ARIMA乘积季节模型在山东省肺结核发病预测中的应用

doi:10.6040/j.issn.1671-7554.0.2021.0049

摘要/Abstract

摘要： 目的根据山东省肺结核的季节性、趋势性建立求和自回归移动平均(ARIMA)乘积季节模型,预测山东省肺结核发病趋势,调整防控措施。方法应用R软件对2010年1月至2019年12月山东省肺结核传染病疫情月度数据建立最优模型,预测2020年1月至10月肺结核发病数,并与实际值进行比较,以此评估模型的预测效果,预测2020年11月至2021年12月的发病趋势。结果山东省肺结核发病数表现为年度周期性,最优模型为ARIMA(3,1,0)(0,1,1)₁₂,2010年1月至2019年12月拟合结果准确性显示平均绝对百分比误差仅为5.50%, 2020年1月至10月模型预测效果的平均相对百分比误差为21.69%,2020年11月至2021年12月的发病数较同期有轻微变化。结论 ARIMA乘积季节模型能够较好地对山东省肺结核发病趋势进行拟合及预测。

关键词: 肺结核, 时间序列, ARIMA模型, 季节性, 预测

Abstract: Objective According to the seasonality and trend of pulmonary tuberculosis in Shandong Province, to establish an Autoregressive Integrated Moving Average(ARIMA)product seasonal model, predict the incidence of pulmonary tuberculosis in Shandong Province and adjust prevention and control measures. Methods An optimal model for the monthly data of pulmonary tuberculosis infectious diseases in Shandong Province from January 2010 to December 2019 was established using R software. The number of pulmonary tuberculosis cases from January to October 2020 was predicted and compared with the actual value to evaluate the prediction effect of the model. Further, the incidence trend of pulmonary tuberculosis cases from November 2020 to December 2021 was predicted. Results The number of pulmonary tuberculosis cases in Shandong Province showed an annual cycle, and the optimal model is ARIMA(3,1,0)(0,1,1)₁₂. The accuracy of fitting results from January 2010 to December 2019 showed that the mean absolute percentage error was only 5.50%. The mean absolute percentage error of the model prediction from January to October 2020 was 21.69%. Compared with the same period, there was a slight change in the number of cases from November 2020 to December 2021. Conclusion The ARIMA product seasonal model can rather satisfactorily fit and predict the incidence of tuberculosis in Shandong Province.

Key words: Tuberculosis, Time series, ARIMA model, Seasonal, Predict

中图分类号:

R183

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.

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.

