Tweet

Prof Norman Fenton

27 Feb, 6 tweets, 3 min read

This has interesting implications for the accuracy of the official #Covid19UK 'case' data and the claim that it is critical to test people even if they don't have any COVID-19 symptoms. No fancy analysis here. Just the raw data from the Cambridge University study of asymptomics

aysomptomatics (not asymptomics!!!). All the relevant links here: probabilityandlaw.blogspot.com/2021/02/the-ca…

This report says 9,480 of 2,372,358 lateral flow tests in UK 28 Jan - 3 Feb were positive. Given false pos rate for these tests that's about 1 in 1587 true positives. In same period ONS estimated UK infection rate was 1 in 77. gov.uk/government/pub…

Obviously all of this data is on asymptomatics tested, so we expect the % testing positive to be less than the overall infection rate. But this data still contradicts Govt claims about asymptomatics probabilityandlaw.blogspot.com/2021/02/claim-…

And of course we have increasing evidence of the extent to which PCR testing has inflated 'case' numbers: probabilityandlaw.blogspot.com/2021/02/uk-lig…

Apparently it was PCR testing at Cambridge (not LTF as stated in original graphic). Even more interesting as it further highlights the problem with PCR test results if no confirmatory testing is done. Updated graphic:

• • •

Missing some Tweet in this thread? You can try to force a refresh

This Thread may be Removed Anytime!

Twitter may remove this content at anytime! Save it as PDF for later use!

More from @profnfenton

Prof Norman Fenton

@profnfenton

28 Feb

1. As I pointed out yesterday with this graphic (now updated) the Cambridge study data shows that since December only a miniscule percentage of people without COVID-like symptoms have the virus. But it also exposes massive contradications in the official Government data.

2. First it demonstrates (for one UK city) that the much publicised claim that "1 in 3 people with the virus has no symptoms" cannot be correct if the ONS estimated infection rate is correct. Here's an informal explanation (formal proof follows later in thread)

3. In fact, if the "1 in 3" claim 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.

Read 14 tweets

Prof Norman Fenton

@profnfenton

18 Dec 20

1/3. The UK #COVID19 testing data since September by different regions reveals a very close correlation between number of people tested and the proportion who test positive (i.e. the positivity rate)....

2/3 ...not clear what if any causal explanation there is to such a close correlation. But indications are that an increasing positivity rate may not be due to an increase in underlying infection rate... A full discussion is here .. probabilityandlaw.blogspot.com/2020/12/uk-cov…

3/3 ... and lockdown decisions could result purely from decisions to increase testing. All the usual caveats apply (and in this case this analysis has been done in a hurry to get it out, as the results seem so unusual and could not be seen from looking at the overall UK data)

Read 5 tweets

Prof Norman Fenton

@profnfenton

17 Oct 20

1. Some people taking issue with my earlier plot of daily new covid hospital admissions as % of new cases don't seem to understand the fundamental limitations of ALL the analyses based on the daily data provided by the government - as summarised in this graphic...

2. They thought I didn't understand that the April peak was largely explained because then it was mainly hospitalized people being tested. As I work on CAUSAL modelling and analysis we were highlighting this very problem back in March ... theconversation.com/coronavirus-co…

We have produced numerous reports exposing the flaws of much published statistical analysis on Covid because of the failure to account of causal factors and explanations. My blog post from last week provides some context and links to much of this work. probabilityandlaw.blogspot.com/2020/10/why-we…

Read 6 tweets

Prof Norman Fenton

@profnfenton

17 Oct 20

1. (This is a corrected version of tweet I just deleted). Some perspective on #CovidUK hospital admissions. Here's an update of another graph (using data from coronavirus.data.gov.uk) that never seems to be shown. Daily number of new covid admissions as % of new cases.

2. So 87% of 'cases' at March peak resulted in hospital admissions. Now it's just 4.6% and is actually decreasing. And that's despite: increased repeat hospital testing now (so more false positives), and hospital admissions always go up in October.

3. And note: a person entering hospital with a non-Covid condition who, for example, tests positive after 3 weeks and several negative tests, is counted as a Covid admission.

Read 8 tweets

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Share this page!

Prof Norman Fenton

Try unrolling a thread yourself!

More from @profnfenton

Prof Norman Fenton

Prof Norman Fenton

Prof Norman Fenton

Prof Norman Fenton

Did Thread Reader help you today?

Like this author's thread?