cambridge covid study
This makes interesting reading for anybody who still believes the Government 'case' data and the claim that just because you don't have any COVID-19 symptoms it doesn't mean you aren't in danger ...

This data also means that if the Government claim that "1 in 3 people with the virus has no symptoms" is correct then the ONS estimated infection rate is massively inflated - the currently reported 'case' numbers must be at least 8 times greater than the true number of cases. On the other hand, if the Government estimates of case numbers are correct then at most 1 in 26 people with the virus has no symptoms. Here's an informal explanation why (formal proof is below):

Cambridge has a population of 129,000.

If the ONS infection estimates for Cambridge (0.71%) are accurate, then during an average week in this period about 916 people had the virus and 128,084 did not.

But if the "1 in 3" claim is correct about 305 people in Cambridge had the virus but no symptoms.

So at most 128,389 people in Cambridge had no symptoms and that means at least 305/128389 people with no symptoms had the virus. That is at least 0.24% (i.e. at least around 1 in 421).

But the study shows on average only 1 in 4867 (0.028%) with no symptoms had the virus. So there should only have been about 36.

That means the "1 in 3" claim and the ONS estimates cannot both be correct.

If the "1 in 3" claim is correct, then the maximum possible value for the infection rate is 0.084% and not 0.71% as claimed (with 0.084% we would have 108 with the virus of whom 36 have no symptoms). So the ONS estimated infection rate is over 8 times greater than the true rate.

If the 0.71% infection rate is correct, then the maximum possible value for the proportion of people with the virus who have no symptoms is 3.9% (as this would mean 36 of the 916 people with the virus have no symptoms as predicted by the Cambridge data).
covid math
covid math
covid math
  • Although the above analysis applied to a single UK city, there is no reason to believe it' is special (see the report below on national lateral flow testing data).
  • Since mass PCR testing began many of those classified as 'cases' were not COVID-19. And the Government claim that "1 in 3 with the virus has no symptoms" is massively exaggerated. There needs to be confirmatory testing for any people testing positive before they are declared a 'case'.
  • We should stop testing people without symptoms unless they have been in recent contact with a person confirmed as having the virus.
  • And it's always interesting to compare number of NHS 999 emergency COVID-19 calls/triages with number of 'cases'. This data ( clearly shows real pandemic last spring but not '2nd/3rd waves'. All caveats discussed here apply
covid graph
Also: This Government report says 9,480 of 2,372,358 lateral flow tests in UK 28 Jan - 3 Feb were positive. It is assumed almost all lateral flow tests are on people without symptoms. Given the false positive rate for these tests that's about 1 in 1587 true positives. In the same period the ONS estimated UK infection rate was 1 in 77.

Obviously all of this data is on asymptomatics tested, so we expect the percentage testing positive to be less than the overall infection rate. However, this data still massively contradicts Government claims about asymptomatics as explained here.

And, of course, we have very solid evidence that the number of 'cases' based on PCR testing are inflated.

The links: