There are two purposes of tests.
Individual: To tell whether a particular patient has the virus.
Social: To enable public health officials and policy makers to know the prevalence of the virus in the population.
For the individual purpose, the quantity of tests and the speed with which results can be read matters more than quality. There is hardly any point in testing someone who is unlikely to be infected. And if a hospital uses one type of test on one patient and a different type of test on a different patient, that is hardly a problem.
For the social purpose, the quantity of tests does not matter, as long as enough people are tested to produce a reliable sample. If you have to wait a week for a test result, that is ok. You want to include a representative sample of the entire population, including people without any reason to believe that they have been infected. It is important that every person tested using the same method.
What level of accuracy do you need? Suppose that 95 percent of the people who test positive are in fact positive, and 95 percent of the people who test negative are in fact negative. Is that good enough? Imagine that out of 1000 people, 40 test positive and 960 test negative. You would have:
| test positive | test negative | |
|---|---|---|
| have virus | 38 | 48 |
| virus-free | 2 | 912 |
Do you see the problem? More people who have the virus test negative for it than test positive for it. That is certainly not good for the individual purpose.
For the social purpose, you can back out the true prevalence of the virus provided you know precisely the rate of false positives and false negatives. But if you don’t know those, and if you just go by the test results, in this example you would say that only 4 percent of people have the virus, even though 8.6 percent of people actually have the virus. But at least you would be in the right ballpark. In the absence of rigorous testing, right now the estimates from different “experts” are orders of magnitude apart.
For the individual purpose, I would prefer the testing method with the lowest rate of false negatives. For the social purpose, I would prefer the test where we have the most precise estimate of the false positive and false negative rates, even if the false negative rate is a bit higher than that of some other test.
With pooled testing of the population you can do even better in terms of the number of actual PCR tests that need to be run.
One of Gelman’s collegaues/readers did the Bayesian statistical study design and it only takes 100 PCR plates of 96 wells, each will with 10 mixed samples and at 100 different locations.
https://statmodeling.stat.columbia.edu/2020/03/27/for-the-cost-of-running-96-wells-you-can-test-960-people-and-accurate-assess-the-prevalence-in-the-population-to-within-about-1-do-this-at-100-locations-around-the-country-and-youd-have-a-spatial/
The only test that matters is the antibody test, how many people were infected, therefore what is the denominator on this death rate to justify mass lockdowns. Easily done with random poll like sampling.
Fauci used communist China numbers to reach 1%
This 2 wk old study was 0.1% & dropping. WSJ Stanford doc article was 0.01%
https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/
Dr Kling, can you contact Peter Attia and get antibody testing funded through The Gates Foundation (or another foundation). Without this information how can further lockdowns be supported?
“ To enable public health officials and policy makers to know the prevalence of the virus in the population.”
Is there any consensus on the optimal policies for any prevalence figure? It would be nice if we knew ex ante what the results mean in terms of policy. Ex post, it is just another number justifying one’s preferred policy.