[show abstract][hide abstract] ABSTRACT: Heightened concerns about bioterrorism are forcing changes to the traditional biosurveillance-model. Public health departments are under pressure to follow multiple, non-specific, pre-diagnostic indicators, often drawn from many data sources. As a result, there is a need for biosurveillance systems that can use a variety of analysis techniques to rapidly integrate and process multiple diverse data feeds using a variety of problem solving techniques to give timely analysis. To meet these requirements, we are developing a new system called BioSTORM (Biological Spatio-Temporal Outbreak Reasoning Module).
[show abstract][hide abstract] ABSTRACT: this paper, we present an argument for knowledge-based surveillance, describe a prototype of the BioSTORM system, and show an initial evaluation of this system applied to a simulated epidemic from a bioterrorism attack
[show abstract][hide abstract] 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.