UK COVID-19 False Positives: understanding the scale of the problem

2 min read


This #casedemic false results issue is hard to grasp. Hopefully the below helps paint the picture.

⚠️ Dr. Malcolm Kendrick, Dr. Michael Yeadon, Prof Carl Heneghan and many many more academics and health professionals have strongly expressed their serious concerns with the reliance on this daily Hyper Testing of the public.

🧑‍🔬 Dr. Kendrick went one step further to receive insight from Testing Lab Technician who was even more concerned about the matter – exposing FP rates of up 5%!

👁 So, to get grounded, you may wish to read the 2x articles from Kendrick and Yeadon.

Bang on the money

👌🏼 Both are absolutely right statistically. We are driving nationwide policy on incredibly unreliable data.

🌋 In a situation where prevalence is so low (as confirmed by ONS on 25th Sept as 1 in 500 people), and a Hyper Scaled testing regime, the (several) imperfections in the Test get massively amplified.

😬 ONS do report an increasing prevalence and daily incidence rate based on their statistical modelling, but nonetheless the low prevalence is incredibly problematic.

👎🏼 Compound that with a terrible Case Definition (i.e. just a positive test – no clinical diagnosis of symptoms or confirmation test), and this testing effort is next to useless.

😖 Oh, and this doesn’t even acknowledge the user and config errors across the UK’s labs. This too is a big issue, given the scaled recruitment effort and sheer pressure for daily processing volume.

The Bottom Line

😳 The BOTTOM line in this statistical analysis below is that only 0.17% of all tests are viably accurate in the detection of the Virus (not the Disease).

❗️AND, 90% of all the Positive Tests are very likely False Alarms.

‼️ Meaning that 3,000 – 4,500 people PLUS their close contacts are being demanded to (wrongly) SELF ISOLATE. Evey single day!

😩 Furthermore, these Positive Tests are locking more and more regions down, informing increasingly restrictive draconian rules on the public, and inflated the downstream hospital and death statistics.

💉 Oh, and not to forget scaring the hell out of the public, and wearing them down ready to submit to a rushed vaccine and the Flu jab.

🧮 It’s in your interest and the Public interest to get your head around this Maths and associated Science.

Covid-19 False Positive Issues in the UK


SOURCE: see graphic.

P.S. As Flu Season prevalence increases, the Test efficacy will marginally improve – but not my much.

COUNTER: HuffPost released a False Positive claim rebuttal. Upon review, it it is not an accurate an honest analysis, with many assumptions that the author hopes you will not challenge them on.

The Original Facebook Post

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3 thoughts on “UK COVID-19 False Positives: understanding the scale of the problem

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  1. I fully believe that what we are seeing is a ‘casedemic’, and I’ve tried to talk to friends and family about false positives, but what I tend to get in response is “but what about false negatives?”. This is where I am struggling, as I’ve not yet found anyone who can validly explain why the false positives are not likely outweighed by false negatives.
    It seems to me that your figures above are in some way ‘circular’ – you base the expected covid presence on estimated prevalence as of 25th sept, and then use this expected presence to calculate an estimate (20%) of the number of cases that wouldn’t be detected (false negatives), then deduct this from the expected presence to get an estimate of the number of true positives expected to be found in tests. But surely the estimated prevalence (B rate & C rate in your table) are themselves in some way determined by test results? And false negatives would therefore be ‘feeding into’ these prevalence estimates (possibly making them lower than is the actual case??)
    How are the prevalence rates estimated? Please someone correct me if I am wrong with any of this, as my gut feeling is screaming at me that the positive ‘case’ numbers we are seeing are massively inflated… but I am struggling to find the correct science to back this up. Everyone that talks scientifically about false positives, doesn’t seem to talk about false negatives (therefore seen by some to be “cherry picking” the data to suit a certain narrative).
    I read Dr Kendrick’s article which you linked, hoping that this would provide the correct science about false negatives too, but there seem to be errors in his explanations – the formula for specificity means specificity is the number of true negatives that are actually picked up from tests as a proportion of the number of people tested who are not actually carrying the virus (from what I can understand, TN + FP is the total number of people tested who are actually negative, not the total number of tests carried out as Dr Kendrick says, nor the total number of negative results, as someone in the comments on that article suggests as a correction – I think this latter would instead be the “negative predictive value” rather than specificity?)
    And he mentions an estimated/average figure of 16% false negatives, but then doesn’t expand on this at all to show what this actually means in terms of the numbers.

    I do wholly believe we are seeing a ‘casedemic’ rather than an epidemic, but would really like some correct science to back it up, and balance it with false negatives, if anyone can point me in the right direction for this?

    1. Just thought I’d best add now that my confusion in the comment about the figures maybe being ‘circular’ has been cleared up over on the original facebook post! Thanks.

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