Abstract
The National Strategy for Biosurveillancedefines 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 does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.
Highlights
As defined in the National Strategy [1], biosurveillance is ‘‘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 decisionmaking at all levels.’’ The systems and processes that constitute the biosurveillance (BSV) enterprise rely on a wide range of data that encompass human, animal, and plant health
Using Multi-Attribute Utility Theory (MAUT), we introduce a universal framework for evaluating biosurveillance data streams that can assess multiple attributes, both quantitative and qualitative, and that linksthese attributes to a specific biosurveillance objective in order to provide a comprehensive and robust evaluation
While the application of MAUT to evaluate specific data streams is the ultimate goal of the framework, this paper focused on broad categories of data streams in order to focus on the development of the framework, laying the foundation for its eventual application to specific data stream evaluation
Summary
As defined in the National Strategy [1], biosurveillance is ‘‘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 decisionmaking at all levels.’’ The systems and processes that constitute the biosurveillance (BSV) enterprise rely on a wide range of data that encompass human, animal, and plant health. There is a recognized need for better methods and techniques within the biosurveillance community that would enable practitioners and system developers to prioritize and select the ‘best’ data streams for a biosurveillance system’s specific intended use In part, this is due to a lack of reliable and tested evaluation methods and criteria for evaluation. The same metrics that are important for early detection (e.g. timeliness, time to detection, etc.) may not be as useful for another goal such as consequence management.Both the use of few attributes and the lack of explicitly defined biosurveillance objectives represent significant barriers to effective evaluation Without these issues being addressed, it is not possible to have an unbiased and completeevaluation framework. Using MAUT, we introduce a universal framework for evaluating biosurveillance data streams that can assess multiple attributes, both quantitative and qualitative, and that linksthese attributes to a specific (or defined) biosurveillance objective in order to provide a comprehensive and robust evaluation. Proof of principle for the developed decision criteria framework is demonstrated by showing its applicability towards the evaluation of broad categories of data streams for inclusion in an integrated global infectious disease surveillance system
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