Journal of Shandong University (Health Sciences) ›› 2018, Vol. 56 ›› Issue (9): 71-76.doi: 10.6040/j.issn.1671-7554.0.2017.1266

Previous Articles    

Application of multiple seasonal model in prediction of tuberculosis epidemic

LIU Xiaodi1, MA Jie1, XIU Jingwei1, CUI Qingxia1,2, LI Wangchen1,2,WANG Zaixiang1,2   

  1. 1. School of Public Health and Management, Weifang Medical University, Weifang 261053, Shandong, China;
    2. “Health Shandong” Major Social Risk Prediction and Governance Collaborative Innovation Center, Weifang 261053, Shandong, China
  • Published:2022-09-27

PDF (PC)

43

Abstract: Objective To explore the feasibility of moving multiple seasonal model for predicting tuberculosis and provide a scientific evidence for the targeted prevention and control policy of tuberculosis. Methods Based on the national tuberculosis data reported by the Chinese Center for Disease Control and Prevention from January 2011 to December 2016, a multiplicative seasonal model was established. The model was used to predict the number of cases of tuberculosis in China from January to September 2017 and to evaluate its predicted effect. Results From January 2011 to December 2016, the number of pulmonary tuberculosis cases in China showed a seasonal effect with annual cycle, and the trend of long-term declining. All parameters of ARIMA(0,1,1)(0,1,1)12(without constant term)were statistically tested(P<0.05). The residual sequence was a white noise sequence(P>0.05). Goodness of fit was the best(AIC=1 223.004, SBC=1 227.159). The projections for January to September 2017 were generally consistent with the actual values. Conclusion ARIMA(0,1,1)(0,1,1)12(without constant term)can be used to predict the epidemic situation of pulmonary tuberculosis in China. The model has good popularization and application value.

Key words: Multiple seasonal model, Tuberculosis, Epidemic, Prediction

CLC Number: 

