Robust surveillance of animal diseases: an application to the detection of bluetongue disease

Abstract

Control of endemic, exotic, and emerging animal diseases critically depends on their early detection and timely management. This paper proposes a novel approach to evaluate alternative surveillance programs based on info-gap theory. A general modeling framework is developed explicitly accounting for severe uncertainty about the incursion, detection, spread, and control of exotic and emergent diseases. The model is illustrated by an evaluation of bluetongue disease surveillance strategies. Key results indicate that, when available, vaccination of the entire population is the most robust strategy. If vaccines are not available then active reporting of suspect clinical signs by farmers is a very robust surveillance policy.