构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型

张荣强; 李凤英; 刘军礼; 刘美宁; 罗文瑞; 马婷; 马波; 张志刚

doi:10.24920/J1001-9294.2017.036

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构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型

中国医学科学杂志（英文版） 2017年32卷第3期页码：152-160

Orginal Article | Updated：2024-04-10

- 构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型
- Affiliations：
  
  1. 1School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China
  2. 2Institute 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. 3Department of Immunology, Center for Disease Control and Prevention of Xianyang City, Xianyang, Shaanxi 712046, China
- Author bio：
  
  *Corresponding author Tel: 86-29-38185219, Fax: 86-29-38185219, E-mail: Zhangrqxianyang@163.com
- Funds：
  
  △Supported by the Youth Project of Shaanxi University of Chinese Medicine (2015QN05).
- DOI：10.24920/J1001-9294.2017.036
  中图分类号：
- 收稿日期：2016-12-21，
  
  网络出版日期：2017-09-26，
  
  纸质出版日期：2017-09
- Accepted：
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张荣强, 李凤英, 刘军礼, 等. 构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型[J]. 中国医学科学杂志（英文版）, 2017,32(3):152-160.

Zhang Rong-qiang, Li Feng-ying, Liu Jun-li, et al. Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China^△[J]. Chinese medical sciences journal, 2017, 32(3): 152-160.
张荣强, 李凤英, 刘军礼, 等. 构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型[J]. 中国医学科学杂志（英文版）, 2017,32(3):152-160. DOI： 10.24920/J1001-9294.2017.036.

Zhang Rong-qiang, Li Feng-ying, Liu Jun-li, et al. Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China^△[J]. Chinese medical sciences journal, 2017, 32(3): 152-160. DOI： 10.24920/J1001-9294.2017.036.

摘要

目的

通过构建季节性时间序列模型（SARIMA），预测陕西省咸阳市日本脑炎疫情，为日本脑炎的预防控制提供参考。

方法

采用理论流行病学研究设计，收集2005年1月至2014年9月陕西省咸阳市日本脑炎的发病率资料，采用Box和Jenkins方法建立最佳的SARIMA模型，并对咸阳市日本脑炎的发病率进行短期预测。

结果

研究结果显示：SARIMA（1

1）（2

1）12是最佳模型，该模型具有最低的贝叶斯信息标准（BIC），Akaike信息标准（AIC），平均绝对误差（MAE）值及最高的R2和较低的平均绝对百分比误差（MAPE）；SARIMA（1

1）（2

1）12可对咸阳市日本脑炎发病率进行较为可靠的预测。

结论

SARIMA（1

1）（2

1）12可为咸阳市早期识别日本脑炎发病率异常升高（≥0.4/100 000）提供预警；咸阳市日本脑炎的发病率将略有下降，从6月至8月达到顶峰。

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) (2

was considered to be the best model with the lowest Bayesian information criterion

Akaike information criterion

Mean Absolute Error values

the highest

and a lower Mean Absolute Percent Error. SARIMA (1

1) (2

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) (2

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.

关键词

Keywords

references

Diagana M , Preux PM , Dumas M. Japanese encephalitis revisited. J Neurol Sci 2007 ; 262 ( 1-2 ): 165 - 70 . doi: 10.1016/j.jns.2007.06.041 10.1016/j.jns.2007.06.041 .

Ghimire S , Dhakal S. Japanese encephalitis: challenges and intervention opportunities in Nepal. Vet World 2015 , 8 ( 1 ): 61 - 5 . doi: 10.14202/vetworld.2015.61-65 10.14202/vetworld.2015.61-65 .

Misra UK , Kalita J. Overview: Japanese encephalitis. Prog Neurobiol 2010 ; 91 ( 2 ): 108 - 20 . doi: 10.1016/j.pneurobio.2010.01.008 10.1016/j.pneurobio.2010.01.008 .

Jasik KP , Okła H , Słodki J , Rozwadowska B , Słodki A , Rupik W. Congenital tick borne diseases: is this an alternative route of transmission of tick-borne pathogens in mammals? Vector Borne Zoonotic Dis 2015 ; 15 ( 11 ): 637 - 44 . doi: 10.1089/vbz.2015.1815 10.1089/vbz.2015.1815 .

Ai J , Xie Z , Liu G , Chen Z , Yang Y , Li Y , et al. Etiology and prognosis of acute viral encephalitis and meningitis in Chinese children: a multicentre prospective study. BMC Infect Dis 2017 ; 17 ( 1 ): 494 . doi: 10.1186/s12879-017-2572-9 10.1186/s12879-017-2572-9 .

