tests set the limit at 40, a few at 37. This means that you are positive for the coronavirus 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. Mina said.

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. Those changes would mean the amount of genetic material in a patient’s sample would have to be 100-fold to 1,000-fold that of the current standard for the test to return a positive result — at least, one worth acting on.

The Food and Drug Administration said in an emailed statement that it does not specify the cycle threshold ranges used to determine who is positive, and that “commercial manufacturers and laboratories set their own.”

The Centers for Disease Control and Prevention said it is examining the use of cycle threshold measures “for policy decisions.” The agency said it would need to collaborate with the F.D.A. and with device manufacturers to ensure the measures “can be used properly and with assurance that we know what they mean.”

The C.D.C.’s own calculations suggest that it is extremely difficult to detect any live virus in a sample above a threshold of 33 cycles. Officials at some state labs said the C.D.C. had not asked them to note threshold values or to share them with contact-tracing organizations.

For example, North Carolina’s state lab uses the Thermo Fisher coronavirus test, which automatically classifies results based on a cutoff of 37 cycles. A spokeswoman for the lab said testers did not have access to the precise numbers. This amounts to an enormous missed opportunity to learn more about the disease, some experts said.

“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.

Officials at the Wadsworth Center, New York’s state lab, have access to C.T. values from tests they have processed, and analyzed their numbers at The Times’s request. In July, the lab identified 872 positive tests, based on a threshold of 40 cycles.

With a cutoff of 35, about 43 percent of those tests would no longer qualify as positive. About 63 percent would no longer be judged positive if the cycles were limited to 30.


In Massachusetts, from 85 to 90 percent of people who tested positive in July with a cycle threshold of 40 would have been deemed negative if the threshold were 30 cycles, Dr. Mina said. “I would say that none of those people should be contact-traced, not one,” he said.

Other experts informed of these numbers were stunned.

“I’m really shocked that it could be that high — the proportion of people with high C.T. value results,” said Dr. Ashish Jha, director of the Harvard Global Health Institute. “Boy, does it really change the way we need to be thinking about testing.”

Dr. Jha said he had thought of the PCR test as a problem because it cannot scale to the volume, frequency or speed of tests needed. “But what I am realizing is that a really substantial part of the problem is that we’re not even testing the people who we need to be testing,” he said.

The number of people with positive results who aren’t infectious is particularly concerning, said Scott Becker, executive director of the Association of Public Health Laboratories. “That worries me a lot, just because it’s so high,” he said, adding that the organization intended to meet with Dr. Mina to discuss the issue.

The F.D.A. noted that people may have a low viral load when they are newly infected. A test with less sensitivity would miss these infections.

But that problem is easily solved, Dr. Mina said: “Test them again, six hours later or 15 hours later or whatever,” he said. A rapid test would find these patients quickly, even if it were less sensitive, because their viral loads would quickly rise.

PCR tests still have a role, he and other experts said. For example, their sensitivity is an asset when identifying newly infected people to enroll in clinical trials of drugs.

But with 20 percent or more of people testing positive for the virus in some parts of the country, Dr. Mina and other researchers are questioning the use of PCR tests as a frontline diagnostic tool.

People infected with the virus are most infectious from a day or two before symptoms appear till about five days after. But at the current testing rates, “you’re not going to be doing it frequently enough to have any chance of really capturing somebody in that window,” Dr. Mina added.

Highly sensitive PCR tests seemed like the best option for tracking the coronavirus at the start of the pandemic. But for the outbreaks raging now, he said, what’s needed are coronavirus tests that are fast, cheap and abundant enough to frequently test everyone who needs it — even if the tests are less sensitive.

