But we shouldn’t look at models to give us the “answers.” How many people will be hospitalized, how many people will die, and so on. That’s our natural, lazy inclination. Instead we should look to the models to show us how to change the answers. That’s why they are important, and why it is so important that those models a) accept uncertainty, and b) are based on the best data we can obtain. The model is not a prophet. It is simply a tool to help us understand the biology of what is happening, and to help us figure out what we have to do next.

And I loved the squirrel-counting analogy!

I heard in an interview, probably with Dr. Jay Bhattacharya, that there is a proposed plan to do the antibody testing in cooperation with the NFL.

As I understand it, they would not test the players but instead office and stadium staff. This would give a quick view into the antibody situation for that many US cities. There would even be an element of apples-to-apples comparison. The participants would not be selected as in a rigorous scientific study but there is some value in having people who are doing the same jobs but in different metro areas.

100% on that link! Thanks so much.

Why would readership not be a valid KPI for a newspaper editor?

Thanks!

I must have run across that idea surfing the web earlier.

Dr. Birx said something about that issue a day or two ago that I found curious. She posed a hypothetical where 1% of the entire population had been infected sometime in the past or currently; and the officials randomly sampled them with an antibody test that is 99% accurate. She said you might end up with a result where 50% of the “tested positive” group were actually false positives.

So her preference is to do random sampling of frontline healthcare workers, where you know the “tested positive” group will be much larger than 1% of the test group.
______

I guess I can accept that the hypothetical could actually happen. I’d counter that even if the end result is incorrect by a factor of 2, at least it would not be off by a factor of ten. We don’t even know the correct order of magnitude now (power of ten). And if she wants to test frontline healthcare workers, then the result will pertain only to them, and yield no information on the population at large.

Also, I would think that since the “tested positive” group is a very much smaller group compared to the original test group, they could be re-tested with a more definitive test.

That was addressed in the Uncommon Knowledge interview with Dr. Jay Bhattacharya. of Stanford Univ. and it was one of his recommendations.

He was the lead in the Santa Clara CA antibody testing study that was the first to show the high rates of infection (asymptomatic) and consequently much lower fatality rate 0.0015 (a bit worse than a bad flu year).

Anyway cited by Powerlineblog.com
https://www.powerlineblog.com/archives/2020/04/an-update-from-dr-b.php
The video of the Uncommon Knowledge interview is

https://youtu.be/k7v2F3usNVA

The Uncommon Knowledge interview is a free download available here:

https://www.podbean.com/site/EpisodeDownload/PBD98DF5P5G8U

Since YouGov, Rasmussen, IPSOS, HarrisX etc. can conduct random polls to determine political popularity …
isn’t there some way to conduct random testing to determine the APPROXIMATE percentage of Wuhan Virus infections in the US?

The polls always give a ‘margin of error’ so it would not be absolutely correct … but wouldn’t it give us some idea of the spread of the virus?