title: An analytic framework fo space-time aberrancy detection in public health surveillance data.
abstract: 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.
authors.affiliation: Palo Alto Veterans Health Care, CA, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
authors.forename: Mark A
authors.initials: MA
authors.lastname: Musen
authors.forename: Paul
authors.initials: P
authors.lastname: Switzer
authors.forename: Monica
authors.initials: M
authors.lastname: Crubézy
mesh_terms: D023482:Bioterrorism; D003140:Communicable Disease Control; D003141:Communicable Diseases / Q000175:diagnosis* / Q000453:epidemiology; D004196:Disease Outbreaks; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D013029:Space-Time Clustering