Friday 30 April 2021

COVID-19: Discrepancy between 'cases' and 'illness'

It's been a while since we last highlighted the difference between Covid-19 'case' numbers (and by extension this means also hospitalisation numbers and death numbers) and actual Covid-19 illness.

The NHS pathways coronavirus triages website (see illness due to Covid-19 as it combines all 999, 111, online and ambulance calls relating to Covid-19 triages. Previous articles (see links) make clear what the caveats are.

The triage data confirms the real pandemic of spring 2020. I've still yet to see any better evidence that the (vast) majority of 'cases' (i.e. positive PCR test results) since the summer of 2020 have been false positives. 


Monday 12 April 2021

The barriers to academic publication for work that challenges the ‘official narrative’ on Covid-19


Our paper about the “1 in 3 people with Covid-19 have no symptoms”  claim has had (at time of writing this) 4093 reads since we posted it on researchgate on Friday,  and 336,755 impressions to the tweet about it. The video summary has been watched by 7,422 people in 2 days.

But, this was the response we got less than 24 hours after we submitted it to the BMJ:

“Thank you for sending us your paper. We read it with interest but I am sorry to say that we do not think it is right for the BMJ. In comparison with the many other papers we have to consider, this one is a lower priority for us. We do not send out for external peer review manuscripts whose subject matter, design or topic do not meet our current priorities and are unlikely to make it through our process.”

Even more bizarrely, neither the medRxiv or  arXiv sites (where we routinely post pre-prints of our research) would accept the paper. MedRxiv said:

“Thank you for submitting your manuscript to medRxiv. We regret to inform you that your manuscript is inappropriate for posting. medRxiv is intended for research papers, and our screening process determined that this manuscript fell short of that description.”

while arXiv (which initially said: “Your article is currently scheduled to be announced at Fri, 9 Apr 2021 00:00:00 GMT”) quietly changed the status of the article to “on hold” as the submission “was identified by arXiv administrators or moderators as needing further attention.” UPDATE: we now have the following response:

"Our moderators have determined that your submission is not of sufficient interest for inclusion within arXiv. The moderators have rejected your submission after examination, having determined that your article does not contain sufficient original or substantive scholarly research.

As a result, we have removed your submission.Please note that our moderators are not referees and provide no reviews with such decisions. For in-depth reviews of your work, please seek feedback from another forum.

Please do not resubmit this paper without contacting arXiv moderation and obtaining a positive response. Resubmission of removed papers may result in the loss of your submission privileges"

Compare this with what happened in April 2020 when we first investigated the Covid-19 data. Whereas our latest work shows that Covid-19 'case' numbers have been exaggerated and that mass testing of asymptomatic people is counter-productive, at that time we were actually concerned that 

a) the numbers infected were being UNDERESTIMATED and 
b) the data was being skewed by the fact that ONLY people with extreme covid symptoms were being tested (and hence we argued for the need for more random testing). 

These views were not considered threatening to the "official narrative" and of course random testing WAS widely implemented after August. We had no problem getting those articles published in academic journals (see e.g. here  and here are some of our other articles on Covid-19)

But things are completely different when you challenge the "official narrative". Given that even researchgate has been censoring such articles it is possible that our latest paper may be removed. If so a copy can be found here.




Friday 9 April 2021

Smashing the “1 in 3 people with Covid-19 have no symptoms” claim – and what it means about the true number of ‘cases’


We have finished our report about the “1 in 3 people with Covid-19 have no symptoms” claim. The full version is here

Here is a 6-minute video summarising the key results:

And below is a short summary (which can also be downloaded as a pdf):

Smashing the “1 in 3 people with Covid-19 have no symptoms” claim – and what it means about the true number of ‘cases’

Norman Fenton[1], Martin Neil, Scott McLachlan

Queen Mary University of London

One of the most persistent and widely publicised claims made by the UK government and its scientific advisers about SARS-Cov-2 is that "1 in 3 people who have the virus have no symptoms”.

However, using data from a study of asymptomatics at Cambridge [1] we show that both the “1 in 3” claim  and the Office for National Statistics (ONS) infection rate estimates are exaggerated. The full analysis is provided in [2], but here we provide a simplified summary and explanation.

