In other words, I’m saying….

  1. Bayes’ theorem predicts a high proportion of false positive PCR tests is likely, especially when used in screening programs
  2. Ivor Cummins’ reviews of readily available COVID-19 data has been supported by some world class immunologists
  3. Pre-symptomatic carriers cause about 1% transmission in contacts, and it’s almost always less than 6 hours before symptoms appear
  4. Transmission of SARS-CoV2 by those without symptoms is virtually non-existent, so why the PCR testing or isolation requirements?

Claim 9: “False positives from PCR testing behind recent waves of infection.”

“Dr Canaday says when relatively few people are infected, most positive cases found via PCR testing are likely to be false positives – and this could be behind reported waves of infection that haven’t had the same level of mortality as previous waves.”

NH Counterclaim: “Dr Campbell said New Zealand is proof that can’t be true.”

NH: “If false positives really were an issue of that magnitude, we would be seeing reported case numbers here as much much higher than they are given the volume of testing that’s being carried out.”

“About 40,000 tests a day were carried out in the week after the Delta outbreak here began, finding at most dozens of community cases each day.”

DrPC: Dr Campbell, who does not have a medical or public health background may not be aware of the statistical tool known as Bayes’ theorem which is commonly applied to diagnostic test evaluation. In the context of PCR testing for presence of COVID-19, Bayes’ theorem is used to determine the number and percentage of false positives when compared to all positive results in a laboratory test, depending upon the prevalence of disease. (R1-pdf, R2)

Briefly, no laboratory test can be 100% accurate, meaning that it always correctly detects disease when it is present (proven through a complete diagnostic work-up and appropriate response to treatment, for example), and correctly excludes disease when it is absent (and it turns out there is some other disease). The former (positive result when disease is present) is called sensitivity and the latter (negative result when disease is absent) is called specificity.

Let’s take an example of a test that is 99% accurate (in both sensitivity and specificity). Let’s assume, for example, that in the 3+ weeks since the first delta case appeared in NZ, there have been 40,000 tests per day for 21 days, total 840,000 tests. We know that there are 617 “active cases” including 27 “hospital cases” as of 10 September, with an unknown number of those “recovered” since the mid-August recent outbreak (statistics are not available for the recent outbreak only).

Let’s assume that there were 840 truly real “cases” that truly have SARS-CoV-2, among the active and recovered (and no deaths to date) in the recent outbreak. The prevalence of disease is then 840/840,000 or 0.1%.  The PCR test would detect 99% or 832 correctly and miss another 8 that were positive but called negative. On the other hand, 99% of the remaining individuals would have tested negative, and from those 839,168 some 1% or 8,392 individuals would be negative but falsely labelled positive. The positive predictive value, determining what proportion of the PCR positives are a true “case” is given by TP/TP+FP= 832/832+8392= 9%, and the remaining 91% would be false positive. (R3)

To be sure, some of the “positive cases” would represent “true positives” with higher viral loads and a lower Cycle Threshold and be a “case” under the traditional medical definition. However, when the bulk of testing is for contacts without symptoms, the false positives will outweigh the true positives by a significant factor.

From my prior response to Claim 1 (see supra), we know that setting the Cycle Threshold (CT) for detection of viral genomic material to 40, which is what PCR testing in NZ is set to, may correlate to virus that can be cultured far less than 50% of the time, probably less than 3% of the time. (R4) Thus, the specificity of the PCR test in this context is well less than the 99% given in the example above, and it follows that the largest proportion of positive PCR tests will likely be false positives.

NH: “Thousands of tests were done every day in the months before then, with none found at all.”

DrPC: No, that’s demonstrably false. There may have been no or only a few community cases, but many positive results were detected in managed isolation and subsequent quarantine, as in the graph below. How many of those were sick or hospitalised cannot be determined from the publicly available data. (R5)


NH: “Dr Canaday’s proof included a graph by discredited Irish conspiracy theorist Ivor Cummins, who last year claimed the pandemic was over because most people were already immune, the Guardian reported in February.”

