title: The Bayesian aerosol release detector: an algorithm for detecting and characterizing outbreaks caused by an atmospheric release of Bacillus anthracis.
abstract: Early detection and characterization of outdoor aerosol releases of Bacillus anthracis is an important problem. As health departments and other government agencies address this problem with newer methods of surveillance such as environmental surveillance through the BioWatch program and enhanced medical surveillance, they increasingly have newer types of surveillance data available. However, existing methods for the statistical analysis of surveillance data do not account for recent meteorological conditions, as human analysts did in the case of the Sverdlovsk anthrax outbreak of 1979 to determine whether the locations of victims were consistent with meteorological conditions in the week preceding their onset of illness. This paper describes the Bayesian aerosol release detector (BARD), an algorithm that analyzes both medical surveillance data and meteorological data for early detection and characterization of outdoor releases of B. anthracis. It estimates a posterior distribution over the location, quantity, and date and time conditioned on a release having occurred. We report a proof-of-concept evaluation of BARD, which demonstrates that the approach shows promise and warrants further development and evaluation.
authors.affiliation: The RODS Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA. mailto:wrh9@pitt.edu
authors.forename: William R
authors.initials: WR
authors.lastname: Hogan
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.forename: Garrick L
authors.initials: GL
authors.lastname: Wallstrom
authors.forename: Michael M
authors.initials: MM
authors.lastname: Wagner
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Depinay
mesh_terms: D000336:Aerosols / Q000032:analysis / Q000633:toxicity; D000881:Anthrax / Q000453:epidemiology* / Q000209:etiology; D001408:Bacillus anthracis / Q000302:isolation & purification; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D023482:Bioterrorism; D004189:Disaster Planning; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D006801:Humans; D019570:Inhalation Exposure / Q000009:adverse effects / Q000706:statistics & numerical data*; D015233:Models, Statistical; D011634:Public Health / Q000379:methods; D040442:Public Health Informatics / Q000379:methods; D012131:Respiratory Insufficiency / Q000453:epidemiology / Q000382:microbiology; D018570:Risk Assessment / Q000379:methods; D018571:Sentinel Surveillance; D013029:Space-Time Clustering; D013171:Spores, Bacterial / Q000302:isolation & purification; D014481:United States; D014919:Wind
chemical_list: D000336:Aerosols