title: Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.
abstract: OBJECTIVE
  We discuss the use of structural models for the analysis of biosurveillance related data.
  
  
  RESULTS
  Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm.
  
  
  CONCLUSIONS
  Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.
authors.affiliation: Health Effects and Medical Response Group, Applied Research Associates, Inc, Arlington, VA 22203-1729, USA. kcheng@ara.com
authors.forename: Karen Elizabeth
authors.initials: KE
authors.lastname: Cheng
authors.forename: David J
authors.initials: DJ
authors.lastname: Crary
authors.forename: Jaideep
authors.initials: J
authors.lastname: Ray
authors.forename: Cosmin
authors.initials: C
authors.lastname: Safta
mesh_terms: D000465:Algorithms; D000881:Anthrax; D055877:Biosurveillance / Q000379:methods*; D023482:Bioterrorism; D004196:Disease Outbreaks; D006801:Humans; D008954:Models, Biological; D012372:ROC Curve