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TDDF: HFMD Outpatients Prediction Based on Time Series Decomposition and Heterogenous Data Fusion in Xiamen, China

  • Zhijin WangEmail author
  • Yaohui Huang
  • Bingyan He
  • Ting Luo
  • Yongming Wang
  • Yingxian LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Hand, foot and mouth disease (HFMD) is a common infectious disease in global public health. In this paper, the time series decomposition and heterogeneous data fusion (TDDF) method is proposed to enhance features in the performance of HFMD outpatients prediction. The TDDF first represents meteorological features and Baidu search index features with the consideration of lags, then those features are fused into decomposed historical HFMD cases to predict coming outpatient cases. Experimental results and analyses on the real collected records show the efficiency and effectiveness of TDDF on regression methods.

Keywords

HFMD prediction Meteorological factor Baidu search index 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of Fujian Province of China (No. 2018J01539 and No. 2019J01713), and the Xiamen Center for Disease Control and Prevention. The authors would like to thank the editor and anonymous reviewers for their helpful comments in improving the quality of this paper.

References

  1. 1.
    Public Health Emergency Events Emergency Regulations. http://www.nhfpc.gov.cn/yjb/s3580/200804/b41369aac27847dba3e6aebccc72e2f8.shtml/chn (2005)
  2. 2.
  3. 3.
    National Public Health Emergency Event Information Report and Management Regulations. http://www.nhfpc.gov.cn/mohbgt/pw10601/200804/27519.shtml/chn (2018). Accessed 1 Feb 2016
  4. 4.
    World Health Organization. http://www.who.int/infection-prevention/en/ (2018)
  5. 5.
    Xiamen from Wikipedia. https://en.wikipedia.org/wiki/Xiamen (2019)
  6. 6.
    Chen, S., et al.: The application of meteorological data and search index data in improving the prediction of HFMD: a study of two cities in Guangdong province, China. Sci. Total Environ. 652, 1013–1021 (2019)CrossRefGoogle Scholar
  7. 7.
    Ji, T., et al.: Surveillance, epidemiology, and pathogen spectrum of hand, foot, and mouth disease in mainland of china from 2008 to 2017. Biosaf. Health (2019) CrossRefGoogle Scholar
  8. 8.
    Sun, B.J., Chen, H.J., Chen, Y., An, X.D., Zhou, B.S.: The risk factors of acquiring severe hand, foot, and mouth disease: a meta-analysis. Can. J. Infect. Dis. Med. Microbiol. 2018, 1–12 (2018)CrossRefGoogle Scholar
  9. 9.
    McMichael, A.J., Woodruff, R.E.: 14 - climate change and infectious diseases. In: Mayer, K.H., Pizer, H. (eds.) The Social Ecology of Infectious Diseases, pp. 378–407. Academic Press, San Diego (2008)CrossRefGoogle Scholar
  10. 10.
    Nourani, V., Alami, M.T., Aminfar, M.H.: A combined neural-wavelet model for prediction of ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 22(3), 466–472 (2009)CrossRefGoogle Scholar
  11. 11.
    Ooi, M.H., et al.: Identification and validation of clinical predictors for the risk of neurological involvement in children with hand, foot, and mouth disease in sarawak. BMC Infect. Dis. 9(1), 3 (2009)CrossRefGoogle Scholar
  12. 12.
    Shao, Q., Yang, L.: Polynomial spline confidence bands for time series trend. J. Stat. Plann. Infer. 142(7), 1678–1689 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P.: A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4144–4147, May 2011Google Scholar
  14. 14.
    Wang, L., Jin, L., Xiong, W., Tu, W., Ye, C.: Infectious disease surveillance in china. In: Yang, W. (ed.) Early Warning for Infectious Disease Outbreak, pp. 23–33. Academic Press, San Diego (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Li, J., Gu, J., Zhou, Z., Wang, Z.: Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl. Soft Comput. 35, 280–290 (2015)CrossRefGoogle Scholar
  16. 16.
    Yang, S., et al.: Epidemiological features of and changes in incidence of infectious diseases in China in the first decade after the sars outbreak: an observational trend study. Lancet. Infect. Dis. 17(7), 716–725 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Engineering CollegeJimei UniversityXiamenChina
  2. 2.Chengyi University CollegeJimei UniversityXiamenChina
  3. 3.China Electronics Technology Group CorporationShanghaiChina

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