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    下一篇

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)

202

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] 王烨,王刚,南海鸥,蔡雨玲,李嘉宇,王海锋,颜英杰,江志伟. 胃癌患者围手术期心率变异度与炎性指标的相关性[J]. 山东大学学报 (医学版), 2026, 64(4): 23-30.
[2] 段盈竹,董波,于睿. 内在情感与类风湿关节炎患者冠状动脉粥样硬化风险关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2026, 64(4): 63-71.
[3] 王建民,李晓峰,由志涛,董圣杰,赵宇驰,李占菊,邹德鑫,张剑锋,孙涛,杜伟. 基于可解释机器学习的后路腰椎椎体间融合术后慢性疼痛风险预测模型构建[J]. 山东大学学报 (医学版), 2026, 64(2): 78-88.
[4] 孙爽爽,仉率杰,张伯韬,袁莹,于媛媛,薛付忠. 基于真实世界研究的18~50岁人群急性缺血性卒中影响因素[J]. 山东大学学报 (医学版), 2025, 63(9): 40-46.
[5] 王梦星,薛付忠,杨帆. 基于多模态交叉注意力机制融合的1型糖尿病血糖浓度预测方法[J]. 山东大学学报 (医学版), 2025, 63(8): 41-50.
[6] 申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[7] 李千,杨帆,薛付忠. 基于多模态数据融合的多癌种风险预测模型[J]. 山东大学学报 (医学版), 2025, 63(8): 79-85.
[8] 李晓琪,刘佩丽,成红,赵艳艳. 基于自注意力机制预测ICU脓毒症患者的死亡率[J]. 山东大学学报 (医学版), 2025, 63(8): 86-93.
[9] 陈莹莹,王鲁,胡锡峰,朱高培,薛付忠. 基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测[J]. 山东大学学报 (医学版), 2025, 63(8): 94-102.
[10] 王丽云,高天勤,刘雨佳,陈青,陈柳,沙凯辉. 基于机器学习产后压力性尿失禁风险预测模型的构建及验证[J]. 山东大学学报 (医学版), 2025, 63(6): 55-66.
[11] 杜雪,李春霞,刘云霞,张涛. 基于MFPC-Cox的结直肠癌患者预后动态预测模型[J]. 山东大学学报 (医学版), 2025, 63(5): 101-110.
[12] 陈瑛翼,游倩,王意,张帆,李凤,季舒铭,徐浩源,饶志勇. 办公室职员肌肉质量减少预测模型的开发与验证[J]. 山东大学学报 (医学版), 2025, 63(4): 26-35.
[13] 王宝炫,焦杰,张厚君,刘奇,于冠英. 衰弱与肌少症评估在胃肠道肿瘤术后结局预测中的应用与展望[J]. 山东大学学报 (医学版), 2025, 63(4): 51-58.
[14] 刘文钊,张远,马湘萍,魏峰涛,卜培莉. 内皮活化和应激指数预测值与心力衰竭患者死亡风险的关联[J]. 山东大学学报 (医学版), 2025, 63(11): 27-35.
[15] 李永,崔书君,杨飞,张凡,殷晓霞. 基于增强MRI的亚区域影像组学模型可预测乳腺癌患者新辅助化疗后的病理完全反应[J]. 山东大学学报 (医学版), 2025, 63(1): 81-89.
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