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Pooled testing and information theory

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  • UserDr Matthew Aldridge, University of Leeds World_link
  • ClockWednesday 15 February 2023, 14:00-15:00
  • HouseMR5, CMS Pavilion A.

If you have a question about this talk, please contact Dr Ramji Venkataramanan.

When testing many people for a disease, the traditional method is “individual testing”: a sample (of, perhaps, blood or saliva) is taken from each person, and each sample is analysed to see if it contains the antigen. People who get a positive test have the disease, and people who get a negative test do not have the disease. An alternative is “pooled testing”: we take samples from a few people, mix those samples together in the same test tube, and perform a single test on this mixed sample. If the test on this pooled sample comes back negative, none of the people have the disease; if the test comes back positive, we know that at least one of the people has the disease, but further tests are required to work out which. In some circumstances, pooled testing can find which people have the disease using many fewer tests than with individual testing.

Analysis of pooled testing has often profited from using information theoretic techniques – we will look at two examples in this talk. First, we will see how the analysis of a rare disease with parallel processing of tests is very similar to the classical information theory problem of channel capacity – but has some important differences too. Second, we see how the analysis of a more common disease with serial processing of tests relates to the problem of finding optimal source codes for infinite alphabets.

This talk is part of the Information Theory Seminar series.

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