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Tracking and predicting U.S. influenza activity with a real-time surveillance network

Author

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  • Sequoia I Leuba
  • Reza Yaesoubi
  • Marina Antillon
  • Ted Cohen
  • Christoph Zimmer

Abstract

Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve “nowcasts” and short-term forecasts of U.S. influenza activity.Author summary: The United States Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance system. However, the CDC influenza surveillance data are subject to a 1 to 2 week reporting delay, which limits how such information can be used to assess the current burden of disease and to make timely projections of the trajectory of the epidemic. Researchers have previously used indirect signals of influenza activity such as Google search queries for influenza-related terms or Twitter posts to develop more timely estimates of influenza burden and improved model-based forecasts. However, these indirect signals are subject to behavioral changes not related to influenza activity, and thus may provide inaccurate estimates and projections. We used a new real-time data source with high geographic resolution that is directly related to influenza activity: influenza test results provided through a national network of commercial influenza diagnostic test machines. We used these influenza test results to accurately estimate the current burden of influenza and improve real-time model projections of influenza burden in the United States.

Suggested Citation

  • Sequoia I Leuba & Reza Yaesoubi & Marina Antillon & Ted Cohen & Christoph Zimmer, 2020. "Tracking and predicting U.S. influenza activity with a real-time surveillance network," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-14, November.
    • Handle: RePEc:plo:pcbi00:1008180
    DOI: 10.1371/journal.pcbi.1008180
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    References listed on IDEAS

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    1. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    2. 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.
    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.
    4. Wan Yang & Alicia Karspeck & Jeffrey Shaman, 2014. "Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-15, April.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
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