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Regional level influenza study based on Twitter and machine learning method

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  • Hongxin Xue
  • Yanping Bai
  • Hongping Hu
  • Haijian Liang

Abstract

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control’s (CDC’s) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0215600
    DOI: 10.1371/journal.pone.0215600
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    References listed on IDEAS

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    1. Tatiana Petukhova & Davor Ojkic & Beverly McEwen & Rob Deardon & Zvonimir Poljak, 2018. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    2. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    3. 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.
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