Use of hangeul twitter to track and predict human influenza infection.
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3722273?pdf=render |
| _version_ | 1828875595561304064 |
|---|---|
| author | Eui-Ki Kim Jong Hyeon Seok Jang Seok Oh Hyong Woo Lee Kyung Hyun Kim |
| author_facet | Eui-Ki Kim Jong Hyeon Seok Jang Seok Oh Hyong Woo Lee Kyung Hyun Kim |
| author_sort | Eui-Ki Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T08:02:07Z |
| format | Article |
| id | doaj.art-31ee731cd23740998342344516e2b382 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T08:02:07Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-31ee731cd23740998342344516e2b3822022-12-21T23:54:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0187e6930510.1371/journal.pone.0069305Use of hangeul twitter to track and predict human influenza infection.Eui-Ki KimJong Hyeon SeokJang Seok OhHyong Woo LeeKyung Hyun KimInfluenza 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.http://europepmc.org/articles/PMC3722273?pdf=render |
| spellingShingle | Eui-Ki Kim Jong Hyeon Seok Jang Seok Oh Hyong Woo Lee Kyung Hyun Kim Use of hangeul twitter to track and predict human influenza infection. PLoS ONE |
| title | Use of hangeul twitter to track and predict human influenza infection. |
| title_full | Use of hangeul twitter to track and predict human influenza infection. |
| title_fullStr | Use of hangeul twitter to track and predict human influenza infection. |
| title_full_unstemmed | Use of hangeul twitter to track and predict human influenza infection. |
| title_short | Use of hangeul twitter to track and predict human influenza infection. |
| title_sort | use of hangeul twitter to track and predict human influenza infection |
| url | http://europepmc.org/articles/PMC3722273?pdf=render |
| work_keys_str_mv | AT euikikim useofhangeultwittertotrackandpredicthumaninfluenzainfection AT jonghyeonseok useofhangeultwittertotrackandpredicthumaninfluenzainfection AT jangseokoh useofhangeultwittertotrackandpredicthumaninfluenzainfection AT hyongwoolee useofhangeultwittertotrackandpredicthumaninfluenzainfection AT kyunghyunkim useofhangeultwittertotrackandpredicthumaninfluenzainfection |