Testing asymptomqtic people probably will not be done, nor should it be. The reason is that tests are invariably not 100% accurate combined with some basic math.
You can google “conditional probability” if your interested in details, but a simple example (with easy numbers) should help. Suppose you have a population of 1,000,000 people. 5% are expected to be infected and you have a test that’s 90% accurate (I am assuming the same accuracy for both positive and negative results). Now the numbers. 50,000 people infected, 950,000 not infected. Of the infected, 45,000 will test positive, of the uninflected, 95,000 will also test positive.
This means there will be 190,000 reported cases in a population with only 50,000 actual cases. Further, only 45,000 of the 130,000 reported cases would actually have the disease. That means testing everyone in our hypothetical population would yield a positive test accuracy of only 34.6%.
While this was only a hypothetical example, you could well imagine the panic that might ensue were such numbers released. Very few would understand the underlying math and (even more) panic would erupt. Poor olive decisions would result from such misleading data. The solution, of course, is to limit the number of false positives by increasing the probability that those tested actually are infected. Hence testing of only those who meet the screening criteria.
I appreciate your response.
Thanks! Very informative. Do you have any idea on just how accurate the current test for CV19 in the US is? I had read that the initial test was very flawed.