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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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    2. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
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