The Cambridge study uses PCR tests on asymptomatic people, and a person is classified as having the virus if an initial positive test is confirmed in a follow-up test. Their data shows that, during a period when the ONS estimated the infection rate was 0.71%, an average of only 1 in 4,867 people (0.00205%) with no symptoms had the virus at any time.  Although this does not tell us how many people with the virus had no symptoms,  we can conclude the following (the formal proofs are provided in [2]):

Conclusion 1: If the ONS reported infection rate, 0.71%, is correct, then at most 2.9% (1 in 34) of people with the virus have no symptoms, and not 1 in 3 as claimed by the government.

Informal explanation: The population of Cambridge is 129,000. So, since only 1 in 4867 asymptomatics have the virus, the maximum possible number of asymptomatics with the virus is 27. If the ONS claimed infection rate is correct, then 0.71% of people in Cambridge would have the virus. This is about 916 people. Hence, at most 27 out of 916 with the virus had no symptoms. That is a maximum of 2.9% (27/916), 1 in 34.

But, on the other hand, it tells us:

Conclusion 2: If the government claim that “1 in 3 people with the virus has no symptoms” is correct, then the ONS reported infection rate must be at most 0.06%. This would mean the reported rate of 0.71% is at least 11 times greater than the true infection rate.

Informal explanation: We already noted that in Cambridge the maximum number of asymptomatics with the virus is 27. But if 1 in 3 people with the virus have no symptoms, then the maximum total number of people with the virus is three times that number, 81. That means a maximum of 81 out of 129,000 have the virus. Thus, the maximum infection rate consistent with the government’s claim is 0.06% (81/129,000) and not 0.71%.

Hence, the UK government claim “1 in 3” claim and the ONS infection rate claim cannot both be simultaneously true.

What is the explanation for the government and ONS claims being mutually incompatible, and what is the likely virus infection rate and proportion of people with the virus who have no symptoms? The explanation lies in the impact of false positives. Unlike in the Cambridge study, the government assumes that a person who tests positive in a single PCR test has SARS-Cov-2 and they are very unlikely to be subject to confirmatory testing to determine whether this is a true or false positive. So, an estimated 0.71% infection rate is essentially an estimate that, if we randomly tested 100,000 people, we would expect 710 to test positive.

However, we also know from the Cambridge study that 3 in every 1000 people without symptoms will falsely test positive. Taking this, and all the other observed data into account in [2] we show that

·       The infection rate, for the period in question, was most likely to be 0.379% (95% confidence interval is 0.372% to 0.387%). We also performed a sensitivity analysis that showed that the results are robust against a range of prior assumptions; specifically, there is no reasonable scenario under which the maximum value for the infection rate was greater than 0.44%.

·       The percentage of people with SARS-Cov-2, but no symptoms was most likely to be 5% (95% confidence interval is 4.4% to 5.4%). Again, we showed this result was robust under a sensitivity analysis. There is thus no reasonable scenario in which more than 1 in 18 people with SARS-Cov-2 had no symptoms.

So, we conclude:

·       The government’s “1 in 3” claim overestimates the percentage of people with SARS-Cov-2 who have no symptoms by at least 560%.

·       The ONS’s estimate of the infection rate (0.71%) is exaggerated by at least 80%.

In [2] we also considered the extent to which the government and ONS claims are exaggerated for different possible infection rates. As the infection rate drops, the extent to which the estimated infection rate is exaggerated increases, because the proportion of false positives inevitably increases. At the current estimated infection rate of 0.35%, it is likely that the actual infection rate is only around 0.1% meaning that the current estimated infection rate exaggerates the true rate by 250%. The percentage of people with SARS-Cov-2, but no symptoms is most likely to be 9.62%, about 1 in 10, not 1 in 3 as claimed.

One other interesting observation we found was that, when the ONS estimated infection rate is 1% the proportion of people testing positive who have no symptoms is approximately 1 in 3. Hence, the claim that “1 in 3 people with the virus have no symptoms” claim is approximately true in this case only if we replace “with the virus” with “test positive”.

While the focus was on just one UK city there is reason to believe that the results apply generally throughout the UK, and it is therefore also reasonable to conclude that SARS-Cov-2 case numbers have been systematically exaggerated. It is also reasonable to conclude that mass testing of asymptomatic people (who have not been in recent contact with a person confirmed as having the virus) may be causing unnecessary anguish for minimal benefit at a very high societal and economic cost.


[1]        Cambridge University, “Asymptomatic COVID-19 screening programme,” 2021. [Online]. Available:

[2]        N. E. Fenton, M. Neil, and G. S. McLachlan, “What proportion of people with COVID-19 do not get symptoms?,” 2021. [Online]. Available:

[1] Corresponding author: