title: Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales.
abstract: The recent series of anthrax attacks has reinforced the importance of biosurveillance systems for the timely detection of epidemics. This paper describes a statistical framework for monitoring grocery data to detect a large-scale but localized bioterrorism attack. Our system illustrates the potential of data sources that may be more timely than traditional medical and public health data. The system includes several layers, each customized to grocery data and tuned to finding footprints of an epidemic. We also propose an evaluation methodology that is suitable in the absence of data on large-scale bioterrorist attacks and disease outbreaks.
authors.affiliation: Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA 15213-3890, USA.
authors.forename: Anna
authors.initials: A
authors.lastname: Goldenberg
authors.forename: Galit
authors.initials: G
authors.lastname: Shmueli
authors.forename: Richard A
authors.initials: RA
authors.lastname: Caruana
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Fienberg
mesh_terms: D000881:Anthrax / Q000453:epidemiology* / Q000517:prevention & control; D023482:Bioterrorism / Q000517:prevention & control*; D004189:Disaster Planning; D004196:Disease Outbreaks; D006295:Health Resources; D015233:Models, Statistical; D004366:Nonprescription Drugs; D011159:Population Surveillance; D011634:Public Health / Q000379:methods; D013997:Time Factors
chemical_list: D004366:Nonprescription Drugs