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Open AccessArticle

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

1
Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China
2
Shandong Center for Disease Control and Prevention, Jinan 250014, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(8), 925; https://doi.org/10.3390/ijerph14080925
Received: 7 July 2017 / Revised: 16 August 2017 / Accepted: 16 August 2017 / Published: 17 August 2017
(This article belongs to the Section Global Health)
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. View Full-Text
Keywords: mumps; time series analysis; SARIMA model; infectious disease epidemiology mumps; time series analysis; SARIMA model; infectious disease epidemiology
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MDPI and ACS Style

Xu, Q.; Li, R.; Liu, Y.; Luo, C.; Xu, A.; Xue, F.; Xu, Q.; Li, X. Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model. Int. J. Environ. Res. Public Health 2017, 14, 925.

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