  • R183
[1] World Health Organization. Global tuberculosis report 2017[EB/OL]. http://www.who.int/tb/publications/global_report/en/, 2017-11-06/2017-12-26.
[2] 杨召,叶中辉,尤爱国,等.乘积季节ARIMA模型在结核病发病率预测中应用[J].中国公共卫生, 2013, 29(4): 469-472. YANG Zhao, YE Zhonghui, YOU Aiguo, et al. Application of multiple seasonal ARIMA model in prediction of tuberculosis incidence[J]. Chin J Public Health, 2013, 29(4): 469-472.
[3] 中国疾病预防控制中心.2016年全国法定传染病疫情概况[EB/OL]. http://www.nhfpc.gov.cn/jkj/s3578/201702/38ca5990f8a54ddf9ca6308fec406157.shtml, 2017-02-23/2017-12-26.
[4] 孟凡东,吴迪,隋承光.2004-2015年中国狂犬病发病数据ARIMA乘积季节模型的建立及预测[J].中国卫生统计, 2016, 33(3): 389-391, 395. MENG Fandong, WU Di, SUI Chengguang. Human rabies incidence in China: trends and predictions from a time series analysis from 2004 through 2015[J]. Chinese Journal of Health Statistics, 2016, 33(3): 389-391, 395.
[5] 娄鹏威.新疆巴州地区布鲁氏菌病模型的分析与仿真[D].乌鲁木齐:新疆医科大学, 2017.
[6] 梁纪伟,姜法春,韩雅琳,等.应用ARIMA乘积季节模型预测青岛市甲肝发病[J].中国公共卫生管理, 2016, 32(6): 780-782, 793. LIANG Jiwei, JIANG Fachun, HAN Yalin, et al. Application of multiple seasonal ARIMA model in forecasting incidence of hepatitis A in Qingdao[J]. Chinese Journal of Public Health Management, 2016, 32(6): 780-782, 793.
[7] 黄惠珍.我国肺结核流行的主要危险因素及干预措施研究进展[J].中外医学研究, 2017, 15(11): 162-164.
[8] 尹小芳,葛海波.住院肺结核合并糖尿病患者流行病学特征[J].山东大学学报(医学版), 2016, 54(1): 58-61. YIN Xiaofang, GE Haibo. Epidemiological features of inpatients suffered from pulmonary tuberculosis with diabetes mellitus[J]. Journal of Shandong University(Health Sciences), 2016, 54(1): 58-61.
[9] 胡晓媛,孙庆文,王玲玲,等.基于乘积SARIMA模型的肺结核发病率预测[J].第二军医大学学报, 2016, 37(8): 969-974. HU Xiaoyuan, SUN Qingwen, WANG Lingling, et al. Multiplicative SARIMA model for prediction of pulmonary tuberculosis incidence[J]. Academic Journal of Second Military Medical University, 2016, 37(8): 969-974.
[10] 黄文辉,邹林南.疏系数ARIMA模型预测江西省肺结核发病趋势[J].安徽预防医学杂志, 2016, 22(3): 145-148, 179. HUANG Wenhui, ZOU Linnan. Application of Sparse coefficient ARIMA model in predicting incidence trend of tuberculosis in JiangXi province[J]. Anhui J Prev Med, 2016, 22(3): 145-148, 179.
[11] 陈会枝,孟伟伟,贺付成.人工神经网络与灰色理论模型在传染病中的应用[J].中国实用神经疾病杂志, 2016, 19(2): 51-52.
[12] 胡晓媛,吴娟,孙庆文,等.ARIMA模型与GRNN模型对肺结核发病率预测的对比研究[J].第二军医大学学报, 2016, 37(1): 115-119. HU Xiaoyuan, WU Juan, SUN Qingwen, et al. Comparative study on ARIMA model and GRNN model for predicting the incidence of tuberculosis[J]. Anhui J Prev Med, 2016, 37(1): 115-119.
[13] 李大罕,张建花,刘涛.应用灰色模型预测连云港市肺结核流行趋势[J].江苏预防医学, 2015, 26(5): 20-21. LI Dahan, ZHANG Jianhua, LIU Tao. Application of grey model on prediction of tuberculosis epidemic trend in Lianyungang[J]. Jiangsu J Prev Med, 2015, 26(5): 20-21.
[14] 柳巍,曾令城,李焕芝.灰色残差GM(1,1)模型在预测肺结核流行趋势中的应用[J].河南医学研究, 2015, 24(7): 1-3. LIU Wei, ZENG Lingcheng, LI Huanzhi. Application of the Grey Residual GM(1,1)Model in predicting the epidemic tendency of lung tuberculosis[J]. Henan Medical Research, 2015, 24(7): 1-3.
[15] 王建书,刘强,覃江纯,等.基于ARIMA乘积季节模型的苏州市介水传染病发病预测研究[J].环境卫生学杂志, 2017, 7(6): 417-420. WANG Jianshu, LIU Qiang, QIN Jiangchun, et al. Prediction of incidence for water-borne diseases on a multiple seasonal ARIMA model in Suzhou[J]. Journal of Environmental Hygiene, 2017, 7(6): 417-420.
[16] 杨小兵,孔德广,江高峰.ARIMA乘积季节模型在手足口病发病预测中的应用研究[J].中国预防医学杂志, 2016, 17(3): 207-211. YANG Xiaobing, KONG Deguang, JIANG Gaofeng. Application of multiple seasonal ARIMA model in the prediction of the incidence of hand-foot-mouth disease[J]. Chin Prev Med, 2016, 17(3): 207-211.
[17] 刘晓芬.胶南市1990~2012年病毒性肝炎流行特征分析及预测研究[D].济南:山东大学, 2014.
[18] 张泽武,卢展鹏,曾耀明,等.ARIMA模型在东莞市细菌性痢疾预测中的应用[J].公共卫生与预防医学, 2013, 24(4): 43-45. ZHANG Zewu, LU Zhanpeng, ZENG Yaoming, et al. Application of autoregressive integrated moving average model in forecasting bacillary dysentery in Dongguan City[J]. J of Pub Health and Prev Med, 2013, 24(4): 43-45.
[19] 彭志行,陶红,贾成梅,等.时间序列分析在麻疹疫情预测预警中的应用研究[J].中国卫生统计, 2010, 27(5): 459-463. PENG Zhihang, TAO Hong, JIA Chengmei, et al. The applied Research of the time series analysis in the forecasting and early waming of infectious diseases[J]. Chinses Journal of Health Statistics, 2010, 27(5): 459-463.
[20] 王丹霞,林伟,饶正远,等.灰色模型在四川省肺结核疫情预测中的应用[J].应用预防医学, 2015, 21(3): 144-147. WANG Danxia, LIN Wei, RAO Zhengyuan, et al. Application of grey model to predict epidemic trends of tuberculosis in Sichuan Province[J]. J Applied Prev Med, 2015, 21(3): 144-147.
[21] 徐学琴,张知鸷,王瑾瑾,等.基于改进BP神经网络模型的肺结核发病率预测[J].中国现代医学杂志, 2017, 27(23): 124-126.
[22] 陈银苹,吴爱萍,余亮科,等.组合模型对肺结核发病趋势的预测研究[J].中国全科医学, 2014, 17(21): 2452-2456. CHEN Yinping, WU Aiping, YU Liangke, et al. Combination model for predicting the incidence of pulmonary tuberculosis[J]. Chinese General Practic, 2014, 17(21): 2452-2456.
