Selecting essential information for biosurveillance--a multi-criteria decision analysis.
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 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". The question 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.
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Nicholas
N
Generous
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Kristen J
KJ
Margevicius
Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Kirsten J
KJ
Taylor-McCabe
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Mac
M
Brown
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
W Brent
WB
Daniel
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Lauren
L
Castro
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Andrea
A
Hengartner
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Alina
A
Deshpande
D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D003663:Decision Trees; D018563:Disease Notification; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D006801:Humans