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Detecting influenza epidemics using search engine query data

Author

Listed:
  • Jeremy Ginsberg

    (Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA)

  • Matthew H. Mohebbi

    (Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA)

  • Rajan S. Patel

    (Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA)

  • Lynnette Brammer

    (Centers for Disease Control and Prevention, 1600 Clifton Road, NE, Atlanta, Georgia 30333, USA)

  • Mark S. Smolinski

    (Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA)

  • Larry Brilliant

    (Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA)

Abstract

Google-driven epidemiology This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control.

Suggested Citation

  • Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    • Handle: RePEc:nat:nature:v:457:y:2009:i:7232:d:10.1038_nature07634
    DOI: 10.1038/nature07634
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