您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2018, Vol. 56 ›› Issue (9): 71-76.doi: 10.6040/j.issn.1671-7554.0.2017.1266

• 公共卫生与管理学 • 上一篇    

乘积季节模型在我国肺结核疫情预测中的应用

刘晓迪1,马洁1,修璟威1,崔庆霞1,2,李望晨1,2,王在翔1,2   

  1. 1.潍坊医学院公共卫生与管理学院, 山东 潍坊 261053;2.“健康山东”重大社会风险预测与治理协同创新中心, 山东 潍坊 261053
  • 发布日期:2022-09-27
  • 通讯作者: 王在翔. E-mail:WANGZX1@126.com
  • 基金资助:
    “健康山东”重大社会风险预测与治理协同创新中心重点项目(XT1405003);潍坊医学院科技创新基金(K1302022)

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)

42

摘要： 目的 探讨乘积季节模型预测肺结核发病例数的可行性,为肺结核的针对性防控提供理论依据。 方法 根据中国疾病预防控制中心2011年1月至2016年12月的全国肺结核上报资料建立乘积季节模型,并预测2017年1月至9月数据,评价其预测效果。 结果 2011年1月至2016年12月我国肺结核发病例数呈现以年为周期的季节效应,并且出现长期递减的趋势;乘积季节ARIMA(0,1,1)(0,1,1)12(不含常数项)模型所有参数都通过统计学检验(P<0.05), 残差序列为白噪声序列(P>0.05), 拟合优度相对最好(AIC=1 223.004, SBC=1 227.159);模型对2017年1月至9月的预测值与实际值基本吻合,预测效果较好。 结论 乘积季节ARIMA(0,1,1)(0,1,1)12(不含常数项)模型可用于预测我国肺结核疫情,具有较好的推广应用价值。

关键词: 乘积季节模型, 肺结核, 疫情, 预测

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

中图分类号: 

  • 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] 贺士卿,李皖皖,董书晴,牟婧怡,刘宇莹,魏思雨,刘钊,张家新. 基于数据库构建乳腺癌焦亡相关基因的预后风险模型[J]. 山东大学学报 (医学版), 2022, 60(8): 34-43.
[2] 冯一平,孙大鹏,王显军,纪伊曼,刘云霞. DLNM和LSTM神经网络对临沂市手足口病发病的预测效果比较[J]. 山东大学学报 (医学版), 2022, 60(2): 96-101.
[3] 吕丽,姜璐,陈诗鸿,庄向华,宋玉文,王殿辉,安文娟,李倩,潘喆. 210例绝经后2型糖尿病发生骨质疏松的相关因素[J]. 山东大学学报 (医学版), 2021, 59(7): 19-25.
[4] 李皖皖,周文凯,董书晴,贺士卿,刘钊,张家新,刘斌. 利用数据库信息构建乳腺癌免疫关联lncRNAs风险评估模型[J]. 山东大学学报 (医学版), 2021, 59(7): 74-84.
[5] 田庆,刘永鹏,张晶晶,刘洪庆. ARIMA乘积季节模型在山东省肺结核发病预测中的应用[J]. 山东大学学报 (医学版), 2021, 59(7): 112-118.
[6] 赵洁,李岩,李明,于德新. 螺旋CT对黏液性软组织肿瘤良恶性鉴别的价值[J]. 山东大学学报 (医学版), 2021, 59(4): 100-107.
[7] 张倍,张修磊, 巴桑片多,尼玛次仁,石大春,次仁加布,尹亭亭,胡军. 日喀则市2011至2018年肺结核空间流行特征及预测分析[J]. 山东大学学报 (医学版), 2021, 59(2): 108-113.
[8] 肖宇飞,冯佳宁,王晓璇,毛倩,石福艳,王素珍. 利用数据库数据采用联合模型动态预测312例肝硬化患者预后的观察分析[J]. 山东大学学报 (医学版), 2020, 1(9): 71-76.
[9] 吴强,何泽鲲,刘琚,崔晓萌,孙双,石伟. 基于机器学习的脑胶质瘤多模态影像分析[J]. 山东大学学报 (医学版), 2020, 1(8): 81-87.
[10] 白尧,陈志军,宋姝璇,贺真,陈保忠,邵中军,刘昆. 西安市一起新型冠状病毒肺炎家族聚集性疫情调查分析[J]. 山东大学学报 (医学版), 2020, 58(10): 95-99.
[11] 王玲,曹海霞,张玲,张文娜,潘艳萍,史颖,张伟,崔峰. 淄博市一起新型冠状病毒肺炎家族聚集性疫情调查分析[J]. 山东大学学报 (医学版), 2020, 58(10): 100-104.
[12] 李晓非,黄山,欧阳兵,余婷婷,梁桂亮,王霖. 利福平耐药实时荧光定量核酸扩增技术在肺结核临床路径中的应用[J]. 山东大学学报 (医学版), 2018, 56(6): 35-40.
[13] 马洁,田野,黄璐,孟维静,王素珍,石福艳. ARIMA乘积季节模型与广义回归神经网络模型在布鲁菌病发病预测的比较[J]. 山东大学学报 (医学版), 2018, 56(6): 76-82.
[14] 李吉庆,赵焕宗,宋炳红,张理纯,李向一,陈亚飞,王萍,薛付忠. 基于健康管理队列的心血管事件风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 56-60.
[15] 于涛,刘焕乐,冯新,徐付印,陈亚飞,薛付忠,张成琪. 基于健康管理队列的高血压风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 61-65.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发