201–210 of 247 posts
Germany decided to mix 10 specimens and run a test, only then - if positive - test individual samples. They increased their testing capacity 10x (roughly) overnight. https://edition.cnn.com/world/live-news/coronavirus-pandemic...
https://hackaday.io/project/27623-coffee-cup-polymerase-chai...
and some synthetic DNA derived from cov2 (seems to cost about $0.09 per base pair)?
Germany decided to mix 10 specimens and run a test, only then - if positive - test individual samples. They increased their testing capacity 10x (roughly) overnight. https://edition.cnn.com/world/live-news/coronavirus-pandemic...
It's only tenfold if they all test negative, right? Otherwise you need to test subgroups to find out which test(s) were positive. So the real speedup is probably something like 2x-4x, depending on infection rates.
Earlier quoted context omitted.
You can do a binary search to efficiently locate the positive sample.
But what if there are two (or more?) positive samples? It's an interesting problem.
Earlier quoted context omitted.
South African here. We are 60m people (plus quite a few immigrants that are not counted) and we have a complete lockdown.
What is a complete lockdown? Food workers don't go to work anymore?
Earlier quoted context omitted.
You are downplaying the value by saying they will be useful in the future, rather than now. This is not so. They are valuable right now.
For what? "Google for answers" isn't very helpful. It sounds like they're saying serological tests are valuable, but not viable for immediately testing a large population.
Earlier quoted context omitted.
Dismissing the value of serological testing seems like a self-serving move. Such tests are immediately of very high value because they allow us to understand immunity to Covid-19, and to actually validate the estimates of cases amongst those who haven’t sought treatment. These are both of huge value regardless of the percentage of the US population estimate to have previously contracted Covid-19. By all means market…
Serological tests are useful and needed right now, not at some future stage. It takes about a month before an antibody test is accurate in someone who has had a virus, so using them now would only detect cases where the person had the virus in early March. That limits the value of serological testing considerably.
Earlier quoted context omitted.
It's only tenfold if they all test negative, right? Otherwise you need to test subgroups to find out which test(s) were positive. So the real speedup is probably something like 2x-4x, depending on infection rates.
Currently in the US ~85% of tests turn negative. If sample turned positive you'd perform 10% more tests (1 extra test for each 10 tests) i.e. x1.1. If it turned negative you'd perform 10% the tests you'd normally perform without this scheme i.e. x0.1. In this case .85 of tests would be .1 of what they're now. The rest of .15 will be 1.1 of what they're now. So if US did this, they would be testing ~x.3 of their curre…
The general formula is (r^x) * (1/x) + (1 - r^x) * (1 + 1/x), where r is the percentage of negative tests and x is the number to mix.
With an 85% negative rate, it's more beneficial to mix 3 tests at a time for 0.72 tests per sample. (2 samples gives 0.78 tests, 4 gives 0.73) As more samples come back negative it becomes more advantageous to mix more, but you shouldn't mix 10 tests at a time (as opposed to 9) till you get to about a 96% negative rate, at which point you're running 0.44 tests per sample.
Edit: The binary search algorithm mentioned elsewhere would probably be more optimal, but I'm gonna do my day job instead of figuring out the dynamics of that one.
Earlier quoted context omitted.
There was a study, IIRC from Israel, which said this is still reliable with 64 samples mixed. The next question is, do you need to test the individual samples then? Not sure how much available material for testing you have, but you might be able to just divide the mix to eliminate bigger groups first. Or even mix parts with new samples. There must be an ideal procedure (throughput-wise) if you know how the range of p…
You can do a binary search to efficiently locate the positive sample.
Earlier quoted context omitted.
Currently in the US ~85% of tests turn negative. If sample turned positive you'd perform 10% more tests (1 extra test for each 10 tests) i.e. x1.1. If it turned negative you'd perform 10% the tests you'd normally perform without this scheme i.e. x0.1. In this case .85 of tests would be .1 of what they're now. The rest of .15 will be 1.1 of what they're now. So if US did this, they would be testing ~x.3 of their curre…
This isn't accounting for combinatorial math. If each sample is negative 85%, then the probability that all 10 samples are negative is 0.85^10, or about 20%. If any sample turned positive (the other 80% of the time), then all will need to be retested. Instead of 1 test per sample, you're now running 0.2 * 0.1 + 0.8 * 1.1, or about 0.9 tests per sample. You've increased throughput by 10%, but not nearly as much as was…