“It might not catch every last one of the transmitting people, but it sure will catch the most transmissible people, including the superspreaders,” Dr. Mina said. “That alone would drive epidemics practically to zero.”
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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Wed Dec 02, 2020 6:34 pm

Another scientific article about the concerning false positive rates of even the supposed "gold standard" PCR tests.

https://www.aacc.org/science-and-resear ... -polymeras

False Positive Results in Real-time Reverse Transcription-Polymerase Chain Reaction (rRT-PCR) for SARS-CoV-2?
Author: Stanley S. Levinson, Ph.D., DABCC // Date: OCT.13.2020

Topics: Lab Management, Infectious Diseases/Microbiology, Polymerase Chain Reaction (PCR)

There are multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergency use authorization (EUA) tests among clinical laboratories and a large amount of cross-assay variation between different assays. Moreover, these assays were rapidly developed, minimally standardized and there is no well recognized external quality assessment program (EQA). As a result, good estimates of the diagnostic sensitivity and specificity are not available. There has been more focus on the diagnostic sensitivity and it is known that sensitivity is poor if the test is performed too early before detectable RNA is shed and that the viral RNA may be detectable for a period of time after active infection although the virus is no longer viable or infective. Therefore, the CDC does not recommend retesting after recovery, but CDC suggests symptomless persons who are immunologically normal are no longer considered infectious about 10 days after symptom onset.

There is less information about diagnostic specificity (false positives). Among others, false positives will depend on the length of the DNA probes, how many and which genes are measured and technical errors. The DNA probes used in the CDC rRT-PCR test kits for SARS-CoV-2 assay are only about 25 bases long which does not meet the FDA recommendation for nucleic acid-based molecular diagnostics for viral disease infections where 100 contiguous bases is desirable (1). Various methods use different genes and different probes that may not be equivalent. There is a 100-fold difference in limit of detection (LoD) between some assays (2). Technical error, especially due to contamination may cause false positives. Seventy-seven professional baseball major league players initially tested positive in one lab but negative elsewhere (3) in what was deemed Lab error. Except that they had multiple sources for testing, they might have been classified as asymptomatic. We don’t know how many other persons were classified in error from this incident.

Originally, PCR was followed by a second step where a separation technique such as a blotting method was used to confirm that the amplified substance was correct. rRT-PCR is usually not followed by a second step. rRT-PCR is usually applied for diagnostic purposes, not for screening. For acute viral infections, after symptoms appear, a rRT-PCR test battery may be performed. In diagnostic testing, symptoms or high-risk behavior cause an increase in prevalence because those with certain symptoms are classified into characterized groups and false positives are few.

Diagnostic applications are usually applied for chronic viral infections such as HCV, HIV and chronic HBV where symptoms or high-risk behavior initiates testing, although there are now screening recommendations for HCV. Still, in all these chronic diseases antibody concentrations are high and serology usually precedes rRT-PCR, so that false positives are rare. At present prevalence, COVID-19 testing is primarily widespread screening without confirmation.

For SARS-CoV-2 rRT-PCR, cycle threshold (Ct) of 24 or less has been shown to be highly predictable for identifying active COVID-19 cases (4), but since LoD of various methods drastically differ it is unclear which methods this applies to. Generally, methods do not amplify more than 40 cycles, but some systems go beyond 40 Ct. It seems likely that short probes in such systems could lead to amplification errors. Although there is no wide spread EQA proficiency programs for SARS-CoV-2, there is one report (5), of EQA in clinical laboratories for other RNA virus. The authors compiled 43 EQAs of rRT-PCR assays, conducted between 2004-2019. Each EQA involved between three and 174 laboratories, which together provided results for 4,113 blind panels containing 10,538 negative samples. 336 of the 10,538 negative samples (3.2%) were reported as positive. The authors defined the lowest percentage of the interquartile range which was 0.8% as a conservative estimate of the false positive rate. In another report, Sin Hang Lee found that 3 of 10 positive proficiency samples in the State of Connecticut were negative containing no SARS-CoV-2 RNA by a confirmatory assay (1). The Foundation for Innovative New Diagnostics (FIND) examined 22 rRT-SARS-CoV-2 diagnostic tests (6) and found diagnostic specificities ranging between 100% and 96% for 100 specimens assayed by each test. Although the great majority showed 100% specificity, given the small number assayed, the lower 95% confidence limit which was 95% for almost all assays would seem to be a better estimate (possible 5% error). Moreover, these were tested under controlled conditions, not at all similar to high output clinical laboratories running thousands of tests.

