  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 whilst avoiding costly disease outbreaks? First, we extend a model
proposed in the literature for the detection of invasive species to suit our
purpose. Secondly, we explore and compare three decision methodologies on the
problem at hand, namely, Bayesian statistics, info-gap theory and imprecise
probability theory, all 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.