[23] 刘继恒,白春林,孙要武,等.应用ARIMA模型预测肺结核报告发病率的研究[J].中国热带医学, 2014, 14(9): 1067-1070. LIU Jiheng, BAI Chunlin, SUN Yaowu, et al. Application of ARIMA model in prediction of tuberculosis incidence[J]. China Tropical Medicine, 2014, 14(9): 1067-1070.
[24] 温亮,张秀山,李承毅,等.季节分解法和ARIMA法预测乌鲁木齐市肺结核发病趋势效果分析[J].军事医学, 2017, 41(4): 287-290. WEN Liang, ZHANG Xiushan, LI Chengyi, et al. Seasonal decomposition and ARIMA methods in prediction of tuberculosis incidence in Urumqi, China[J]. Mil Med Sci, 2017, 41(4): 287-290.
[25] 王健,周脉耕,胡嘉,等.求和自回归移动平均模型在江西省结核病发病预测中应用[J].疾病监测, 2012, 27(6): 462-467. WANG Jian, ZHOU Maigeng, HU Jia, et al. Application of ARIMA model in predicting tuberculosis incidence in Jiangxi[J]. Disease Surveillance, 2012, 27(6): 462-467.
[26] 陈建,陈佰锋,文育锋,等.2004-2014年全国肺结核月发病率时间趋势分析[J].中国公共卫生管理, 2016, 32(4): 438-440. CHEN Jian, CHEN Baifeng, WEN Yufeng, et al. Analysis of temporal trend of the monthly incidence of pulmonary tuberculosis in China, 2004-2014[J]. Chinese Journal of Public Health Management, 2016, 32(4): 438-440.
[1] HE Shiqing, LI Wanwan, DONG Shuqing, MOU Jingyi, LIU Yuying, WEI Siyu, LIU Zhao, ZHANG Jiaxin. Construction of a prognostic risk model of pyroptosis-related genes in breast cancer based on database [J]. Journal of Shandong University (Health Sciences), 2022, 60(8): 34-43.
[2] LI Yanli, LI Yan, SHE Kaili, LIU Ying, LIU Weijuan, LI Xiujun, LIU Yinghui. Prevalence and risk factors of latent tuberculosis infection among nursing staff in some designated hospitals in Shandong Province [J]. Journal of Shandong University (Health Sciences), 2022, 60(4): 107-112.
[3] FENG Yiping, SUN Dapeng, WANG Xianjun, JI Yiman, LIU Yunxia. Comparison of prediction effects of DLNM and LSTM neural network on the incidence of hand, foot and mouth disease in Linyi City [J]. Journal of Shandong University (Health Sciences), 2022, 60(2): 96-101.
[4] TIAN Qing, LIU Yongpeng, ZHANG Jingjing, LIU Hongqing. Application of ARIMA multiplicative seasonal model in the prediction of pulmonary tuberculosis incidence in Shandong Province [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 112-118.
[5] ZHAO Jie, LI Yan, LI Ming, YU Dexin. Spiral CT in differentiating benign and malignant tumors with myxoid degeneration [J]. Journal of Shandong University (Health Sciences), 2021, 59(4): 100-107.
[6] ZHANG Bei, ZHANG Xiulei, BASANG Pianduo, NIMA Ciren, SHI Dachun, CIREN Jiabu, YIN Tingting, HU Jun. Spatial epidemiological characteristics and prediction of tuberculosis in Shigatse City from 2011 to 2018 [J]. Journal of Shandong University (Health Sciences), 2021, 59(2): 108-113.
[7] XIAO Yufei, FENG Jianing, WANG Xiaoxuan, MAO Qian, SHI Fuyan, WANG Suzhen. Prediction of the prognosis of 312 cirrhosis patients using the joint models and database [J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 71-76.
[8] Qiang WU,Zekun HE,Ju LIU,Xiaomeng CUI,Shuang SUN,Wei SHI. A research on multi-modal MRI analysis based on machine learning for brain glioma [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 81-87.
[9] LI Chunyu, ZHU Yuchen, QI Chang, LIU Lili, ZHANG Dandan, WANG Xu, XU Xueli, LI Xiujun. Epidemic dynamics of COVID-19 in Xinyang City, Henan Province [J]. Journal of Shandong University (Health Sciences), 2020, 58(10): 38-43.
[10] LI Xiaofei, HUANG Shan, OUYANG Bing, YU Tingting, LIANG Guiliang, WANG Lin. Applicative value of gene Xpert MTB/RIF for the rapid detection of pulmonary tuberculosis in the clinical pathway [J]. Journal of Shandong University (Health Sciences), 2018, 56(6): 35-40.
[11] MA Jie, TIAN Ye, HUANG Lu, MENG Weijing, WANG Suzhen, SHI Fuyan. Comparison of multiple seasonal ARIMA model and generalized regression neural network model in forecasting the incidence of brucellosis [J]. Journal of Shandong University (Health Sciences), 2018, 56(6): 76-82.
[12] LI Jiqing, ZHAO Huanzong, SONG Binghong, ZHANG Lichun, LI Xiangyi, CHEN Yafei, WANG Ping, XUE Fuzhong. Risk prediction model of cardiovascular disease based on health management cohort [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 56-60.
[13] YU Tao, LIU Huanle, FENG Xin, XU Fuyin, CHEN Yafei, XUE Fuzhong, ZHANG Chengqi. A hypertension risk prediction model based on health management cohort [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 61-65.
[14] WANG Chunxia, XU Yibo, YANG Ning, XIA Bing, WANG Ping, XUE Fuzhong. A prediction model for coronary heart disease risks based on health management cohort [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 66-71.
[15] ZHANG Guang, WANG Guangyin, WU Hongyan, ZHANG Hongyu, WANG Tingting, LI Jiqing, LI Min, KANG Fengling, LIU Yanxun, XUE Fuzhong. A prediction model of hyperlipidemia risk based on the health management population [J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 72-76.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd