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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 2

Mean absolute residual, by method and dataset, for selected BioSense data used in algorithm modification study*

Stratification of baseline by weekday vs. weekend Mean absolute residual
Department of Defense
 Hospital emergency department
Count Rate Count Rate
Unstratified 4.2 2.4 2.2 2.0
Stratified 2.4 2.2 2.3 2.0

*The count method uses only numerator data; the rate method uses numerator and denominator data. Because varying the baseline duration did not affect residuals (data not shown), all calculations shown here use a baseline duration of 7 days.

Main Article

Page created: December 10, 2010
Page updated: December 10, 2010
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The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
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