Buckeridge David L, Musen Mark A, Switzer Paul, Crubézy Monica
Palo Alto Veterans Health Care, CA, USA.
AMIA Annu Symp Proc. 2003;2003:120-4.
Public health surveillance is changing in response to concerns about bioterrorism, which have increased the pressure for early detection of epidemics. Rapid detection necessitates following multiple non-specific indicators and accounting for spatial structure. No single analytic method can meet all of these requirements for all data sources and all surveillance goals. Analytic methods must be selected and configured to meet a surveillance goal, but there are no uniform criteria to guide the selection and configuration process. In this paper, we describe work towards the development of an analytic framework for space-time aberrancy detection in public health surveillance data. The framework decomposes surveillance analysis into sub-tasks and identifies knowledge that can facilitate selection of methods to accomplish sub-tasks.
公共卫生监测正在发生变化，以应对生物恐怖主义问题，这增加了对流行病早期检测的压力。快速检测需要跟踪多个非特异性指标并考虑空间结构。没有一种单一的分析方法能够满足所有数据源和所有监测目标的所有这些要求。必须选择和配置分析方法以满足监测目标，但没有统一的标准来指导选择和配置过程。在本文中，我们描述了为公共卫生监测数据中的时空异常检测开发分析框架的工作。该框架将监测分析分解为子任务，并识别有助于选择完成子任务方法的知识。