The Reverend Thomas Bayes (1701-1761) recognized a kind of statistic that predicts the posterior probability from the prior probability. For testing, this means the post test probability can be derived from the pretest probability if the prevalence is known. This sounds complicated but actually, Bayesian statistics are simple compared to classical frequentist statistics since one does not have to apply a null hypothesis, nor interpret p-values or effect-size and the results are obtained from simple mathematics. If, as discussed above (5), a 0.8% false positive rate is correct, at a six percent positive rate that some States claim, then there would be: 100 x 0.06 = 6 positives/100 tests. But if 0.8% are false positives, then only 5.2% are true positives with a positive predictive value (True positives/total positives x 100) of 5.2/6 x 100 = 86.6%. This means about 13.4% are false positive. Notice as the prevalence of disease decreases, the percentage of false positives to total positives increases because the true positive percentage decreases but the percent false positive (in this case 0.8%) stays the same. Thus, the percentage of false positives would be about 26.6% at a three percent positive rate.

The source of the problem is recognized from Bayesian analysis. If the prevalence is low (say a prevalence of 1%) even a very good screening test with 99% diagnostic specificity and 100% sensitivity will produce only 1% false positive results: (diagnostic specificity 1%) = 0.01 x 10,000 tests = 100 false positives/10,000 tests and (0.01% prevalence of disease at 100% sensitivity) = 0.01 x 10,000 = 100 true positive but for a poor positive predictive value of only 50% (100/200 x 100 = 50%). Recognizing this problem, the CDC suggests most testing should be diagnostic: “Considerations for who should get tested: People who have symptoms of COVID-19, people who have had close contact with someone with confirmed COVID-19, people who have been asked or referred to get testing by their healthcare provider, or state health department. Not everyone needs to be tested. (7)”

Because of rightful concern regarding disease transmission from asymptomatic and pre-symptomatic cases, this advice is not being followed. As a result, the great abundance of testing is screening not diagnostic. One way to reduce false positive results is to repeat the test using a test with a different format (different manufacturer). Due to limited testing facilities confirmation is not routinely performed and only a few positives are confirmed by a second rRT-PCR assay. I conclude it is likely that at current active disease prevalence the positive rRT-PCR results of many “asymptomatic” persons are false positives.

There are negative psychological implications of thinking one is infected when one is not and some persons with illness other than COVID-19 who test false positive might be hospitalized with COVID-19 patients and become infected. This may explain why some persons seem to have been infected twice: the first time being a false positive. It seems to me it is important for practicing medical professionals to be aware of these issues so that they can appropriately advise and direct suspect patients for additional testing.
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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby Belligerent Savant » Mon Dec 07, 2020 12:42 am

.

This may have already been shared here.

https://www.fda.gov/medical-devices/let ... laboratory

Potential for False Positive Results with Antigen Tests for Rapid Detection of SARS-CoV-2 - Letter to Clinical Laboratory Staff and Health Care Providers

The U.S. Food and Drug Administration (FDA) is alerting clinical laboratory staff and health care providers that false positive results can occur with antigen tests, including when users do not follow the instructions for use of antigen tests for the rapid detection of SARS-CoV-2. Generally, antigen tests are indicated for the qualitative detection of SARS-CoV-2 antigens in authorized specimen types collected from individuals who are suspected of COVID-19 by their healthcare provider within a certain number of days of symptom onset. The FDA is aware of reports of false positive results associated with antigen tests used in nursing homes and other settings and continues to monitor and evaluate these reports and other available information about device safety and performance.

The FDA reminds clinical laboratory staff and health care providers about the risk of false positive results with all laboratory tests. Laboratories should expect some false positive results to occur even when very accurate tests are used for screening large populations with a low prevalence of infection. Health care providers and clinical laboratory staff can help ensure accurate reporting of test results by following the authorized instructions for use of a test and key steps in the testing process as recommended by the Centers for Disease Control and Prevention (CDC), including routine follow-up testing (reflex testing) with a molecular assay when appropriate, and by considering the expected occurrence of false positive results when interpreting test results in their patient populations.

