Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
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

Page created: December 10, 2010
Page updated: December 10, 2010
Page reviewed: December 10, 2010
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.
file_external