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Real-time influenza forecasts during the 2012–2013 season

Author

Listed:
  • Jeffrey Shaman

    (Mailman School of Public Health, Columbia University)

  • Alicia Karspeck

    (National Center for Atmospheric Research)

  • Wan Yang

    (Mailman School of Public Health, Columbia University)

  • James Tamerius

    (University of Iowa)

  • Marc Lipsitch

    (Center for Communicable Disease Dynamics, Harvard School of Public Health, Harvard University)

Abstract

Recently, we developed a seasonal influenza prediction system that uses an advanced data assimilation technique and real-time estimates of influenza incidence to optimize and initialize a population-based mathematical model of influenza transmission dynamics. This system was used to generate and evaluate retrospective forecasts of influenza peak timing in New York City. Here we present weekly forecasts of seasonal influenza developed and run in real time for 108 cities in the USA during the recent 2012–2013 season. Reliable ensemble forecasts of influenza outbreak peak timing with leads of up to 9 weeks were produced. Forecast accuracy increased as the season progressed, and the forecasts significantly outperformed alternate, analogue prediction methods. By week 52, prior to peak for the majority of cities, 63% of all ensemble forecasts were accurate. To our knowledge, this is the first time predictions of seasonal influenza have been made in real time and with demonstrated accuracy.

Suggested Citation

  • Jeffrey Shaman & Alicia Karspeck & Wan Yang & James Tamerius & Marc Lipsitch, 2013. "Real-time influenza forecasts during the 2012–2013 season," Nature Communications, Nature, vol. 4(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms3837
    DOI: 10.1038/ncomms3837
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    Cited by:

    1. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    2. Lutz Bornmann & Robin Haunschild & Vanash M Patel, 2020. "Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.

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