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

Troffaes, Matthias C.M.; Gosling, John Paul

Abstract

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.

Citation

Troffaes, M. C., & Gosling, J. P. (2012). Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 53(8), 1271-1281. https://doi.org/10.1016/j.ijar.2012.06.020

• Journal Article Type: Article
• Publication Date: Nov 1, 2012
• Deposit Date: Mar 12, 2013
• Publicly Available Date: Oct 31, 2014
• Journal: International Journal of Approximate Reasoning
• Print ISSN: 0888-613X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 53
• Issue: 8
• Pages: 1271-1281
• DOI: https://doi.org/10.1016/j.ijar.2012.06.020
• Keywords: Exotic disease, Lower prevision, Info-gap, Maximality, Minimax, Robustness.
• Public URL: https://durham-repository.worktribe.com/output/1466757