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Markov Switching Models for Outbreak Detection

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Infectious Disease Informatics and Biosurveillance

Part of the book series: Integrated Series in Information Systems ((ISIS,volume 27))

Abstract

Infectious disease outbreak detection is one of the main objectives of syndromic surveillance systems. Accurate and timely detection can provide valuable information for public health officials to react to major public health threats. However, disease outbreaks are often not directly observable. Moreover, additional noise caused by routine behavioral patterns and special events further complicates the task of identifying abnormal patterns caused by infectious disease outbreaks. We consider the problem of identifying outbreak patterns in a syndrome count time series using the Markov switching models. The outbreak states are treated as hidden (unobservable) state variables. Gibbs sampler then is used to estimate both the parameters and hidden state variables. We cover both the theoretical foundation of the estimation methods and the technical details of estimating the Markov switching models. A case study is presented in the last section.

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Author information

Authors and Affiliations

  1. Department of Information Management, National Taiwan University, Taipei, Taiwan

    Hsin-Min Lu

Authors
  1. Hsin-Min Lu
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  2. Daniel Zeng
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  3. Hsinchun Chen
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Corresponding author

Correspondence to Hsin-Min Lu .

Editor information

Editors and Affiliations

  1. Dept. Mathematics & Statistics, Arizona State University, Tempe, Arizona, USA

    Carlos Castillo-Chavez

  2. Eller College of Management, University of Arizona, E. Helen St. 1130, Tucson, 85721, Arizona, USA

    Hsinchun Chen

  3. Health Sciences Center, University of Washington, NE. Pacific St. 1959, Seattle, 98195, Washington, USA

    William B. Lober

  4. School of Veterinary Medicine, Dept. Medicine & Epidemiology, University of California, Davis, Shields Avenue 1, Davis, 95616, California, USA

    Mark Thurmond

  5. Eller College of Management, Dept. Management Information Systems, University of Arizona, Tucson, 85721, Arizona, USA

    Daniel Zeng

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Lu, HM., Zeng, D., Chen, H. (2011). Markov Switching Models for Outbreak Detection. In: Castillo-Chavez, C., Chen, H., Lober, W., Thurmond, M., Zeng, D. (eds) Infectious Disease Informatics and Biosurveillance. Integrated Series in Information Systems, vol 27. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6892-0_6

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  • DOI: https://doi.org/10.1007/978-1-4419-6892-0_6

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-6891-3

  • Online ISBN: 978-1-4419-6892-0

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