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

When animals are transported and pass through customs, some of them may have dangerous infectious diseases. Typically, due to the cost of testing, not all animals are tested: a reasonable selection must be made. How to test effectively, yet avoid cataclysmic events? First, we extend a model proposed in the literature for the detection of invasive species to suit our purpose. Secondly, we explore and compare two decision methodologies on the problem at hand, namely, info-gap theory and imprecise probability theory, both of which are designed to handle severe uncertainty. We show that, under rather general conditions, every info-gap solution is maximal with respect to a suitably chosen imprecise probability model, and that therefore, perhaps surprisingly, the set of maximal options can be inferred at least partly—and sometimes entirely—from an info-gap analysis.