Originally posted by SueEllen
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Previously on "Coronavirus: Follow virus advice or 'tougher measures' likely, says PM"
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Originally posted by scooterscot View PostAlways amazed at the number of blokes that walk out of the public loo without washing their hands. Filthy buggers.
Was at services along the M4 a few months back, practically had the sinks to myself.
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In Leeds:
For the last 4 years Johnson, Gove and Raab have been telling the British people that;
1. They shouldn't listen to experts
2. They should not follow the example of other countries
3. They are special
So why are they surprised when the British people;
1. Ignore experts
2. Don't follow the example of other countries
3. Think they are special
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Originally posted by d000hg View PostI am surprised the police cannot disperse crowds.
One imagines all the people went out expecting it to be quiet. The thing is you CAN have a heck of a lot of people outside maintaining 2m distance (over 1000 can fit in a football pitch!) but you inevitably get pinch points as they enter and exit.
A looped walk could be very busy if you could stop people bunching in the car-parks.
Best is just find your nearest footpath, not some national beauty spot.
Also as you said, the next day we choose a route that runs alongside the sewage treatment plant and there were considerably fewer people.
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But actually an overall IFR or CFR is less important than an age stratified one. Basically under 50, death rates are similar to normal flu. Above 50 they're most certainly not.
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Originally posted by sasguru View PostAlso the S. Korea example is informative, but they had very sophisticated techniques to stop spread, this has CFRS:
https://www.ijidonline.com/article/S...150-8/fulltext
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Originally posted by sasguru View PostNot my area either and for obvious reasons of speed not many peer reviewed papers out yet. Obviously everything is an informed guess but I think 0.5%-1% is very reasonable.
Some indications (albeit from an obviously biased sample: rich 50-somethings healthy enough to go on a cruise, but we have the denom for this unusually, so its a kind of controlled experiment):
Cruise ship outbreak helps pin down how deadly the new coronavirus is | Science News.
But the 1% is quoted by Whitty and I think the Imperial College team have his ear.
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I am surprised the police cannot disperse crowds.
One imagines all the people went out expecting it to be quiet. The thing is you CAN have a heck of a lot of people outside maintaining 2m distance (over 1000 can fit in a football pitch!) but you inevitably get pinch points as they enter and exit.
A looped walk could be very busy if you could stop people bunching in the car-parks.
Best is just find your nearest footpath, not some national beauty spot.
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Originally posted by jamesbrown View PostMy understanding is that the IFR is, by definition, harder to calculate than the CFR precisely because it requires these assumptions about "true" infections.
Where have you seen better or less arbitrary assumptions as the basis for modeling? Genuine question, because this is not my area, but I take an interest from a statistical modeling POV.
Some indications (albeit from an obviously biased sample: rich 50-somethings healthy enough to go on a cruise, but we have the denom for this unusually, so its a kind of controlled experiment):
Cruise ship outbreak helps pin down how deadly the new coronavirus is | Science News.
But the 1% is quoted by Whitty and I think the Imperial College team have his ear.
Also the S. Korea example is informative, but they had very sophisticated techniques to stop spread, this has CFRS:
https://www.ijidonline.com/article/S...150-8/fulltext
And most obviously, this which Ive posted elsewhere:
https://www.imperial.ac.uk/media/imp...16-03-2020.pdfLast edited by sasguru; 23 March 2020, 12:25.
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Originally posted by sasguru View PostHmmm their methodology seems complete arbitrary. IFR of 0.2% based on :
"Therefore, to estimate the IFR, we used the estimate from Germany’s current data 22nd March (93 deaths 23129) cases); CFR 0.40% (95% CI, 0.33% to 0.49%) and halved this for the IFR of 0.20% (95% CI, 0.17% to 0.25%) based on the assumption that half the cases go undetected by testing and none of this group dies"
Let alone the arbitrariness of simply halving, there are severe problems of bias using just German death data - for one its health system probably has the most capacity of any system in the world (largest per capita no of ventilators and ICU beds)
Where have you seen better or less arbitrary assumptions as the basis for modeling? Genuine question, because this is not my area, but I take an interest from a statistical modeling POV.
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Originally posted by jamesbrown View PostGood article here on fatality rates and conditionalities (and uncertainties and unknowns):
https://www.cebm.net/global-covid-19...atality-rates/
"Therefore, to estimate the IFR, we used the estimate from Germany’s current data 22nd March (93 deaths 23129) cases); CFR 0.40% (95% CI, 0.33% to 0.49%) and halved this for the IFR of 0.20% (95% CI, 0.17% to 0.25%) based on the assumption that half the cases go undetected by testing and none of this group dies"
Let alone the arbitrariness of simply halving CFR, there are severe problems of bias using just German death data (which has one of the lowest CFRs to date) - for one its health system probably has the most capacity of any system in the world (largest per capita no of ventilators and ICU beds)Last edited by sasguru; 23 March 2020, 12:08.
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Originally posted by sasguru View PostThe 1% is a figure that most epidemiologists have arrived at as a reasonable figure, when all the dust has settled.
The WHO figure of 3.4% is too high.
In part based on analysis of Diamond Princess where the denom was known.
I've read several academic papers on this, I suggest you do the same.
https://www.cebm.net/global-covid-19...atality-rates/
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Originally posted by Whorty View PostShow us your workings and assumptions then, rather than just posting soundbites and getting all hissy-fitty. Is this what you're like at work when you come up with some analysis and people want to know how you got there?
The WHO figure of 3.4% is too high.
In part based on analysis of Diamond Princess where the denom was known.
I've read several academic papers on this, I suggest you do the same.
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