title: Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding.
abstract: The goals of automated biosurveillance systems are to detect disease outbreaks early, while exhibiting few false positives. Evaluation measures currently exist to estimate the expected detection time of biosurveillance systems. Researchers also have developed models that estimate clinician detection of cases of outbreak diseases, which is a process known as clinical case finding. However, little research has been done on estimating how well biosurveillance systems augment traditional outbreak detection that is carried out by clinicians. In this paper, we introduce a general approach for doing so for non-endemic disease outbreaks, which are characteristic of bioterrorist induced diseases, such as respiratory anthrax. We first layout the basic framework, which makes minimal assumptions, and then we specialize it in several ways. We illustrate the method using a Bayesian outbreak detection algorithm called PANDA, a model of clinician outbreak detection, and simulated cases of a windborne anthrax release. This analysis derives a bound on how well we would expect PANDA to augment clinician detection of an anthrax outbreak. The results support that such analyses are useful in assessing the extent to which computer-based outbreak detection systems are expected to augment traditional clinician outbreak detection.
authors.affiliation: Intelligent Systems Program, University of Pittsburgh, 5113 Sennott Square, 210 S. Bouquet St., Pittsburgh, PA 15260, USA. shenyn@cbmi.pitt.edu
authors.forename: Yanna
authors.initials: Y
authors.lastname: Shen
authors.forename: Christina
authors.initials: C
authors.lastname: Adamou
authors.forename: John N
authors.initials: JN
authors.lastname: Dowling
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D023482:Bioterrorism / Q000517:prevention & control; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data*; D015991:Epidemiologic Measurements; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics / Q000379:methods*