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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

Figure 1

Distribution of syndrome counts, by day of week and data source, for selected BioSense data used in algorithm modification study. Black bars show Department of Defense data, and white bars show hospital emergency department data.

Figure 1. Distribution of syndrome counts, by day of week and data source, for selected BioSense data used in algorithm modification study. Black bars show Department of Defense data, and white bars show hospital emergency department data.

Main Article

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