Generous Nicholas, Margevicius Kristen J, Taylor-McCabe Kirsten J, Brown Mac, Daniel W Brent, Castro Lauren, Hengartner Andrea, Deshpande Alina
Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
PLoS One. 2014 Jan 29;9(1):e86601. doi: 10.1371/journal.pone.0086601. eCollection 2014.
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.
《国家生物监测战略》将生物监测定义为“收集、整合、解读和传达与影响人类、动物或植物健康的所有危害威胁或疾病活动相关的基本信息的过程，以实现早期发现和预警，有助于全面了解事件健康方面的态势，并在各级实现更好的决策”。然而，该战略并未具体说明如何识别“基本信息”并将其整合到当前的生物监测工作中，也未说明哪些指标可将信息界定为“基本”信息。数据流识别和选择问题需要一种结构化方法，该方法能够系统地评估众多需要考虑的标准之间的权衡。多属性效用理论作为一种多标准决策分析方法，能够提供一种定义明确的结构化方法，为这一问题提供解决方案。虽然多属性效用理论作为一种将正式科学决策理论方法应用于复杂多标准问题的实用方法已在多个领域得到证明，但该方法从未应用于生物监测的决策支持。我们开发了一个形式化的决策支持分析框架，该框架有助于识别用于生物监测系统或流程的“基本信息”，并将此框架作为优化生物监测工作的工具提供给全球生物监测界。为证明其效用，我们将该框架应用于评估用于综合全球传染病监测系统的数据流问题。
