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Volume 15, Number 4—April 2009

Research

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

Jerome I. TokarsComments to Author , Howard Burkom, Jian Xing, Roseanne English, Steven Bloom, Kenneth Cox, and Julie A. Pavlin
Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA (J.I. Tokars, J. Xing, R. English); The Johns Hopkins University, Baltimore, Maryland, USA (H. Burkom); Science Applications Incorporated, San Diego, California, USA (S. Bloom); Department of Defense, Washington, DC, USA (K. Cox); Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand (J.A. Pavlin)

Main Article

Table 3

Sensitivity for detection of additional counts, by method and dataset, for selected BioSense data used in algorithm modification study*

Minimum SD Stratified baseline Baseline duration, d Sensitivity
Department of Defense
Hospital emergency department
Count Rate Count Rate
0.2 No 7 40.6† 43.9 40.2† 39.1
1.0 No 7 52.3 70.8 50.4 53.6
1.0 No 14 58.6 76.8 58.7 60.9
1.0 No 28 62.0 79.4 62.8 64.8‡
1.0 Yes 7 64.9 75.7 50.2 53.8
1.0 Yes 14 75.1 80.4 57.6 60.1
1.0 Yes 28 77.0 82.0‡ 60.5 62.1

*All facility–syndrome days were included in calculations. The number of additional counts varied according to categories of average count for each facility–syndrome (0.5–<2, 2–<4, 4–<6, 6–<8, 8–<10, 10–<20, 20–<40, and >40) to produce 40% sensitivity for the initial method. For the Department of Defense, the additional counts were 5.0, 9.1, 11.7, 13.6, 16.0, 20.9, 30.4, and 40.0 for the average count categories, respectively. For the hospital emergency departments, the additional counts were 4.3, 6.3, 8.2, 9.5, 10.4, 12.9, 18.7, and 28.2, respectively.
†Initial method.
‡Best method for the dataset.

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