Recently, test pooling has been suggested by the Frankfurt Goethe University where you test a group of nasal samples, all in a single test. If the test is negative this means all the samples in the group (pool) were negative, sparing a lot of unnecessary individual tests.

Unfortunately, the Frankfurt group didn't publish any recommended pool size, so anyone using this approach would make a guess for the pool size and use that, which isn't optimal.

Our simulations show that the best strategy to use for doing a pooled test on COVID-19 varies from context to context, as shown bellow:

What this table essentially says is that for the case of Italy for example, where there is an expected 0.33% infection rate among the full population, the optimal strategy for testing 100 people (cohort size), where you are allowed a maximum of 16 samples per pool, is to test:

first in groups of 15

the remaining people in the positive groups, regrouped in groups of 4

the remaining people in the positive groups, regrouped in groups of 2

the remaining people, individually

So, a 4 step strategy of (15, 4, 2, 1) where you should expect, on average, to find all the infections using only 9.47 tests!

**That means an approximately 10x increase in capacity! (in some cases even 20x)**

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