Enhancing time-series detection algorithms for automated biosurveillance

• Published in: Emerging infectious diseases Vol. 15; no. 4; pp. 533 - 539
• Main Authors: Tokars, Jerome I, Burkom, Howard, Xing, Jian, English, Roseanne, Bloom, Steven, Cox, Kenneth, Pavlin, Julie A
• Format: Journal Article
• Language: English
• Published: Centers for Disease Control and Prevention 01.04.2009
• Subjects: algorithms; data analysis; outbreak detection; statistics; Surveillance
• ISSN: 1080-6040; 1080-6059
• DOI: 10.3201/eid1504.080616

BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data.