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Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

  • Vanja Dukic
  • Hedibert F. Lopes
  • Nicholas G. Polson
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    In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003--2009 and in nine separate U.S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. This article has online supplementary materials.

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    File URL: http://hdl.handle.net/10.1080/01621459.2012.713876
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    Article provided by Taylor & Francis Journals in its journal Journal of the American Statistical Association.

    Volume (Year): 107 (2012)
    Issue (Month): 500 (December)
    Pages: 1410-1426

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    Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1410-1426
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