1. This 6 minute video summarises the findings in our new report exposing the "1 in 3 people with Covid-19 have no symptoms" claim:
2. The full report is here:
doi.org/10.13140/RG.2.…
3. And here is a blog post containing a summary of the report: probabilityandlaw.blogspot.com/2021/04/smashi…
4. Key findings for the particular period and location analysed:
- The “1 in 3” claim is a massive exaggeration. At most 1 in 18 with the virus had no symptoms

- Number of 'cases' was exaggerated by at least 80%.
5. But things to note:
“1 in 3 people who have the virus have no symptoms”

is NOT the same as

“1 in 3 people who have no symptoms have the virus”

(assuming they're the same is the fallacy of the transposed conditional)
6. Nor is it the same as

“1 in 3 people who test positive for the virus have no symptoms”

In fact, because of false positive PCR tests, our analysis indicate that this claim was approximately true
Moreover this story today is also likely to be accurate because, as infection rates go down, false positives means the proportion of people testing positive who have no symptoms will increase dailymail.co.uk/news/article-9…

• • •

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

Keep Current with Prof Norman Fenton

Prof Norman Fenton Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

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

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @profnfenton

23 Mar
1. Here is an up-to-date table of results from the Cambridge University #Covid19UK study of asymptomatics since the start of 2021
2. Some key points about it:
3. The explanation of why this means the much repeated Government claim that “1 in 3 people with the virus has no symptoms” is a massive exaggeration was provided in this previous analysis of the Cambridge study: probabilityandlaw.blogspot.com/2021/02/the-ca…
Read 6 tweets
14 Mar
1. This article nicely explains why (because of possible false positives) a positive LFT test result does NOT mean you certainly have Covid. However, it's focus on false negatives (implying we should also be wary of a negative result) is misleading. theguardian.com/theobserver/co…
2. In fact, with the assumptions used in the article and the current results of the Cambridge University study of asymptomatics, it follows that there's ony a 1 in 10,000 chance a person testing negative with LFT will have the virus
3. The results suggest we should NOT be mass testing asymptomatic people. The lastest Cambridge asymptomatic study results are here: cam.ac.uk/sites/www.cam.…
Read 6 tweets
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 16 tweets
27 Feb
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…
Read 6 tweets
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
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

Did Thread Reader help you today?

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!

Follow Us on Twitter!