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

TL;DR

This paper compares three decision-making methodologies—Bayesian, info-gap, and imprecise probability—for effectively detecting infectious diseases in animal herds under severe uncertainty, extending existing models to improve testing strategies.

Contribution

It introduces a novel comparison of three decision methodologies in the context of disease detection under uncertainty and links info-gap solutions to imprecise probability models.

Findings

All info-gap solutions are maximal within a certain imprecise probability framework.

The set of optimal testing options can often be inferred from info-gap analysis.

The study extends existing models for invasive species detection to infectious disease testing.

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.