4 ms·

> During the first surge (ie, before June, 2020), 533 381 people were tested, of whom 11 727 (2.20%) were PCR positive The conclusion relies on the accuracy of

by trott 6y ago

> During the first surge (ie, before June, 2020), 533 381 people were tested, of whom 11 727 (2.20%) were PCR positive The conclusion relies on the accuracy of the tests. But what if these tests had a 0.5% false-positive rate? This would explain the cases that seemingly do not develop immunity. The authors' analysis mentions a 0.02% false-positive rate. However, there are those that estimate that the real-world number (for the UK, at least) is unknown and could be between 0.8-4.0% : https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30453-7 https://www.thelancet.com/journals/lanres/article/PIIS2213-2...

mike_hearn 6y ago
It's actually much worse than that. Nobody knows what the false positive rate of the COVID PCR testing programmes are, nor is it even possible to know thanks to the prevalence of circular logic in the public health sector. Summarised, COVID has been defined as having a positive test. Therefore, by definition, the test has no false positives. We know this is unlikely to be true in reality because in prior PCR testing programmes where disease was defined correctly they did indeed have FPs. In one notorious case the FP rate was 14% and the error rate was 100% - literally every single reported positive was false. In other pre-COVID times PCR labs were challenged with random samples and some labs had 8% FP rates. Last year I wrote about some of these problems on my blog: https://blog.plan99.net/pseudo-epidemics-7603b2da839 https://blog.plan99.net/pseudo-epidemics-7603b2da839 This second article summarises the first in bullet-point form for those who prefer that to a narrative format: https://blog.plan99.net/pseudo-epidemics-part-ii-61cb05669608 https://blog.plan99.net/pseudo-epidemics-part-ii-61cb0566960... None of the problems identified there have been fixed, as far as I know. Every FP rate you see quoted for PCR testing is based on some form of invalid reasoning like "one test might be false, but if we test the same person twice in a row then it's guaranteed to be correct and we can calculate the FP rate from that". There are some subtleties to consider. 1. FP rates seem to vary a lot across labs and across time. There is no one single FP rate. Lab challenge programmes in the past returned everything from zero FPs to unusably high rates of FPs for what was theoretically the same test and the same challenge samples. This can occur if FPs are driven mostly by cross-contamination. 2. Public health officials seem to routinely mis-use logic in ways that converts false positives into false negatives. FNs are then used to justify ramping up the sensitivity of the test, which creates more FPs, which are interpreted as more FNs, in a loop. My essay provides some evidence for this. 3. PCR tests don't detect live virus. They start with a lysis stage that kills the viruses and breaks open the capsid, so the RNA is exposed and can be detected. By implication the test cannot tell the difference between someone who is currently infected and infectious, and someone who has debris from their own body destroying the virus in their bloodstream. Normally this doesn't matter because PCR tests aren't scheduled by doctors for people who are healthy, but for COVID clinical diagnosis has been cut out of the equation entirely, so anyone who tests positive is considered to be "sick". It's fair to say that huge quantities of research papers that are predicated upon the correctness of PCR testing are actually in some limbo state of quasi pseudo-science. A test which detects a "disease" which is itself defined as testing positive is an exercise in circular reasoning and all conclusions based on it must be treated with extreme care.
- prepend 6y ago
  > Summarised, COVID has been defined as having a positive test. Therefore, by definition, the test has no false positives. These are different things. The covid case definition is an epidemiological case definition and wouldn’t be used for measuring test sensitivity. So the sentences I quoted incorrectly conflate two different concepts: incidence/prevalence and clinical accuracy. What’s screwy is that there needs to be some “gold standard” to measure “true prevalence” of disease so there’s some sort of estimates of this for however they calculate the sensitivity and specificity of these tests. But it’s not as simple as a tautological “positive test==COVID; therefore impossible to have false positives”
  - mike_hearn 6y ago
    What is this epidemiological case definition they use, which defines real COVID? I've done a lot of research and couldn't find such a thing, at least not last year. Such a definition can't be driven by symptoms because the list of COVID symptoms is itself derived from test-positive people, which is why the official list consists of basically all symptoms: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/s... "Fever or chills, Cough, Shortness of breath or difficulty breathing, Fatigue, Muscle or body aches, Headache, New loss of taste or smell, Sore throat, Congestion or runny nose, Nausea or vomiting, Diarrhea. This list does not include all possible symptoms." That's what you'd expect to see if a test with false positives was mandated to be treated as if it had none. Instead of the test being calibrated against a set of symptoms (the usual approach) the set of symptoms becomes calibrated against the test, and as the test has FPs, the set keeps expanding until it becomes a description of more or less any illness. This is why the few papers that ask questions about test FPs invariably seem to use an invalid definition, usually one based on double testing, i.e. no followed by yes is a false negative. there needs to be some “gold standard” to measure “true prevalence” of disease so there’s some sort of estimates of this for however they calculate the sensitivity and specificity of these tests Indeed, but unfortunately no such gold standard exists, as the papers I quoted in the essay explain. Or rather, the test is considered to be the gold standard. I read somewhere that the WHO may finally have changed the official definition of COVID to actually incorporate symptoms ... on the day of Joe Biden's inaugeration. That won't help with people claiming they're politically biased. However at this point such a move seems unlikely to fix anything because it's too late: the official set of symptoms was already derived from test results.
unanswered 6y ago
It's obvious if you know the solution ahead of time: we must conclude that the whole population needs to be vaccinated, and therefore we can work backward to find the false positive rate. Now, you can get upset at this explanation, but I challenge you to find a better explanation for starting with the exact, but arbitrarily picked false positive rate that leads to a politically desirable outcome.

