Enhancing Time-Series Detection Algorithms for Automated Biosurveillance
Jerome I. Tokars , 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)
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.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions 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.