[rplnt]

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 positives to expect.

[buboard]

That works well for low infection rates

https://www.biorxiv.org/content/10.1101/2020.04.03.024216v1....

https://www.medrxiv.org/content/10.1101/2020.04.05.20054445v...

[douglaswlance]

You can do a binary search to efficiently locate the positive sample.

[timerol]

Note that optimal branch factor would probably depend on the expected negative rate. If 98% of your samples are coming back negative, it may be more optimal to divide by into 4 or 8 groups instead of 2 groups.

[ced]

But what if there are two (or more?) positive samples? It's an interesting problem.

[bequanna]

If the initial test size is 64 and contains a positive, split into two groups of 32 and test each. Continue splitting and testing when a positive is encountered.

[ced]

Yeah, but this version is not necessarily optimal. If you have 4 positive samples, then you will test 2+2+1+1+1+1 = 8, instead of 4 in the naive case.

It feels like a problem for information theory.

[bequanna]

I didn’t say it was optimal, but it is a huge improvement on doing 64 tests for 64 samples.

[ced]

Oh, sure!

[gnulinux]

You keep binary searching each part. I.e. if both 32/32 subsamples turn positive, you perform binary search on those 32 samples.