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      Application of SARIMA Model in the Prediction of Incident Number of Mumps in Changsha

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          Abstract

          Objective: To predict the incident number of mumps in Changsha using seasonal autoregressive integrated moving average (SARIMA) model.

          Methods: We collected the data of incident number of mumps reported in Changsha from 2005 to 2015, then, with the data between 2005 and 2014 as the modeling data, and that in 2015 as the validation data, we built and validated a SARIMA model and used it to predict the incident number of mumps in Changsha in 2016.

          Results: SARIMA (3,0,0)×(1,0,0) 12 model could well fit the actual data, the expansion of the model was: Yt=222.545+1.225Yt-1-0.713Yt-2+0.291Yt-3+0.366Yt-12-0.448Yt-13+0.261Yt-14-0.107Yt-15+at. The validation data and model prediction data were analyzed, and the results demonstrated that there was significant correlation between them (r=0.61, P<0.001). SARIMA model predicted that the incident number of mumps occurred in Changsha in the whole year of 2016 would reach 3 032, the average monthly incident number was 253.

          Conclusion: SARIMA model can be used to predict the incident number of mumps. It predicted that the mumps outbreak might still show a trend of high incidence in Changsha in 2016.

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          Author and article information

          Journal
          CGP
          Chinese General Practice
          Compuscript (Ireland )
          1007-9572
          15 January 2017
          15 January 2017
          : 20
          : 2
          Affiliations
          [1] 1Lanzhou University School of Public Health, Lanzhou 730000, China
          [2] 2Changsha Center for Disease Control and Prevention, Changsha 410004, China
          Author notes
          Corresponding author: ZHANG Ben-zhong, Professor; E-mail: Zhangbzh@ 123456lzu.edu.cn
          Article
          j.issn.1007-9572.2017.02.013
          10.3969/j.issn.1007-9572.2017.02.013
          65fcabfc-c56e-4f39-8388-c74a5c7c1017
          © 2017 Chinese General Practice

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

          History
          Categories
          Abstract

          Endocrinology & Diabetes,General medicine,Occupational & Environmental medicine,Internal medicine,Health & Social care
          SARIMA,Mumps,Time series,Forecasting

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