Selecting essential information for biosurveillance--a multi-criteria decision analysis.

Area of Science:

• Public Health
• Decision Science
• Infectious Disease Surveillance

Background:

• The National Strategy for Biosurveillance lacks a defined method for identifying "essential information."
• Current biosurveillance lacks systematic evaluation of data streams and their associated tradeoffs.
• Effective biosurveillance requires robust methods for data stream selection and integration.

Purpose of the Study:

• To develop a formalized decision support analytic framework for identifying "essential information" in biosurveillance.
• To address the need for a structured methodology in data stream identification and selection for biosurveillance.
• To offer a tool for optimizing the global biosurveillance enterprise.

Main Methods:

• Application of Multi-Attribute Utility Theory (MAUT), a multi-criteria decision analysis approach.
• Development of a formalized decision support analytic framework.
• Evaluation of data streams for an integrated global infectious disease surveillance system using the developed framework.

Main Results:

• A formalized decision support analytic framework was developed to identify "essential information" for biosurveillance.
• The framework provides a structured approach to systematically evaluate tradeoffs between various criteria for data stream selection.
• Demonstrated utility of the framework in evaluating data streams for global infectious disease surveillance.

Conclusions:

• Multi-Attribute Utility Theory offers a viable solution for the challenge of identifying and selecting essential information in biosurveillance.
• The developed framework can enhance the optimization of biosurveillance systems and processes globally.
• This approach facilitates better decision-making in public health by improving situational awareness through essential data integration.