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Time Series Analyses of Hand, Foot and Mouth Disease Integrating Weather Variables

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  • Yuanbin Song
  • Fan Wang
  • Bin Wang
  • Shaohua Tao
  • Huiping Zhang
  • Sai Liu
  • Oscar Ramirez
  • Qiyi Zeng

Abstract

Background: The past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities. Methods: Samples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors. Results: Among inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in. Conclusion: Climate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.

Suggested Citation

  • Yuanbin Song & Fan Wang & Bin Wang & Shaohua Tao & Huiping Zhang & Sai Liu & Oscar Ramirez & Qiyi Zeng, 2015. "Time Series Analyses of Hand, Foot and Mouth Disease Integrating Weather Variables," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
    • Handle: RePEc:plo:pone00:0117296
    DOI: 10.1371/journal.pone.0117296
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    Citations

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    Cited by:

    1. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    2. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    3. Li Qi & Wenge Tang & Han Zhao & Hua Ling & Kun Su & Hua Zhao & Qin Li & Tao Shen, 2018. "Epidemiological Characteristics and Spatial-Temporal Distribution of Hand, Foot, and Mouth Disease in Chongqing, China, 2009–2016," IJERPH, MDPI, vol. 15(2), pages 1-12, February.
    4. Chengdong Xu, 2017. "Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing–Tianjin–Hebei Region of China," IJERPH, MDPI, vol. 14(4), pages 1-13, April.

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