TY - JOUR AU - Tokars, Jerome I. AU - Burkom, Howard AU - Xing, Jian AU - English, Roseanne AU - Bloom, Steven AU - Cox, Kenneth AU - Pavlin, Julie A. T1 - Enhancing Time-Series Detection Algorithms for Automated Biosurveillance T2 - Emerging Infectious Disease journal PY - 2009 VL - 15 IS - 4 SP - 533 SN - 1080-6059 AB - 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. KW - Surveillance KW - algorithms KW - statistics KW - data analysis KW - outbreak detection KW - research KW - United States DO - 10.3201/eid1504.080616 UR - https://wwwnc.cdc.gov/eid/article/15/4/08-0616_article ER - End of Reference