tetano
Editor, Senior Moderator
PLoS One. 2013 Jul 24;8(7):e69305. doi: 10.1371/journal.pone.0069305. Print 2013.
Use of hangeul twitter to track and predict human influenza infection.
Kim EK, Seok JH, Oh JS, Lee HW, Kim KH.
Source
Department of Biotechnology and Bioinformatics, College of Science and Technology, Korea University, Sejong, Korea.
Abstract
Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.
PMID:
23894447
[PubMed - in process]
PMCID:
PMC3722273
Free PMC Article
http://www.ncbi.nlm.nih.gov/pubmed/23894447
Use of hangeul twitter to track and predict human influenza infection.
Kim EK, Seok JH, Oh JS, Lee HW, Kim KH.
Source
Department of Biotechnology and Bioinformatics, College of Science and Technology, Korea University, Sejong, Korea.
Abstract
Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.
PMID:
23894447
[PubMed - in process]
PMCID:
PMC3722273
Free PMC Article
http://www.ncbi.nlm.nih.gov/pubmed/23894447