Feng H , Duan G , Zhang R , Zhang W. Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors. PLoS One 2014 ; 9 ( 1 ): e87916 . doi: 10.1371/journal.pone.0087916.eCollection2014 10.1371/journal.pone.0087916.eCollection2014 .

Cappelle J , Duong V , Pring L , Kong L , Yakovleff M , Prasetyo DB , et al. Intensive circulation of Japanese Encephalitis virus in peri-urban Sentinel Pigs near Phnom Penh, Cambodia. PLoS Negl Trop Dis 2016 ; 10 ( 12 ): e0005149 . doi: 10.1371/journal.pntd.0005149 10.1371/journal.pntd.0005149 .

Luo L , Luo L , Zhang X , He X. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res 2017 ; 17 ( 1 ): 469 . doi: 10.1186/s12913-017-2407-9 10.1186/s12913-017-2407-9 .

Bozkurt ÖÖ , Biricik G , Tayşi ZC. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS One 2017 ; 12 ( 4 ): e0175915 . doi: 10.1371/journal.pone.0175915 10.1371/journal.pone.0175915 .

Sharafi M , Ghaem H , Tabatabaee HR , Faramarzi H. Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method. Asian Pac J Trop Med 2017 ; 10 ( 1 ): 79 - 86 . doi: 10.1016/j.apjtm.2016.12.007 10.1016/j.apjtm.2016.12.007 .

Yousefzadeh-Chabok S , Ranjbar-Taklimie F , Malekpouri R , Razzaghi A. A time series model for assessing the trend and forecasting the road traffic accident mortality. Arch Trauma Res 2016 ; 5 ( 3 ): e36570 . doi: 10.5812/atr.36570 10.5812/atr.36570 .

Azeez A , Obaromi D , Odeyemi A , Ndege J , Muntabayi R. Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model . Int J Environ Res Public Health 2016 ; 13 ( 8 ). pii: E757 . doi: 10.3390/ijerph13080757 10.3390/ijerph13080757 .

Zhang G , Huang S , Duan Q , Shu W , Hou Y , Zhu S , et al. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One 2013 ; 8 ( 11 ): e80969 . doi: 10.1371/journal.pone.0080969 10.1371/journal.pone.0080969 .

Kam HJ , Sung JO , Park RW. Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthc Inform Res 2010 ; 16 ( 3 ): 158 - 65 . doi: 10.4258/hir.2010.16.3.158 10.4258/hir.2010.16.3.158 .

Guo Chen , Amy K. Glasmeier , Min Zhang , et al. Urbanization and income inequality in post-reform China: a causal analysis based on time series data. PLoS One 2016 ; 11 ( 7 ): e0158826 . doi: 10.1371/journal.pone.0158826 10.1371/journal.pone.0158826 .

Box GE , Jenkins GM. Time series analysis: forecasting and control. editors. Oakland: Holden-Day ; 1976 . p. 300 - 33 .

Ren X , Fu S , Dai P , Wang H , Li Y , Li X , et al. Pigsties near dwellings as a potential risk factor for the prevalence of Japanese encephalitis virus in adult in Shanxi, China. Infect Dis Poverty 2017 ; 6 ( 1 ): 100 . doi: 10.1186/s40249-017-0312-4 10.1186/s40249-017-0312-4 .

Yu L , Zhou L , Tan L , Jiang H , Wang Y , Wei S , et al. Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One 2014 ; 9 ( 6 ): e98241 . doi: 10.1371/journal.pone.0098241 10.1371/journal.pone.0098241 .

IHS EViews EViews 7 . [cited 2016 Nov 1]. Available from: http://www.eviews.com/download/download.html#eviews7 (English http://www.eviews.com/download/download.html#eviews7 (English .

Wang X , Li SH , Zhu L , Nian QG , Yuan S , Gao Q , et al. Near-atomic structure of Japanese encephalitis virus reveals critical determinants of virulence and stability. Nat Commun 2017 ; 8 ( 1 ): 14 . doi: 10.1038/s41467-017-00024-6 10.1038/s41467-017-00024-6 .

Parmar KS , Bhardwaj R. Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management. Environ Sci Pollut Res Int 2015 ; 22 ( 1 ): 397 - 414 . doi: 10.1007/s11356-014-3346-1 10.1007/s11356-014-3346-1 .

Heffelfinger JD , Li X , Batmunkh N , Grabovac V , Diorditsa S , Liyanage JB , et al. Japanese Encephalitis Surveillance and Immunization-Asia and Western Pacific Regions, 2016. MMWR Morb Mortal Wkly Rep 2017 ; 66 ( 22 ): 579 - 83 . doi: 10.15585/mmwr.mm6622a3 10.15585/mmwr.mm6622a3 .

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