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

Canelle Poirier, Yulin Hswen, Guillaume Bouzillé, Marc Cuggia, Audrey Lavenu, John S Brownstein, Thomas Brewer and Mauricio Santillana

PLOS ONE, 2021, vol. 16, issue 5, 1-26

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0250890

DOI: 10.1371/journal.pone.0250890

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