DrPC: It doesn’t matter whether Dr Campbell believes or whether the Guardian believes that Ivor Cummins’s work has been discredited. It only matters if rigorous analysis of the data and statistical methods used shows that his work does not pass muster by those with relevant scientific expertise who are external reviewers without declared or undeclared conflicts of interest. Ivor Cummins’s factual and accurate analysis of government data on Covid-19 is supported by one of the world’s leading immunologists, Emeritus Professor Beda Stadler, former Director of the Institute for Immunology at the University of Bern, amongst others. (R6, R6A)


DrPC: If Bayes’ theorem shows us that false positive test results often prevail for a disease of low prevalence, what can we infer regarding the danger to others of the so-called “asymptomatic carrier,” or PCR positive person who is supposedly carrying enough viral load to infect others with the SARS-CoV-2 virus but who has no symptoms of a respiratory or any other illness?

Case example 1

In the post-lockdown period in Wuhan, a study of nearly 10 million subjects (with a 93% participation rate) found no symptomatic cases and 300 asymptomatic cases (incidence 3/100,000) of COVID-19 as determined by positive PCR tests. (R7) The incidence of asymptomatic cases was almost always less than 1% of the number of prior confirmed COVID-19 cases in each province, often less than 0.5%.

Whilst most of the asymptomatic cases had either IgM or IgG antibody evidence of infection, 37% had no antibody evidence of disease at all, suggesting that the remainder likely had false positive PCR tests (or possibly only cell-mediated immunity, not tested). In addition, the authors confirmed that when “… virus culture was carried out on samples from asymptomatic positive cases … no viable SARS-CoV-2 virus [was found],“ again reflecting false positives with no chance of transmitting disease. In addition, “…all 1174 close contacts of the asymptomatic positive cases tested negative, indicating that the asymptomatic positive cases detected in this study were unlikely to be infectious.

Case example 2

A more recent paper from Pakistan studied 201 COVID-19 subjects and their 7168 contacts, where COVID-19 was confirmed by a positive PCR test. Few or no mitigating measures were in place in this densely populated and poverty-stricken region. Nevertheless, only 1.1% of 5611 contacts of pre-symptomatic proven COVID-19 subjects developed symptomatic, PCR positive COVID-19 disease when followed for a mean of 85 days, and all but one of these had been in close contact, usually with a live-in family member or co-workers, within 6 hours of the primary person’s onset of symptoms. Only one of 63 PCR positive contacts turned positive if exposed at 6-12 hours before the primary subject’s symptoms appeared, and none prior to that. Transmission of COVID-19 from asymptomatic persons was exceedingly rare at 0.06% of 1557 contacts and this occurred in a brother who remained himself asymptomatic throughout the remaining observation period. The other 1556 contacts of the asymptomatic primary subject never tested positive for COVID-19, with the same result also for 5598 contacts of the pre-symptomatic subjects. (R8)


Where the prevalence of disease is low and the expectation of false positives is high (as in the screening and contact tracing scenario), justification for intrusive measures like lockdowns, masks, and isolation of the asymptomatic becomes more difficult. Isolate the symptomatic, sick, and frail elderly and let’s be done with it! See also (R9-pdf) and (R10).


R1: An application of Bayes’ rule to medical test evaluation. https://journals.sagepub.com/doi/pdf/10.1177/875647939000600403

R2: A qualitative approach to Bayes’ theorem. https://ebm.bmj.com/content/16/6/163

R3: False positive RT-PCR. J Occup Environ Med. 2021 Mar; 63(3): e159–e162. Published online 2021 Jan 6. doi: 10.1097/JOM.0000000000002138

R4: Jafaar et al. on growth of SARS-CoV-2 at differing PCR cycle thresholds. https://doi.org/10.1093/cid/ciaa1491

R5: Current cases of COVID-19 per MOH definitions and statistics. https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-current-cases#current-situation

R6: Includes Emeritus Professor Beda Stadler on T-cell immunity and support of Ivor Cummins. https://principia-scientific.com/t-cells-really-are-the-superstars-in-fighting-covid-19/  and

R6A:  https://odysee.com/@IvorCummins:f/ep91-emeritus-professor-of-immunology:d

R7: Asymptomatic subjects post Wuhan lockdown do not transmit infection. https://www.nature.com/articles/s41467-020-19802-w

R8: Transmission of COVID-19 in pre-symptomatic and asymptomatic patients. https://virologyj.biomedcentral.com/articles/10.1186/s12985-021-01609-w#Tab3

R9: Failed PCR tests and lockdowns, UK. MP-briefing-26-Nov-2020.pdf (dailysceptic.org)

R10: Open letter to UK employers on COVID-19 testing of employees. Open Letter to Employers re Covid-19 Testing Requirements for Employees (ukmedfreedom.org)