by trott 6y ago

mike_hearn 6y ago

It's actually much worse than that. Nobody knows what the false positive rate of the COVID PCR testing programmes are, nor is it even possible to know thanks to the prevalence of circular logic in the public health sector. Summarised, COVID has been defined as having a positive test. Therefore, by definition, the test has no false positives. We know this is unlikely to be true in reality because in prior PCR testing programmes where disease was defined correctly they did indeed have FPs. In one notorious case the FP rate was 14% and the error rate was 100% - literally every single reported positive was false. In other pre-COVID times PCR labs were challenged with random samples and some labs had 8% FP rates. Last year I wrote about some of these problems on my blog: https://blog.plan99.net/pseudo-epidemics-7603b2da839 https://blog.plan99.net/pseudo-epidemics-7603b2da839 This second article summarises the first in bullet-point form for those who prefer that to a narrative format: https://blog.plan99.net/pseudo-epidemics-part-ii-61cb05669608 https://blog.plan99.net/pseudo-epidemics-part-ii-61cb0566960... None of the problems identified there have been fixed, as far as I know. Every FP rate you see quoted for PCR testing is based on some form of invalid reasoning like "one test might be false, but if we test the same person twice in a row then it's guaranteed to be correct and we can calculate the FP rate from that". There are some subtleties to consider. 1. FP rates seem to vary a lot across labs and across time. There is no one single FP rate. Lab challenge programmes in the past returned everything from zero FPs to unusably high rates of FPs for what was theoretically the same test and the same challenge samples. This can occur if FPs are driven mostly by cross-contamination. 2. Public health officials seem to routinely mis-use logic in ways that converts false positives into false negatives. FNs are then used to justify ramping up the sensitivity of the test, which creates more FPs, which are interpreted as more FNs, in a loop. My essay provides some evidence for this. 3. PCR tests don't detect live virus. They start with a lysis stage that kills the viruses and breaks open the capsid, so the RNA is exposed and can be detected. By implication the test cannot tell the difference between someone who is currently infected and infectious, and someone who has debris from their own body destroying the virus in their bloodstream. Normally this doesn't matter because PCR tests aren't scheduled by doctors for people who are healthy, but for COVID clinical diagnosis has been cut out of the equation entirely, so anyone who tests positive is considered to be "sick". It's fair to say that huge quantities of research papers that are predicated upon the correctness of PCR testing are actually in some limbo state of quasi pseudo-science. A test which detects a "disease" which is itself defined as testing positive is an exercise in circular reasoning and all conclusions based on it must be treated with extreme care.

prepend 6y ago
> Summarised, COVID has been defined as having a positive test. Therefore, by definition, the test has no false positives. These are different things. The covid case definition is an epidemiological case definition and wouldn’t be used for measuring test sensitivity. So the sentences I quoted incorrectly conflate two different concepts: incidence/prevalence and clinical accuracy. What’s screwy is that there needs to be some “gold standard” to measure “true prevalence” of disease so there’s some sort of estimates of this for however they calculate the sensitivity and specificity of these tests. But it’s not as simple as a tautological “positive test==COVID; therefore impossible to have false positives”
- mike_hearn 6y ago
  What is this epidemiological case definition they use, which defines real COVID? I've done a lot of research and couldn't find such a thing, at least not last year. Such a definition can't be driven by symptoms because the list of COVID symptoms is itself derived from test-positive people, which is why the official list consists of basically all symptoms: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/s... "Fever or chills, Cough, Shortness of breath or difficulty breathing, Fatigue, Muscle or body aches, Headache, New loss of taste or smell, Sore throat, Congestion or runny nose, Nausea or vomiting, Diarrhea. This list does not include all possible symptoms." That's what you'd expect to see if a test with false positives was mandated to be treated as if it had none. Instead of the test being calibrated against a set of symptoms (the usual approach) the set of symptoms becomes calibrated against the test, and as the test has FPs, the set keeps expanding until it becomes a description of more or less any illness. This is why the few papers that ask questions about test FPs invariably seem to use an invalid definition, usually one based on double testing, i.e. no followed by yes is a false negative. there needs to be some “gold standard” to measure “true prevalence” of disease so there’s some sort of estimates of this for however they calculate the sensitivity and specificity of these tests Indeed, but unfortunately no such gold standard exists, as the papers I quoted in the essay explain. Or rather, the test is considered to be the gold standard. I read somewhere that the WHO may finally have changed the official definition of COVID to actually incorporate symptoms ... on the day of Joe Biden's inaugeration. That won't help with people claiming they're politically biased. However at this point such a move seems unlikely to fix anything because it's too late: the official set of symptoms was already derived from test results.

unanswered 6y ago

It's obvious if you know the solution ahead of time: we must conclude that the whole population needs to be vaccinated, and therefore we can work backward to find the false positive rate. Now, you can get upset at this explanation, but I challenge you to find a better explanation for starting with the exact, but arbitrarily picked false positive rate that leads to a politically desirable outcome.