Category-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support

Authors

  • Yevgeniy Elbert JHUAPL, Laurel, MD
  • Vivian Hung JHUAPL, Laurel, MD
  • Howard Burkom JHUAPL, Laurel, MD

DOI:

https://doi.org/10.5210/ojphi.v5i1.4411

Abstract

We compared detection performance of univariate alerting methods on real and simulated events in different types of biosurveillance data. Both kinds of detection performance analysis showed the method based on Holt-Winters exponential smoothing superior on non-sparse time series with day-of-week effects. The adaptive CUSUM and Shewhart methods proved optimal on sparse data and data without weekly patterns.

Author Biography

Yevgeniy Elbert, JHUAPL, Laurel, MD

Yevgeniy Elbert, MS, is a biostatistician at the Johns Hopkins University Applied Physics Laboratory(JHUAPL). He has extensive experience in developing and reporting on systems for disease surveillance. Since 2001, he has been working on the development of Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE) at Walter Reed Army Institute of Research and later JHUAPL. He has been an active member of ISDS since its inception in 2003.

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Published

2013-03-23

How to Cite

Elbert, Y., Hung, V., & Burkom, H. (2013). Category-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4411

Issue

Vol. 5 No. 1 (2013)

Section

Oral Presentations: Temporal or Spatio-temporal

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