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

  • Author(s): Poirier, Canelle;
  • Hswen, Yulin;
  • Bouzillé, Guillaume;
  • Cuggia, Marc;
  • Lavenu, Audrey;
  • Brownstein, John S;
  • Brewer, Thomas;
  • Santillana, Mauricio
  • et al.
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.

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