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

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...

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Bibliographic Details
Published in:PloS one 2021-05, Vol.16 (5), p.e0250890-e0250890
Main Authors: Poirier, Canelle, Hswen, Yulin, Bouzillé, Guillaume, Cuggia, Marc, Lavenu, Audrey, Brownstein, John S, Brewer, Thomas, Santillana, Mauricio
Format: Article
Language:English
Subjects:
Analysis
Bioengineering
Children
Climatic data
Computer and Information Sciences
Computer applications
Data sources
Design
Diagnosis
Disease
Earth Sciences
Editing
Electronic health records
Electronic mail
Electronic records
Epidemics
Estimates
Evaluation
Health informatics
Health surveillance
Hospitals
Influenza
Informatics
Innovations
Internet
Learning algorithms
Life Sciences
Machine learning
Mathematics
Medical records
Medicine and Health Sciences
Methods
Pandemics
Pediatrics
People and places
Physical Sciences
Public health
Real time
Regions
Research and Analysis Methods
Search engines
Social Sciences
Statistical methods
Statistics
Trends
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Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250890