• Jeff Ely

Optimal Test Allocation

Joint with Jakub Steiner and Andrea Galeotti


A health authority chooses a binary action for each of several individuals that differ in their pre-test probabilities of being infected and in the additive losses associated with two types of decision errors. The authority is endowed with a portfolio of tests that differ in their sensitivities and specificities. We derive a simple necessary condition for optimality of test allocation. In special cases, precision parameters of the allocated test are monotone in the individuals’ types. We characterize the marginal benefit of a test and provide an algorithmic solution for the test-allocation problem.


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