IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0250890.html
   My bibliography  Save this article

Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach

Author

Listed:
  • Canelle Poirier
  • Yulin Hswen
  • Guillaume Bouzillé
  • Marc Cuggia
  • Audrey Lavenu
  • John S Brownstein
  • Thomas Brewer
  • Mauricio Santillana

Abstract

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.

Suggested Citation

  • Canelle Poirier & Yulin Hswen & Guillaume Bouzillé & Marc Cuggia & Audrey Lavenu & John S Brownstein & Thomas Brewer & Mauricio Santillana, 2021. "Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-26, May.
    • Handle: RePEc:plo:pone00:0250890
    DOI: 10.1371/journal.pone.0250890
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250890
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0250890&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0250890?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Fred S. Lu & Mohammad W. Hattab & Cesar Leonardo Clemente & Matthew Biggerstaff & Mauricio Santillana, 2019. "Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Kyriaki Kalimeri & Matteo Delfino & Ciro Cattuto & Daniela Perrotta & Vittoria Colizza & Caroline Guerrisi & Clement Turbelin & Jim Duggan & John Edmunds & Chinelo Obi & Richard Pebody & Ana O Franco , 2019. "Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Sabique Islam & Sirish Namilae & Richard Prazenica & Dahai Liu, 2020. "Fuel shortages during hurricanes: Epidemiological modeling and optimal control," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
    4. Nicholas G Reich & Craig J McGowan & Teresa K Yamana & Abhinav Tushar & Evan L Ray & Dave Osthus & Sasikiran Kandula & Logan C Brooks & Willow Crawford-Crudell & Graham Casey Gibson & Evan Moore & Reb, 2019. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-19, November.
    5. Morley, Jessica & Machado, Caio C.V. & Burr, Christopher & Cowls, Josh & Joshi, Indra & Taddeo, Mariarosaria & Floridi, Luciano, 2020. "The ethics of AI in health care: A mapping review," Social Science & Medicine, Elsevier, vol. 260(C).
    6. Zhijuan Song & Xiaocan Jia & Junzhe Bao & Yongli Yang & Huili Zhu & Xuezhong Shi, 2021. "Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020," IJERPH, MDPI, vol. 18(13), pages 1-14, July.
    7. Maud Thomas & Holger Rootzén, 2022. "Real‐time prediction of severe influenza epidemics using extreme value statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 376-394, March.
    8. Franco Salerno, 2023. "The Greta Thunberg Effect on Climate Equity: A Worldwide Google Trend Analysis," Sustainability, MDPI, vol. 15(7), pages 1-13, April.
    9. Michal Ben-Nun & Pete Riley & James Turtle & David P Bacon & Steven Riley, 2019. "Forecasting national and regional influenza-like illness for the USA," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-20, May.
    10. 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.
    11. Sarah C Kramer & Jeffrey Shaman, 2019. "Development and validation of influenza forecasting for 64 temperate and tropical countries," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-20, February.
    12. Kookjin Lee & Jaideep Ray & Cosmin Safta, 2021. "The predictive skill of convolutional neural networks models for disease forecasting," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-26, July.
    13. Teresa K Yamana & Sasikiran Kandula & Jeffrey Shaman, 2017. "Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-17, November.
    14. 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.
    15. Sarah C Kramer & Sen Pei & Jeffrey Shaman, 2020. "Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-21, October.
    16. Jeffrey Shaman & Sasikiran Kandula & Wan Yang & Alicia Karspeck, 2017. "The use of ambient humidity conditions to improve influenza forecast," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-16, November.
    17. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    18. Taichi Murayama & Nobuyuki Shimizu & Sumio Fujita & Shoko Wakamiya & Eiji Aramaki, 2020. "Robust two-stage influenza prediction model considering regular and irregular trends," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
    19. 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.
    20. Kyle S Hickmann & Geoffrey Fairchild & Reid Priedhorsky & Nicholas Generous & James M Hyman & Alina Deshpande & Sara Y Del Valle, 2015. "Forecasting the 2013–2014 Influenza Season Using Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-29, May.

    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:plo:pone00:0250890. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.