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

Autor: Andrea Hengartner, Alina Deshpande, Kirsten J. Taylor-McCabe, W. Brent Daniel, M. G. Brown, Nicholas Generous, Kristen Margevicius, Lauren Castro
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Decision support system
Epidemiology
Computer science
Social and Behavioral Sciences
Data Stream
Biosurveillance
Business decision mapping
Medicine
Evaluation
Mathematical Computing
Information Science
Decision Making
Computer-Assisted
General Environmental Science
Multidisciplinary
Decision engineering
Data stream mining
Utility theory
Applied Mathematics
Evidential reasoning approach
Complex Systems
Multiple-criteria decision analysis
Infectious Diseases
Epidemiological Monitoring
Public Health
Algorithms
Research Article
Environmental Monitoring
Data stream
Situation awareness
Clinical Research Design
Science
Decision tree
ISDS 2013 Conference Abstracts
Infectious Disease Epidemiology
Decision Support Techniques
Multi-criteria decision analysis
Humans
Disease Notification
Survey Research
business.industry
Decision Trees
Data science
General Earth and Planetary Sciences
identification
business
Infectious Disease Modeling
Mathematics
Decision analysis
Zdroj: PLoS ONE, Vol 9, Iss 1, p e86601 (2014)
Online Journal of Public Health Informatics
PLoS ONE
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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