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CDC agrees with FDA, also calls PCR tests into question
The CDC has echoed the FDA's recommendations. It stated that antigen tests "are not 100% accurate," conceding that false positive and false negative results may occur. “Antibody test results should not be used to determine if someone can return to work,” the CDC stated, adding that this also applied to schools, dormitories and even correctional facilities. “People who receive positive results on an antibody test but don’t have symptoms of COVID-19 and have not been around someone who may have COVID-19 are not likely to have a current infection,” the agency explained, stating that these people can "continue with normal activities." The CDC had already issued similar guidelines in August. That same month, a study published in the Journal of Clinical Microbiology found that rapid tests with a 98 percent specificity used in American schools could result in over 800,000 false-positive results every week. The CDC's concerns over the accuracy of coronavirus testing extend beyond the rapid antigen test. In November, it highlighted a study that showed that screening for a single polymerase chain reaction (PCR) test only caught around 53 percent of all positive cases of COVID-19 in students returning home from college. The main protocol for PCR testing for COVID-19 has itself come into question after a study that claimed that PCR nasal swabs had a 63 percent sensitivity failed to provide a peer-reviewed report. At the end of November, an international group of scientists called for the paper's retraction, calling it "severely flawed with respect to its biomolecular and methodological design."

Around the world, governments have already questioned the effectiveness of PCR testing. Portugal recently deemed the method unreliable. Meanwhile, other legal challenges to it are pending in Germany, Italy, Switzerland and South Africa. Follow Pandemic.news for more on the latest updates on the coronavirus pandemic.

Sources include: LifeSiteNews.com FDA.gov TheLancet.com WWWNC.CDC.gov CDC.gov JCM.ASM.org LockdownSkeptics.org



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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby stickdog99 » Thu Dec 10, 2020 9:12 pm

As more data comes in, it appears that the "false alarm" that I hypothesized in the USA is extremely dependent on one's locality:

https://public.tableau.com/profile/data ... cessDeaths

It appears that certain jurisdictions in the United States may have been severely underreporting COVID-19 related deaths all through this epidemic. And some states have recently experienced a huge increase in excess deaths.

On the other hand, certain states, including several currently on lockdown, have experienced no clear trend in overall excess deaths over the last few weeks.

Just to check on some recent data, I analyzed the change in California's numbers over the last 4 days (12/5 to 12/9). Over that time period, there were 138,610 cases of COVID-19 and 672 deaths associated with these cases reported in California. That's a 0.48% death rate, down from a 1.51% death rate among all positive cases since from the start of the pandemic to 12/5.

And the death rate has plummeted for all age groups:

Over 80 from 20.8% to 8.0%
75-79 from 10.4% to 3.8%
70-74 from 4.8% to 2.4%
65-69 from 4.4% to 1.4%
60-64 from 2.6% to 0.8%
50-59 from 1.1% to 0.3%

And for everyone from 18 to 59 from 0.35% to 0.10%.
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Leaving tests aside...

Postby JackRiddler » Thu Dec 10, 2020 9:35 pm

.

How does one assess this from today's news?

The United States recorded 3,124 COVID-19 deaths on Wednesday, shattering every daily world record since the start of the pandemic. One hundred seven thousand people across the U.S. are hospitalized with the disease — also a record — and more than 220,000 new infections were reported in just 24 hours. More than a third of U.S. residents live in areas where intensive care units have either filled to capacity or are running critically short of ICU beds.

https://www.democracynow.org/2020/12/10/headlines



What is usual for the total number of people hospitalized in a given day or at this time of year?

I haven't found a direct answer to this, although my research has been minimal and I've run into different statistics I don't know how to use in arithmetic/assess to get an answer for "excess hospitalizations," not that this would be definitively indicative of anything.

Here's a table over years for persons undergoing hospital stays per year as a percentage of the population (two categories: one stay in a year, more than one stay). But I haven't found what the average length of stay would be (or the number of stays ending in death).
https://www.cdc.gov/nchs/data/hus/2018/039.pdf

There are about 924,000 hospital beds. (It's gone down by a lot.) Not sure what that means either, how they're distributed, etc.
https://www.statista.com/statistics/185 ... ince-2001/

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Re: The supposed recent "spike" in COVID-19 cases is total b

Postby Joe Hillshoist » Thu Dec 10, 2020 11:08 pm

There appears to be a relationship between immunity or low symptomatic or asymptomatic response to SARS and prior infection by some Coronaviruses tho I can't remember which ones.

IE The antibodies you have from a previous infection that isn't COVID may work against COVID because of its similarity to the virus from the prior infection.

So I wonder if that has anything to do with the unreliability of the antigen and antibody test.
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