Most people who test positive for Covid 19 do not have the disease.

The government is shutting down the UK’s economy largely because of a misunderstanding of testing.

Surely this can’t be true.

Unfortunately it is.

Let me explain:

The test being used for Covid 19 is a swab called PCR (Polymerase chain reaction).

It detects active Covid infections but can also be (falsely) positive in old infections, re-infections and other virus infections.

 The false positive rate is 0.8%. (It is 99.2% accurate)

This seems impressive.

But there is a problem and it is a major one.

Whenever there is a disease with a low prevalence then false positives will always outnumber true positives.

This has been known for years and is called the False Positive Paradox.

The prevalence of Covid 19 is approximately 1 in a 500 or 0.2% at the moment.

This is a low.

This is how it works out:

Testing 100,000 people at the moment would find 200 people who tested positive who had a genuine active Covid 19 infection.

But it would also find 800 people will had a false positive test. They would not have an active Covid infection but would be told they do.

 (See below for maths as the figures may surprise you).

What this means is that if you test positive for Covid then it is far more likely that you do not have a Covid infection than you do.

It also means that in autumn when viral infections naturally go up then figures for Covid will also falsely rise (due to other viruses increasing) causing unnecessary alarm.

Covid 19 is a dangerous disease but fortunately death rates will not rise in line with cases detected because much of the apparent rise is caused by non-Covid infections.

For the Moonshot testing the government has purchased large amounts of the AbC-19 test. The British Medical Journal has found the clinical sensitivity of this test varies between 84.7 and 92.5%. In other words its accuracy is very poor. The number of false positives will greatly outweigh true positives (far greater than the 5:1 ratio of false to true positives noted above). In simple terms it is not fit for purpose.


TRUE POSITIVES   = 100,000 X 0.002 (incidence of disease is 0.2%) = 200 cases

FALSE POSITIVES = 99,800 (the people without the disease) X 0.008 (that is because 0.8% have a false positive) = approximately 800 people

TRUE NEGATIVES = 99,800 X 0.992 (the 99.2% of people for whom the test is accurate) =  99000


Basing government policy on this testing system makes no sense