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Use of Hangeul Twitter to Track and Predict Human Influenza Infection

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  • Eui-Ki Kim
  • Jong Hyeon Seok
  • Jang Seok Oh
  • Hyong Woo Lee
  • Kyung Hyun Kim

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.

Suggested Citation

  • Eui-Ki Kim & Jong Hyeon Seok & Jang Seok Oh & Hyong Woo Lee & Kyung Hyun Kim, 2013. "Use of Hangeul Twitter to Track and Predict Human Influenza Infection," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
  • Handle: RePEc:plo:pone00:0069305
    DOI: 10.1371/journal.pone.0069305
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    Cited by:

    1. Jiangpeng Chen & Xun Lei & Li Zhang & Bin Peng, 2015. "Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-10, February.
    2. Yulin Hswen & Alyssa J. Moran & Siona Prasad & Anna Li & Denise Simon & Lauren Cleveland & Jared B. Hawkins & John S. Brownstein & Jason Block, 2021. "The Federal Menu Labeling Law and Twitter Discussions about Calories in the United States: An Interrupted Time-Series Analysis," IJERPH, MDPI, vol. 18(20), pages 1-11, October.
    3. Hongxin Xue & Yanping Bai & Hongping Hu & Haijian Liang, 2019. "Regional level influenza study based on Twitter and machine learning method," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-23, April.
    4. Soo Beom Choi & Insung Ahn, 2020. "Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-14, July.
    5. Kui Liu & Li Li & Tao Jiang & Bin Chen & Zhenggang Jiang & Zhengting Wang & Yongdi Chen & Jianmin Jiang & Hua Gu, 2016. "Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform," IJERPH, MDPI, vol. 13(8), pages 1-15, August.

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