参考文献

[1] Kang W, Du J, Yang S, et al. The prevalence and risks of major comorbidities among inpatients with pulmonary tuberculosis in China from a gender and age perspective: a large-scale multicenter observational study[J]. Eur J Clin Microbiol Infect Dis, 2021, 40(4): 787-800.
[2] Zhu QQ, Wu Q, Wang AM, et al. Epidemiological characteristics of pulmonary tuberculosis in Anhui Province, Eastern China from 2013 to 2018[J]. PLoS One, 2020, 15(8): e0237311. doi: 10.1371/journal.pone.0237311.
[3] 李硕,张云辉,王永怡,等.2019年国内外传染病领域重要事件回顾[J].传染病信息, 2020, 33(1): 10-17. LI Shuo, ZHANG Yunhui, WANG Yongyi, et al. Review of important events in field of infections disease abroad in 2019[J]. Infectious Disease Information, 2020, 33(1): 10-17.
[4] Chen J, Qiu Y, Yang R, et al. The characteristics of spatial-temporal distribution and cluster of tuberculosis in Yunnan Province, China, 2005-2018[J]. BMC Public Health, 2019, 19(1): 1715. doi: 10.1186/s12889-019-7993-5.
[5] Shuai YX, Zhou ZF. GDP analysis and comparison in coastal cities based on time series analysis[J]. JCR, 2019, 98(sp1): 402-406.
[6] 黄莉,陈田木,杨云斌,等.应用SARIMA模型预测肺结核发病趋势[J].现代预防医学, 2019, 46(5): 794-797, 803. HUANG Li, CHEN Tianmu, YANG Yunbin, et al. Application of seasonal ARIMA model in predicting the incidence of tuberculosis[J]. Modern Preventive Medicine, 2019, 46(5): 794-797, 803.
[7] 孙娜,许小珊,冯佳宁,等.ARIMA与GM(1,1)模型对我国肺结核年发病人数预测情况的比较[J].中国卫生统计, 2019, 36(1): 71-74.
[8] 崔哲哲,林定文,林玫.广西学生肺结核发病趋势分析及预警系统建立[J].中国学校卫生, 2017, 38(1): 144-147.
[9] 罗兴甸,戴家佳,罗登菊.ARIMA乘积季节模型在我国麻疹发病预测中的应用[J].贵州大学学报(自然科学版), 2019, 36(3): 9-14. LUO Xingdian, DAI Jiajia, LUO Dengju. Application of multiple seasonal ARIMA modelin forecasting the incidence of Measles in China[J]. Journal of Guizhou University(Natural Sciences), 2019, 36(3): 9-14.
[10] 杨静,张强.ARIMA模型在成都市成华区狂犬病暴露监测数据分析中的应用[J].中国卫生统计, 2016, 33(5): 755-757, 762. YANG Jing, ZHANG Qiang. ARIMA model in prediction of the rabies surveillance in Chenghua District of Chengdu City[J]. Chinese Journal of Health Statistics, 2016, 33(5): 755-757, 762.
[11] Aliakbar TF, Karamatollah R, Saeed H, et al. Analysis of temporal trends of human brucellosis between 2013 and 2018 in Yazd Province, Iran to predict future trends in incidence: a time-series study using ARIMA model[J]. APJTM,2020,13(6): 272-277.
[12] Zheng A, Fang Q, Zhu YL, et al. An application of ARIMA model for predicting total health expenditure in China from 1978-2022[J]. J Glob Health, 2020, 10(1): 010803. doi: 10.7189/jogh.10.010803.
[13] 苟铁军,杨千三,刘乔虹. 2014年南充市法定传染病流行特征分析[J].职业卫生与病伤, 2015, 30(6): 332-335. GOU Tiejun, YANG Qiansan, LIU Qiaohong. Analysis on the epidemical characteristics of notifiable diseases in Nanchong City, 2014[J]. Occupational Health and Damage, 2015, 30(6): 332-335.
[14] Vera NA, Juariah, Aslam N, et al. The relationship of social support with medication adherence pulmonary tuberculosis patients through DOTS strategy in pidie aceh indonesia[J]. IOP Conference Series: Materials Science and Engineering, 2019, 469(1). doi: 10.1088/1757-899X/469/1/012055.
[15] 张传芳,唐益,徐祖辉,等.湖南省2012—2017年学生肺结核登记情况及流行特征[J].中国感染控制杂志, 2018, 17(11): 1008-1012. ZHANG Chuanfang, TANG Yi, XU Zuhui, et al. Registration and epidemiological characteristics of pulmonary tuberculosis among students in Hunan Province from 2012 to 2017[J]. Chinese Journal of Infection Control, 2018, 17(11): 1008-1012.
[16] 郑榕,张自力,张璿璿.高等教育、公共卫生意识和健康行为——兼论高校扩招对“新冠肺炎”疫情防控的影响[J].经济与管理评论, 2020, 36(6): 5-15. ZHENG Rong, ZHANG Zili, ZHANG Jingjing. Higher education, public health awareness and health behavior—a discussion of the impact of college enrollment expansion on COVID-19 epidemic prevention and control[J]. Review of Economy and Management, 2020, 36(6): 5-15.
[17] 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.
[18] 任正洪.2005~2011年我国肺结核发病的时间流行病学特征及趋势[J].中国卫生统计, 2013, 30(2): 158-161. REN Zhenghong. The temporal characteristics and trend of tuberculosis incidence cases in China[J]. Chinese Journal of Health Statistics, 2013, 30(2): 158-161.
[19] 沈岿.论突发传染病信息发布的法律设置[J].当代法学, 2020, 34(4): 27-36. SHEN Kui. On the legal setting of information release of sudden infectious diseases[J]. Contemporary Law Review, 2020, 34(4): 27-36.
[20] Liu Q, Li ZQ, Ji Y, et al. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses[J]. Infect Drug Resist, 2019, 12: 2311-2322. doi: 10.2147/IDR.S207809.
[21] Qi C, Zhang DD, Zhu YC, et al. SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA[J]. BMC Med Res Methodol, 2020, 20(1):243. doi: 10.1186/s12874-020-01130-8.
[22] Rachel TA, Gilbert MT. Forecasting measles immunization coverage using ARIMA model[J]. Journal of Computer and Communications, 2019, 7(10): 157-168.
[23] He WC, Ju K, Gao YM, et al. Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011-2017: a spatial analysis[J]. Infect Dis Poverty, 2020, 9(1): 159. doi: 10.1186/s40249-020-00778-0.
[24] 李欣阳,李素娟,刘晓迪,等.自回归移动平均乘积季节模型在甲型肝炎发病数中的应用[J].山东大学学报(医学版), 2018, 56(12): 103-108. LI Xinyang, LI Sujuan, LIU Xiaodi, et al, Application of multiple seasonal autoregressive integrated moving average model in the prediction of viral hepatitis A[J]. Journal of Shandong University(Health Sciences), 2018, 56(12): 103-108.
[25] 谭慧仪,李纯颖,肖岚,等.湖南省2009-2018年百日咳流行特征分析与发病趋势预测[J]. 中华疾病控制杂志, 2020, 24(11): 1263-1268, 1281. TAN Huiyi, LI Chunying, XIAO Lan, et al. Epidemiological characteristics and trend prediction of pertussis in Hunan Province from 2009 to 2018[J]. Chinese Journal of Disease Control & Prevention, 2020, 24(11): 1263-1268, 1281.

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