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Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

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  • Xingyu Zhang
  • Tao Zhang
  • Jiao Pei
  • Yuanyuan Liu
  • Xiaosong Li
  • Pau Medrano-Gracia

Abstract

Background: The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods: In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results: The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion: Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

Suggested Citation

  • Xingyu Zhang & Tao Zhang & Jiao Pei & Yuanyuan Liu & Xiaosong Li & Pau Medrano-Gracia, 2016. "Time Series Modelling of Syphilis Incidence in China from 2005 to 2012," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0149401
    DOI: 10.1371/journal.pone.0149401
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    References listed on IDEAS

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    1. Xingyu Zhang & Yuanyuan Liu & Min Yang & Tao Zhang & Alistair A Young & Xiaosong Li, 2013. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    2. Fong, Pak Wing & Li, Wai Keung, 2003. "On time series with randomized unit root and randomized seasonal unit root," Computational Statistics & Data Analysis, Elsevier, vol. 43(3), pages 369-395, July.
    3. Anne B. Koehler & Emily S. Murphree, 1988. "A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 187-195, June.
    4. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    5. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    6. Galbraith, JohnW. & Zinde-Walsh, Victoria, 1999. "On the distributions of Augmented Dickey-Fuller statistics in processes with moving average components," Journal of Econometrics, Elsevier, vol. 93(1), pages 25-47, November.
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    1. Hadi Bagheri & Leili Tapak & Manoochehr Karami & Zahra Hosseinkhani & Hamidreza Najari & Safdar Karimi & Zahra Cheraghi, 2020. "Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    2. Rui Zhang & Hejia Song & Qiulan Chen & Yu Wang & Songwang Wang & Yonghong Li, 2022. "Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-14, January.
    3. Liping Zhang & Li Wang & Yanling Zheng & Kai Wang & Xueliang Zhang & Yujian Zheng, 2017. "Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics," IJERPH, MDPI, vol. 14(3), pages 1-14, March.

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