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

Troffaes, Matthias C. M. and Gosling, John Paul (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., 53 (8). pp. 1271-1281.


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.

Item Type:Article
Additional Information:
Keywords:Exotic disease, Lower prevision, Info-gap, Maximality, Minimax, Robustness.
Full text:(AM) Accepted Manuscript
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Publisher statement:NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Approximate Reasoning. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Approximate Reasoning, 53, 8, 2012, 10.1016/j.ijar.2012.06.020.
Date accepted:No date available
Date deposited:31 October 2014
Date of first online publication:November 2012
Date first made open access:No date available

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