The National Strategy for Biosurveillance defines biosurveillance as 'the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.' However, the strategy leaves unanswered how 'essential information' is to be identified and integrated, or what the metrics qualify information as being 'essential'. Multi-Attribute Utility Theory (MAUT), a type of multi-criteria decision analysis, provides a structured approach that can offer solutions to this problem. While the use of MAUT has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance. We have developed a decision support analytic framework using MAUT that can facilitate identifying data streams for use in biosurveillance. We applied this framework to the problem of evaluating data streams for use in a global infectious disease surveillance system.
Objective
To describe how multi-criteria decision analysis can be applied to identifying essential biosurveillance information and demonstrate feasibility by applying it to prioritize data streams.