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

• Published in: International journal of approximate reasoning 2012-11, Vol.53 (8), p.1271-1281, Article 1271
• Main Authors: Troffaes, Matthias C.M., Gosling, John Paul
• Format: Article
• Language: English
• Subjects: Applied sciences; Decision theory. Utility theory; Exact sciences and technology; Exotic disease; Info-gap; Lower prevision; Mathematics; Maximality; Minimax; Operational research and scientific management; Operational research. Management science; Probability and statistics; Probability theory and stochastic processes; Probability theory on algebraic and topological structures; Robustness; Sciences and techniques of general use
• ISSN: 0888-613X; 1873-4731
• DOI: 10.1016/j.ijar.2012.06.020

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, and we discuss the main sources of model uncertainty, many of which are hard to quantify. 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.