Twitter improves seasonal influenza prediction

HEALTHINF 2012 - Proceedings of the International Conference on Health Informatics

Volumn , Issue , 2012, Pages 61-70

  Achrekar, Harshavardhan ^a   Gandhe, Avinash ^b   Lazarus, Ross ^c   Yu, Ssu Hsin ^b   Liu, Benyuan ^a  

^a University of Massachusetts Lowell   (United States)

^b SCIENTIFIC SYSTEMS COMPANY INC   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Flu trends; Online social networks; Prediction

Indexed keywords

ACCURATE PREDICTION; AGE GROUPS; AGE-BASED; AUTO-REGRESSION; BEST FIT; CENTERS FOR DISEASE CONTROL AND PREVENTIONS; CONTINUOUS DATA; EXOGENOUS INPUT; FLU TRENDS; HIGHLY-CORRELATED; ONLINE SOCIAL NETWORKS; REAL-TIME ASSESSMENT; SOCIAL NETWORKS; TEXT MINING;

EPIDEMIOLOGY; FORECASTING; HEALTH RISKS; INSPECTION; SOCIAL NETWORKING (ONLINE);

POPULATION STATISTICS;

EID: 84861991954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., and Liu, B. (2011). Predicting flu trends using twitter data. IEEE Infocom, 2011 workshop on on Cyber-Physical Networking Systems (CPNS) 2011.
2. Centers for Disease Control and Prevention (2009). Flu-View, a weekly influenza surveillance report.
3. Culotta, A. (2010). Detecting influenza outbreaks by analyzing twitter messages. Knowledge Discovery and Data Mining Workshop on Social Media Analytics, 2010.
4. Espino, J., Hogan, W., and Wagner, M. (2003). Telephone triage: A timely data source for surveillance of influenza-like diseases. In AMIA: Annual Symposium Proceedings.
5. Ferguson, N. M., Cummings, D. A., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A., Iamsirithaworn, S., and Burke, D. S. (2005). Strategies for containing an emerging influenza pandemic in southeast asia. Nature, 437:209-214.
6. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457:1012-1014.
7. Jansen, B., Zhang, M., Sobel, K., and Chowdury, A. (2009). Twitter power: tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(1532):2169-2188.
8. Jordans, F. (2009). WHO working on formulas to model swine flu spread.
9. Lazarus, R., Kleinman, K., Dashevsky, I., Adams, C., Kludt, P., DeMaria, A., Jr., R., and Platt (2002). Use of automated ambulatory-care encounter records for detection of acute illness clusters, including potential bioterrorism events.
10. Leskovec, J., Backstrom, L., and Kleinberg, J. (2009). Meme-tracking and the dynamics of the news cycle. International Conference on Knowledge Discovery and Data Mining, Paris, France, 495(978).
11. Longini, I., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D., and Halloran, M. (2005). Containing pandemic influenza at the source. Science, 309(5737):1083-1087.
12. Magruder, S. (2003). Evaluation of over-the-counter pharmaceutical sales as a possible early warning indicator of human disease. In Johns Hopkins University APL Technical Digest.
13. Motoyama, M., Meeder, B., Levchenko, K., Voelker, G. M., and Savage, S. (2010). Measuring online service availability using twitter. Workshop on online social networks, Boston, Massachusetts, USA.
14. Nardelli, A. (2010). Tweetminister.
15. Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In 19th international conference on World wide web, Raleigh, North Carolina, USA.
16. Signorini, A., Segre, A. M., and Polgreen, P. M. (2011). The use of twitter to track levels of disease activity and public concern in the u.s. during the influenza a h1n1 pandemic. PLoS ONE, Volume 6 - Issue 5.
17. Sitaram, A. and Huberman, B. A. (2010). Predicting the future with social media. In Social Computing Lab, HP Labs, Palo Alto, California, USA.
18. Twitter (2011). Information on twitter users age-wise.