[colechristensen]

I don't believe this is a reliable metric.

Who gets tested is a moving target. Stanford a short time ago did a free-for-all testing binge in order to collect data, but finished that and is now restricting tests to people requiring specific risk factors to give a test.

The first time I tried to get a test from another provider I just wasn't able, they didn't know of anywhere that would test me outside of hospitalization-type symptoms.

So testing is uneven and not very available, any stats need to include some metric for the criteria to get tests in the first place.

In other words, there is likely an enormous population with no symptoms or mild symptoms who couldn't get tested if they tried.

After two video appointments with separate providers I was able to get tested yesterday and the result came back negative about 22 hours later. It took me about 8 hours of effort and time to get that done, a luxury many people do not have.

[majormajor]

> After two video appointments with separate providers I was able to get tested yesterday and the result came back negative about 22 hours later. It took me about 8 hours of effort and time to get that done, a luxury many people do not have.

Is there any value in people self-selecting into personal choice testing? You could get infected tomorrow, for instance...

If we wanted a full picture of community spread we'd need a top-down random sample, not self-selection, no?

[colechristensen]

Aren't there ways of turning self-selection populations into random sample populations for statistical purposes? (It has just been a while since I have had to think of these things).

But really we want more than just accurate statistics, we want to minimize damage. Any increase in testing is good testing, and triaging testing to highest risk individuals makes sense when your capacity is limited.

The consequences though are that reported statistics are often just wrong. Skewed towards higher negative outcomes and comparisons between dates are flawed without much additional information.

[Jommi]

It's one of the most reliable metrics we have, and a lot better than just tests or just confirmed cases.

After you have this info, you can compare the rates to what kind of testing policies the areas have, and make some initial conclusions.