Regional Level Influenza Study with Geo-Tagged Twitter Data

Feng Wang, Haiyan Wang, Kuai Xu, Ross Raymond, Jaime Chon, Shaun Fuller, Anton Debruyn

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.

Original languageEnglish (US)
Article number189
JournalJournal of Medical Systems
Volume40
Issue number8
DOIs
StatePublished - Aug 1 2016

Keywords

  • Geo-tagged twitter stream
  • Influenza
  • Partial differential equation modeling
  • Regional level

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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