IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v107y2012i500p1410-1426.html
   My bibliography  Save this article

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

Author

Listed:
  • Vanja Dukic
  • Hedibert F. Lopes
  • Nicholas G. Polson

Abstract

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.

Suggested Citation

  • Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1410-1426
    DOI: 10.1080/01621459.2012.713876
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2012.713876
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Baek, Changryong & Davis, Richard A. & Pipiras, Vladas, 2017. "Sparse seasonal and periodic vector autoregressive modeling," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 103-126.
    2. repec:eee:ecmode:v:70:y:2018:i:c:p:194-202 is not listed on IDEAS
    3. Paul Fearnhead & Vasilieos Giagos & Chris Sherlock, 2014. "Inference for reaction networks using the linear noise approximation," Biometrics, The International Biometric Society, vol. 70(2), pages 457-466, June.
    4. repec:bla:biomet:v:73:y:2017:i:1:p:283-293 is not listed on IDEAS
    5. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    6. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    7. Lili Zhuang & Noel Cressie, 2014. "Bayesian hierarchical statistical SIRS models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(4), pages 601-646, November.
    8. Andrew Hoegh & Marco A. R. Ferreira & Scotland Leman, 2016. "Spatiotemporal model fusion: multiscale modelling of civil unrest," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 529-545, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1410-1426. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/UASA20 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.