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

By P J Bryant

Ramblings of a freelance IT Consultant working for some nice SME's, large organisations, resellers and the usual friends and family! Bit of

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