Algorithm Combination for Improved Performance in Biosurveillance Systems

  • Inbal Yahav
  • Galit Shmueli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)

Abstract

The majority of statistical research on detecting disease outbreaks from prediagnostic data has focused on tools for modeling background behavior of such data, and for monitoring the data for anomaly detection. Because pre-diagnostic data tends to include explainable patterns such as day-of-week, seasonality, and holiday effects, the monitoring process often calls for a two-step algorithm: first, a preprocessing technique is used for deriving a residual series, and then the residuals are monitored using a classic control chart. Most studies tend to apply a single combination of a pre-processing technique with a particular control chart to a particular type of data. Although the choice of preprocessing technique should be driven by the nature of the non-outbreak data and the choice of the control chart by the nature of the outbreak to be detected, often the nature of both is non-stationary and unclear, and varies considerable across different data series. We therefore take an approach that combines algorithms rather than choosing a single one. In particular, we propose a method for combining multiple preprocessing algorithms and a method for combining multiple control charts, both based on linear-programming. We show preliminary results for combining pre-processing techniques, applied to both simulated and authentic syndromic data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lotze, T., Murphy, S., Shmueli, G.: Preparing biosurveillance data for classic monitoring. Submitted to Advances in Disease Surveillance (2007)Google Scholar
  2. 2.
    Brillman, J.C., et al.: Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance. BMC Medical Informatics and Decision Making 5, 4 (2005)CrossRefGoogle Scholar
  3. 3.
    Reis, B.Y., Mandl, K.D.: Time series modeling for syndromic surveillance. BMC Medical Informatics and Decision Making 3(2) (2003), http://www.biomedcentral.com/1472-6947/3/2
  4. 4.
    Shmueli, G., Fienberg, S.: Current and potential statistical methods for monitoring multiple data streams for bio-surveillance. In: Wilson, A., Olwell, D. (eds.) Statistical Methods in Counter-Terrorism, Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Buckeridgea, D.L., et al.: Algorithms for rapid outbreak detection: a research synthesis. Journal of Biomedical Informatics 38, 99–113 (2005)CrossRefGoogle Scholar
  6. 6.
    Rice, J.A.: Mathematical Statistics and Data Analysis, 2nd edn. Duxbury Press, Belmont (1995)MATHGoogle Scholar
  7. 7.
    Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd edn. Springer Series in Statistics. Springer, New York (1991)Google Scholar
  8. 8.
    Muscatello, D.: An adjusted cumulative sum for count data with day-of-week effects: application to influenza-like illness. Presentation at Syndromic Surveillance Conference (2004)Google Scholar
  9. 9.
    Montgomery, D.C.: Introduction to Statistical Quality Control, 3rd edn. Wiley, Chichester (1997)MATHGoogle Scholar
  10. 10.
    Chatfield, C.: The Holt-Winters forecasting procedure. J. Appl. Stat. 27(3) (1978)Google Scholar
  11. 11.
    Burkom, H.S., Murphy, S.P., Shmuely, G.: Automated time series forecasting for biosurveillance. Statistics in Medicine (2007)Google Scholar
  12. 12.
    Box, G., Luceno, A.: Statistical Control: By Monitoring and Feedback Adjustment, 1st edn. Wiley-Interscience, Chichester (1997)MATHGoogle Scholar
  13. 13.
    NIST/SEMATECH (e-handbook of statistical methods div898/handbook/), http://www.itl.nist.gov/
  14. 14.
    Sapir, L.: The optimality of the expert and majority rules under exponentially distributed competence. Theory and Decision 45, 19–36 (1998)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    CDC: Centers for disease control and prevention, http://www.bt.cdc.gov/surveillance/syndromedef/
  16. 16.
    Aradhye, H.B., et al.: Multiscale statistical process control using wavelets - theoretical analysis and properties. AIChE Journal 49, 939–958 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Inbal Yahav
    • 1
  • Galit Shmueli
    • 1
  1. 1.Department of Decision & Information Technologies, and Center for Health Information and Decision Systems, Robert H Smith School of Business, University of Maryland, College Park, MD 20742U.S.A

Personalised recommendations