Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China.
10.24920/J1001-9294.2017.036
- Author:
Rong-Qiang ZHANG
1
,
2
;
Feng-Ying LI
3
;
Jun-Li LIU
3
;
Mei-Ning LIU
3
;
Wen-Rui LUO
3
;
Ting MA
3
;
Bo MA
3
;
Zhi-Gang ZHANG
4
Author Information
1. School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China
2. Institute of Endemic Diseases, School of Public Health, Health Science Center, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an 710061, China.
3. Department of Immunology, Center for Disease Control and Prevention of Xianyang City, Xianyang, Shaanxi 712046, China.
4. School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China.
- Publication Type:Journal Article
- From:
Chinese Medical Sciences Journal
2017;32(3):152-160
- CountryChina
- Language:English
-
Abstract:
Objective To construct a model of Seasonal Autoregressive Integrated Moving Average (SARIMA) for forecasting the epidemic of Japanese encephalitis (JE) in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA (1, 1, 1) (2, 1, 1)was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R, and a lower Mean Absolute Percent Error. SARIMA (1, 1, 1) (2, 1, 1)was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA (1, 1, 1) (2, 1, 1)appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally (≥0.4/100 000). According to the predicted Results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.
