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Algorithm Combination for Improved Performance in Biosurveillance

Univariate Monitoring
  • Inbal Yahav
  • Thomas Lotze
  • Galit Shmueli
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 27)

Chapter Overview

This chapter proposes an enhancement to currently used algorithms for monitoring daily counts of pre-diagnostic data. Rather than use a single algorithm or apply multiple algorithms simultaneously, our approach is based on ensembles of algorithms. The ensembles lead to better performance in terms of higher true alert rates for a given false alert rate. Combinations can be employed at the data preprocessing step and/or at the monitoring step. We discuss the advantages of such an approach and illustrate its usefulness using authentic modern biosurveillance data.

Keywords

Control charts Monitoring Ensemble methods Optimization Pre-diagnostic data 

References

  1. Box, G., Luceno, A. (1997)Statistical Control: By Monitoring and Feedback Adjustment. 1st ed. Wiley-Interscience, London.Google Scholar
  2. Burkom, H. S. (2003) “Development, adaptation and assessment of alerting algorithms for biosurveillance”,Johns Hopkins APL Technical Digest24(4), 335–342.Google Scholar
  3. Burkom, H. S., Murphy, S. P., and Shmueli, G. (2007) “Automated time series forecasting for biosurveillance”,Statistics in Medicine 26(22), 4202–4218.PubMedCrossRefGoogle Scholar
  4. Lotze, T. and Shmueli, G. (2008) “Ensemble forecasting for disease outbreak detection”, 23rd AAAI Conference on Artificial Intelligence, Chicago July 08. Google Scholar
  5. Lotze, T., Shmueli G. and Yahav, I. (2007) Simulating Multivariate Syndromic Time Series and Outbreak Signatures, Robert H. Smith School Research Paper No. RHS-06-054, Available at SSRN: http://www.ssrn.com/abstract=990020.
  6. Lotze, T., Murphy, S. P., and Shmueli, G. (2008) “Preparing biosurveillance data for classic monitoring”, Advances in Disease Surveillance 6.Google Scholar
  7. Montgomery, D.C. (1997)Introduction to Statistical Quality Control.3rd ed. Wiley, London.NIST/SEMATECH: E-handbook of statistical methods.http://www.itl.nist.gov/div898/handbook.
  8. Siegrist, D. and Pavlin, J. (2004) Bio-ALIRT biosurveillance detection algorithm evaluation. MMWR, 53(Suppl), 152“158PubMedGoogle Scholar
  9. Shmueli, G. and Burkom, H. (2008) “Statistical challenges in modern biosurveillance”,Technometrics, in press. Google Scholar

Online Resources

  1. http://www.projectmimic.com: R code for simulating or mimicking multivariate time series of biosurveillance data and outbreak signatures. Also includes semi-authentic data.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Inbal Yahav
    • 1
  • Thomas Lotze
    • 2
  • Galit Shmueli
    • 1
  1. 1.Department of Decision, Operations & Information TechnologiesRobert H Smith School of Business, University of MarylandCollege ParkUSA
  2. 2.Applied Mathematics & Scientific Computation ProgramUniversity of MarylandCollege ParkUSA

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