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Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model

Author

Listed:
  • Qinqin Xu

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

  • Runzi Li

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

  • Yafei Liu

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

  • Cheng Luo

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

  • Aiqiang Xu

    (Shandong Center for Disease Control and Prevention, Jinan 250014, China)

  • Fuzhong Xue

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

  • Qing Xu

    (Shandong Center for Disease Control and Prevention, Jinan 250014, China)

  • Xiujun Li

    (Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China)

Abstract

This study aimed to predict the incidence of mumps using a seasonal autoregressive integrated moving average (SARIMA) model, and provide theoretical evidence for early warning prevention and control in Zibo City, Shandong Province, China. Monthly mumps data from Zibo City gathered between 2005 and 2013 were used as a training set to construct a SARIMA model, and the monthly mumps in 2014 were defined as a test set for the model. From 2005 to 2014, a total of 8722 cases of mumps were reported in Zibo City; the male-to-female ratio of cases was 1.85:1, the age group of 1–20 years old accounted for 94.05% of all reported cases, and students made up the largest proportion (65.89%). The main serious endemic areas of mumps were located in Huantai County, Linzi District, and Boshan District of Zibo City. There were two epidemic peaks from April to July and from October to January in next year. The fitted model SARIMA (0, 1, 1) (0, 1, 1) 12 was established (AIC = 157.528), which has high validity and reasonability. The SARIMA model fitted dynamic changes of mumps in Zibo City well. It can be used for short-term forecasting and early warning of mumps.

Suggested Citation

  • Qinqin Xu & Runzi Li & Yafei Liu & Cheng Luo & Aiqiang Xu & Fuzhong Xue & Qing Xu & Xiujun Li, 2017. "Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model," IJERPH, MDPI, vol. 14(8), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:8:p:925-:d:108685
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    References listed on IDEAS

    as
    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. Li-Ping Yang & Si-Yuan Liang & Xian-Jun Wang & Xiu-Jun Li & Yan-Ling Wu & Wei Ma, 2015. "Burden of Disease Measured by Disability-Adjusted Life Years and a Disease Forecasting Time Series Model of Scrub Typhus in Laiwu, China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(1), pages 1-9, January.
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    Cited by:

    1. Hong Zhang & Kun Su & Xiaoni Zhong, 2022. "Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China," IJERPH, MDPI, vol. 19(11), pages 1-11, May.
    2. Yong Li & Xianning Liu & Lianwen Wang, 2017. "Modelling the Transmission Dynamics and Control of Mumps in Mainland China," IJERPH, MDPI, vol. 15(1), pages 1-17, December.
    3. Wei Kit Phang & Mohd Hafizi Abdul Hamid & Jenarun Jelip & Rose Nani Mudin & Ting-Wu Chuang & Yee Ling Lau & Mun Yik Fong, 2020. "Spatial and Temporal Analysis of Plasmodium knowlesi Infection in Peninsular Malaysia, 2011 to 2018," IJERPH, MDPI, vol. 17(24), pages 1-21, December.

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