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More insights on the extent of COVID-19 among asymptomatics and false positive PCR testing rates


Here is an up-to-date table of results from the Cambridge University study of asymptomatics since the start of 2021 (see previous report and commentary).


Key points:

  • The testing is pooled PCR testing whereby, instead of separately testing each sample, several samples are combined and the pooled sample is tested. If a pooled sample tests positive then each individual sample from the pool is re-tested for confirmation.

  • Since the start of 2021 there have been 32,819 tests on asymptomatic students. Only 8 were confirmed positive. So less than 1 in 4000 people without symptoms (when tested) had the virus and we do not know how many of those went on to develop symptoms (it is possible none of them did).

  • There were 31 false positives in 13,873 pools tested. So the false positive rate is about 0.22%.

  • Of the 39 positives only 8 were confirmed, meaning that if an asymptomatic person gets a positive PCR test result, there is only a 20.5% chance the person has the virus.

  • While less than 1 in 4000 without symptoms has the virus, about 1 in 447 people without symptoms who get a PCR test wrongly test positive. Hence, mass testing of asymptomatic people may be creating unnecessary anxiety, with minimal benefit at enormous cost.

  • This study provides useful information about the false positive rate of PCR testing – but only for asymptomatics tested under very careful conditions. The 0.22% false positive rate likely represents a lower bound for asymptomatics tested. We know nothing from this study about the false positive rate for people with (COVID-like) symptoms but, given the nature of the PCR test, it is certain to be much higher especially given the evidence about reporting positives for a single gene since Sept 2020.

  • Taking account of the Government reported COVID-19 ‘case numbers’ this data exposes the much repeated claim that “1 in 3 people with the virus has no symptoms” as a massive exaggeration (see proof). In a paper we are near to completing we show that what MIGHT be true is that “1 in 3 people who test positive have no symptoms”. But, because of false positives, that’s very different to the claim that “1 in 3 people with the virus have no symptoms”.

  • And a reminder that “1 in 3 people with the virus have no symptoms” is NOT the same as “1 in 3 people with no symptoms have the virus” (transposed conditional fallacy). But because many assume these are equal it is possible that the messaging was deliberate to scare people even more.

It is also important to note that people who argue that the false positive rates must be less than 0.1% because in the summer of 2020 fewer than 0.1% of those tested were positive, fail to understand the following: during that period there was no mass testing and, because the virus had mostly gone then, it is likely that any positive test was subject to confirmatory testing (which, incidentally, was originally supposed to happen routinely). Assuming independence between tests – and assuming asymptomatics are being tested - this means (assuming a 0.22% single test false positive rate) that the ‘overall’ false positive rate would have been 0.000484% if all positives had been subject to a confirmatory test. For more on this see here.

In fact uncertainty about the extent of any confirmatory testing being done is one of the most important missing features in the debate about 'case numbers'. Suppose, for example, authority A reports 'cases' on the basis of a single positive test result, while authority B reports 'cases' only on the basis of a confirmed positive test result. Then even if exactly the same testing process is carried out (with say an average false positive rate of 0.4% on any single test) and both authorities had exactly the same population size 500,000 and nobody with the virus, Authority A would be reporting 8 'confirmed cases' while Authority B would be reporting 2,000 'confirmed cases'. If Authority C carried out confirmatory testing on 50% of the positives then they would be reporting 1,004 'confirmed cases'. So you could be seeing anything between 8 and 2,000 'confirmed cases' that are acually false positives depending simply on what percentage are subject to confirmatory testing. And the same applies to 'the false positive rate' which could be anything between 0.00016% and 0.4%.

Hence, any data about about 'cases', 'positive test results' and 'false positive rates' that fails to make explicit the extent of confirmatory testing ' are essentially useless.

See also:


  • Cambridge Study

  • The Cambridge study and its implications for the claim that "1 in 3 people with the virus have no symptoms"

  • UK lighthouse laboratories testing for SARS-COV-2 may have breached WHO Emergency Use Assessment and potentially violated Manufacturer Instructions for Use

  • About pooled testing

  • Implications to the Government's claim that "1 in 3 people with the virus has no symptoms"

  • On the importance of confirmatory testing