Covid numbers yet again (it’s been a while)

As ever, the raw data is taken from UK Government Stats and the numbers smoothed with a rolling 7 day average.

Overall Health Numbers (from positive tests to deaths)

Testing Numbers (performed and positive)

Positive testing rate (+ve tests against tests taken), with second line removing estimated (based on 0.32%) false positives on Lateral Flow Tests (LFT)

Positive test percentage since Feb 1st, 2021 AND positive test percentage allowing for a 0.32% false positive rate on LTF

Testing volumes and the +ve test rate.

And finally – a new chart – an attempt to map the % transitioning from infection to hospital, to ICU and death with a time lag in the numbers

Covid Uncategorized

Covid Numbers

I have an enquiring mind.  Which can be trouble.  Tired of unsatisfactory reporting in the various media open to me, I decided to have a look myself, and came up with these.  My data source was – you can go there and download your own data.  NB – the historical statistics change as information is updated, so you need to update more than just \”today\’s\” data to get an accurate image.
The first tracks the daily health stats (hospitalisation, ICU admissions and deaths) against the number of positive tests reported.  NOTE the red line is plotted against the right axis, all the others the left.
In red are the positive test numbers.  As you no doubt are very aware, a lot of government and media communication has been based on how many positive tests have been reported. Setting aside arguments about how many false positives there are, and the accuracy of the tests being used; there is a clear message in the this graph.  From about the beginning of September (when schools went back, and Universities started to go back) there has been a massive increase in positive tests, BUT the curves for admissions and ICU have matched it albeit less steeply.  For me this means whilst the focus on raw positive test numbers was inappropriate – it did indicate an underlying increase in transmission that led to ill health.  I think the language about tests means many infer that a positive test means someone is ill.  That is clearly not the case.
The other thing you can see is that the hospitalisation, ICU and death statistics were tracking the spring numbers to some degree when tiering and then lockdown 2 came.  It wasn\’t just positive test results that triggered government action.

The second chart plots just testing data – capacity, tests performed, and positive tests (with all the caveats above repeated on what is a \”positive\”).  The red line is plotted against the right axis, the others the left.

For me the interesting data is whilst testing has grown in September and October, and grown a lot – the increase in positive tests has increased much than in proportion to testing.  Ignoring false positives, this shows that the infection (as measured by these tests) has dramatically increased in those months.

I have few other conclusions I want to draw or investigate.  But I\’d add – I would to see false positives at least approximated in this data.


Covid Uncategorized

Covid Risk Factors and mitigation – an update

In I set out a bunch of assumptions and numbers for the risk of meeting someone with Covid.

With the area numbers for positive cases here announced as 6.9 (the number top right in the spreadsheet) per 100,000 I decided to revise it.

Same premises/assumptions, new numbers

Covid Uncategorized

Covid risk factors and face coverings

Before I discuss this, the attached image contains the overall spreadsheet and most assumptions are detailed on the page.  I don’t declare any infallibility (papal or otherwise), but if you think I’ve made a mistake contact me and I’ll upload a corrected version (if required).
I’m not drawing any specific conclusions on what our behaviour should now be, I did this to consider my own risk level for venturing out so that I could do so on a more scientific basis rather than gut reaction.
I used the following assumptions/default values:
  1. The infection rate is 1 person in 3,900 (the last one i saw)
  2. You need face to face exposure of 15 minutes to risk transmission of the infection
  3. Whilst the face covering mitigation %\’s that have been circulating for some time are not proven, I have used heavily moderated values to explore the impact of face coverings
  4. I\’ve ignored the risks (and maths) of meeting more than 1 carrier of C-19 in any one day
  5. I have ignored the fact that infection is not evenly distributed around the country, these numbers do not apply to hot spots.
  6. Equally, these numbers overestimate the risk in cold spots (if that is the opposite of hot spot!)
  7. This does not consider behaviour patterns whilst out – if you go to the pub and hug all your mates, then your risk likely increases – the behaviour assumed is that social distancing is observed.
  8. This ignores whether the exposure is indoors or outdoors
  9. The percentages shown don’t actually demonstrate the chance that you will get a C-19 infection, they show the risk of you participating in a face to face meeting where the risk of transmission can occur.  So given transmission is not certain, this means the risk % overstate the chance of you (on average) catching C-19 from that contact.
  10. Finally, even if the event does transmit C-19 to you, it says nothing about the risks of you falling ill (seriously or not) with C-19

The calculation in the second column is done from this logic:
  1. The risk of someone being infected is 1/3900 – call this X. In percentage terms this is 0.00000256410256
  2. The chance of any 1 person you meet being infection free is (1- X) which is 0.99999743589744 or 99. 999743589744%, call this Y
  3. The chance of all the people you meet being infection free is therefore the value in Y multiplied by itself once for each person, so for 10 people all to be infection free is Y*Y*Y*Y*Y*Y*Y*Y*Y*Y or Y^10.  Which is roughly 99.744%.
  4. Which means the probability of 1 or more people you meet being infect is 100%-99.744% which is my calculated 0.26%.

Image from calculation spreadsheet