Forecasting Influenza Levels Using Real-Time Social Media Streams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Seasonal influenza is a contagious respiratory illness that can cause various complications, worsen chronic illnesses, and sometimes lead to deaths. During 2009 H1N1 flu pandemic, up to 203,000 deaths occurred worldwide. Early detection and prediction of disease outbreak is critical because it can provide more time to prepare a response and significantly reduce the impact caused by a pandemic. The traditional influenza surveillance system by Centers for Disease Control and Prevention (CDC) collects U.S. Influenza-Like Illness related physicians visits data from sentinel practices and provides a retrospective analysis delayed by two weeks. Google Flu Trends proposed a method that uses online search queries data to estimate current (real-time) influenza activity. Here we present a system that (1) predicts future influenza activities, (2) provides more accurate real-time assessment than before, and (3) combines real-time big social media data streams and CDC historical datasets for predictive models to accomplish accurate predictions. Although retrospective analysis and observations are important, prediction of future flu levels can represent a big leap because such predictions provide actionable insights for public health that can be used for planning, resource allocation, treatments and prevention. Thus, compared to previous work, our work represents an advancement in accuracy of assessments, prediction of future flu activity accurately and an ability to combine big social data and observed CDC data to build predictive models.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
EditorsMollie Cummins, Julio Facelli, Gerrit Meixner, Christophe Giraud-Carrier, Hiroshi Nakajima
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages409-414
Number of pages6
ISBN (Electronic)9781509048816
DOIs
StatePublished - Sep 8 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: Aug 23 2017Aug 26 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
Country/TerritoryUnited States
CityPark City
Period8/23/178/26/17

Keywords

  • Influenza
  • Prediction
  • Public health
  • Social media

ASJC Scopus subject areas

  • Health Informatics

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