#Swineflu: Twitter Predicts Swine Flu Outbreak in 2009

  • Martin Szomszor
  • Patty Kostkova
  • Ed de Quincey
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 69)


Early warning systems for the identification and tracking of infections disease outbreaks have become an important tool in the field of epidemiology. While government lead initiatives to increase the sharing of surveillance data have improved early detection and control, along with advanced web monitoring and analytics services, the recent swine flu outbreak of 2009 demonstrated the important role social media has and the wealth of data it exposes. In this paper, we present an investigation into Twitter, using around 3 Million tweets gathered between May and December 2009, as a possible source of surveillance data and its feasibility to serve as an early warning system. By performing simple filtering and normalization, we demonstrate that Twitter can serve as a self-reporting tool, and hence, provide indications of increased infection spreading. Our initial findings indicate that Twitter can detect such events up to one week before conventional GP reported surveillance data.


Epidemic Intelligence Twitter H1N1 Pandemic Flu 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Kaiser, R., Coulombier, D., Maldari, M., Morgan, D., Paquet, C.: What is epidemic intelligence, and how it is being improved in Europe? Eurosureillance 11(2), 60202 (2006)Google Scholar
  3. 3.
    Kaiser, R., Coulombier, D.: Different approaches to gathering epidemic intelligence in Europe. Euro Surveillance 11(17), 2948 (2006)Google Scholar
  4. 4.
    Paquet, C., Coulombier, D., Kaiser, R., Ciotti, M.: Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Euro Surveillance 11(12), 665 (2006)Google Scholar
  5. 5.
    Coulombier, D., Pinto, A., Valenciano, M.: Epidemiological surveillance during humanitarian emergencies. Médecine tropicale: revue du Corps de santé colonial 62(4), 391–395 (2002)Google Scholar
  6. 6.
  7. 7.
  8. 8.
  9. 9.
    Linge, J.P., Steinberger, R., Weber, T.P., Yangarber, R., van der Goot, E., Al Khudhairy, D.H., Stilianakis, N.I.: Internet surveillance systems for early alerting of health threats. Euro Surveill. 14(13), 1916 (2009)Google Scholar
  10. 10.
    Madoff, L.C.: ProMED-mail: An Early Warning System for Emerging Diseases. Clinical Infectious Diseases 39(2), 227 (2004)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    de Quicney, E., Kostkova, P., Wiseman, S.: An investigation into the potential of Web 2.0 websites to tracks disease outbreak. Poster at Infection 2009, Birmingham, UK (2009) Google Scholar
  13. 13.
  14. 14.
    Williams, D.: API Overview,
  15. 15.
  16. 16.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. Raleigh, North Carolina (2010)Google Scholar
  17. 17.
  18. 18.

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Martin Szomszor
    • 1
  • Patty Kostkova
    • 1
  • Ed de Quincey
    • 2
  1. 1.City eHealth Research CentreCity UniversityLondonUK
  2. 2.School of Computing and MathematicsUniversity of GreenwichUK

Personalised recommendations