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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
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    References listed on IDEAS

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    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.
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