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Journal of Medical Systems

, 40:189 | Cite as

Regional Level Influenza Study with Geo-Tagged Twitter Data

  • Feng Wang
  • Haiyan Wang
  • Kuai Xu
  • Ross Raymond
  • Jaime Chon
  • Shaun Fuller
  • Anton Debruyn
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Advances in Big-Data based mHealth Theories and Applications

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.

Keywords

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

Notes

Acknowledgements

This project is supported by NSF grant CNS #1218212.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and SciencesArizona State UniversityGlendaleUSA

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