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When animals are transported and pass through customs, some of them may havedangerous infectious diseases. Typically, due to the cost of testing, not allanimals are tested: a reasonable selection must be made. How to testeffectively whilst avoiding costly disease outbreaks? First, we extend a modelproposed in the literature for the detection of invasive species to suit ourpurpose. Secondly, we explore and compare three decision methodologies on theproblem at hand, namely, Bayesian statistics, info-gap theory and impreciseprobability theory, all of which are designed to handle severe uncertainty. Weshow that, under rather general conditions, every info-gap solution is maximalwith respect to a suitably chosen imprecise probability model, and thattherefore, perhaps surprisingly, the set of maximal options can be inferred atleast partly---and sometimes entirely---from an info-gap analysis.

Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty