Modeling flu trends with real-time geo-tagged Twitter data streams

Jaime Chon, Ross Raymond, Haiyan Wang, Feng Wang

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

8 Scopus citations


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 realtime 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 realtime tweet streams. To evaluate the accuracy of the influenza estimation based on tweet streams, we correlate the results with official statistics from Center for Disease Control and Prevention (CDC). Our preliminary results have demonstrated that real-time tweet streams capture the dynamics of influenza at national level, and could potentially serve as an early warning system of influenza epidemics or flu trends.

Original languageEnglish (US)
Title of host publicationWireless Algorithms, Systems, and Applications - 10th International Conference, WASA 2015, Proceedings
EditorsKuai Xu, Haojin Zhu
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319218366
StatePublished - 2015
Event10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015 - Qufu, China
Duration: Aug 10 2015Aug 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015


  • Geo-tagged twitter stream
  • Influenza
  • Mathematical modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Modeling flu trends with real-time geo-tagged Twitter data streams'. Together they form a unique fingerprint.

Cite this