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 3. Comparison of initial versus best algorithms for analysis of fever syndrome data at an example emergency department, October–November 2006. A) SD comparison. Count, fever syndrome counts; SD (initial), SD by using initial algorithm (minimum SD = 0.2, 7-day baseline, count method, unstratified baseline); SD (best), SD by using best algorithm (minimum SD = 1.0, 28-day baseline, rate method, unstratified baseline). B) Count threshold comparison. Count, fever syndrome counts; threshold 1, minimum count needed to trigger an alert by using initial method; threshold 2, minimum count needed to trigger an alert by using best method (for the best algorithm, which accounts for rate, 8 counts were added to total visits for calculating the threshold). C) Detection of 8 additional counts. Count, daily fever syndrome counts; count + 8, daily count plus 8 counts; both methods, 30 days with the additional counts detected by both the initial and best methods; initial only, 2 days with the additional counts detected by using initial method only; and best only, 19 days with additional counts detected by using best method only.
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