4 ms·
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
by rplnt 7y ago
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 7y agoThat works well for low infection rates https://www.biorxiv.org/content/10.1101/2020.04.03.024216v1.article-info https://www.biorxiv.org/content/10.1101/2020.04.03.024216v1.... https://www.medrxiv.org/content/10.1101/2020.04.05.20054445v1 https://www.medrxiv.org/content/10.1101/2020.04.05.20054445v...
- douglaswlance 7y agoYou can do a binary search to efficiently locate the positive sample.
- ced 7y agoBut what if there are two (or more?) positive samples? It's an interesting problem.
- bequanna 7y agoIf 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 7y agoYeah, 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.
- gnulinux 7y agoYou keep binary searching each part. I.e. if both 32/32 subsamples turn positive, you perform binary search on those 32 samples.
- timerol 7y agoNote 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.