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No. of Recommendations: 5
We all hope for the super-duper-contagious low-fatality-ratio theory to be the right one, as it would suggest an early peak and low final death total.
...
I have seen no data that would lead me to think that this is a credible scenario.


I tend to agree. The "Oxford theory" seemed tempting but unconvincing.
Until, and except for, this recent study.

The "excess sickness" population does seem to be very large, around 26m in three weeks.
That's not a count--it's calculated by sampling from 2600 clinicians. But that's a big sample.

The data led them to estimate that there were 7m "excess" new US infections of something flu-like
but not influenza in the stretch March 8-14, compared with 7000 officially counted deaths three weeks later.
Dividing A by B is what suggests the low IFR.
The big unknowns is how many of those "excess for 2020" non-influenza influenza-like illnesses were actually Covid-19, and how many excess deaths are being missed in the official counts.

So, their work doesn't mean it's the high-spread rare-severity scenario, but it is consistent with that interpretation.
Strongly suggestive, say.
I figure it would not be unreasonable to shift one's best guess somewhat in that direction.
Even a little good news would be nice.

This is the paper
https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v...

From the abstract:
"... we estimate the symptomatic case detection rate of SARS-CoV-2 in the US to be 1/100 to 1/1000.
This corresponds to approximately 10 million presumed symptomatic SARS-CoV-2 patients across the US during the week starting on March 15, 2020.
...
We emphasize the importance of testing these findings with seroprevalence data"


I would have thought that somebody out there would be doing sample testing (PCR and serum if possible) of some broad population by now, to get an answer.
If the people were selected truly randomly from the population, it wouldn't take that many tests to get a pretty darned good snapshot estimate.
The biggest problem would be the low positive rate, meaning you need a big sample to catch enough for your percentage positive to have any statistical significance.
But...maybe it wouldn't be so low if the positive rate is high. Don't know till you try.
A quick search suggests this has started in Finland, Ohio and Canberra.

Jim
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