title: Knowledge-based bioterrorism surveillance.
abstract: An epidemic resulting from an act of bioterrorism could be catastrophic. However, if an epidemic can be detected and characterized early on, prompt public health intervention may mitigate its impact. Current surveillance approaches do not perform well in terms of rapid epidemic detection or epidemic monitoring. One reason for this shortcoming is their failure to bring existing knowledge and data to bear on the problem in a coherent manner. Knowledge-based methods can integrate surveillance data and knowledge, and allow for careful evaluation of problem-solving methods. This paper presents an argument for knowledge-based surveillance, describes a prototype of BioSTORM, a system for real-time epidemic surveillance, and shows an initial evaluation of this system applied to a simulated epidemic from a bioterrorism attack.
authors.affiliation: Medical Informatics, Stanford University, Stanford Medical Informatics, Stanford, CA, USA.
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
authors.forename: Justin
authors.initials: J
authors.lastname: Graham
authors.forename: Martin J
authors.initials: MJ
authors.lastname: O'Connor
authors.forename: Michael K
authors.initials: MK
authors.lastname: Choy
authors.forename: Samson W
authors.initials: SW
authors.lastname: Tu
authors.forename: Mark A
authors.initials: MA
authors.lastname: Musen
mesh_terms: D001185:Artificial Intelligence; D023482:Bioterrorism; D004196:Disease Outbreaks; D006801:Humans; D007256:Information Systems; D011159:Population Surveillance / Q000379:methods*