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Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance

Author

Listed:
  • Jialiang Liu

    (Department of Epidemiology and Biostatistics, College of Public Health, Temple University, Philadelphia, PA 19122, USA)

  • Sumihiro Suzuki

    (Department of Preventive Medicine, Rush University Medical Center, Chicago, IL 60612, USA)

Abstract

The current gold standard for detection of flu season onset in the USA is done retrospectively, where flu season is detected after it has already started. We aimed to create a new surveillance strategy capable of detecting flu season onset prior to its starting. We used an established data generation method that combines Google search volume and historical flu activity data to simulate real-time estimates of flu activity. We then applied a method known as change-point detection to the generated data to determine the point in time that identifies the initial uptick in flu activity which indicates the imminent onset of flu season. Our strategy exhibits a high level of accuracy in predicting the onset of flu season at 86%. Additionally, on average, we detected the onset three weeks prior to the official start of flu season. The results provide evidence to support both the feasibility and efficacy of our strategy to improve the current standard of flu surveillance. The improvement may provide valuable support and lead time for public health officials to take appropriate actions to prevent and control the spread of the flu.

Suggested Citation

  • Jialiang Liu & Sumihiro Suzuki, 2022. "Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance," IJERPH, MDPI, vol. 19(6), pages 1-9, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3681-:d:775188
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    References listed on IDEAS

    as
    1. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    2. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    3. 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.
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