Prediction of hand-foot-mouth disease epidemic with seasonal autoregressive integrated moving average model
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摘要: 目的 利用时间序列法研究手足口病的发病趋势和流行特征,建立ARIMA乘积季节模型,对2017年7月—2018年12月辽宁省手足口病的月发病疫情情况及流行强度进行预测,为手足口病的预防监测工作提供依据。方法 收集辽宁省手足口病2012年1月—2016年12月的月发病报告数,采用Excel 2010建立辽宁省手足口病月报告发病数数据库,应用SPSS 23.0软件进行SARIMA模型的构建,拟合发病情况,对辽宁省手足口病2017年7月—2018年12月发病数进行预测,评价预测效果。结果 辽宁省手足口病发病特征以年为流行周期,季节性周期为12个月(s=12)。每年6—9月为该病的发病高峰期。最佳模型为SARIMA(0,1,0)×(1,1,0)12季节性模型,模型残差Ljung-Box Q=18.564,P=0.354,序列为白噪声。预测平均相对误差为0.229,预测效果较好。结论 季节性ARIMA模型能较好的拟合辽宁省手足口病的发病流行趋势,能够比较直观准确的反映辽宁省手足口病的疫情发展情况,该模型适用于辽宁省手足口病的短期流行趋势的预测。
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关键词:
- 手足口病 /
- 季节性ARIMA模型 /
- 辽宁省 /
- 预测
Abstract: Objective To describe the prevalence and incidence trend of hand-foot-mouth disease (HFMD) using time series method and to establish a seasonal autoregressive integrated moving average (SARIMA) model for the prediction of HFMD epidemic from July 2017 to December 2018 in Liaoning province.Methods A prediction model was set up based on reported monthly data of HFMD from January 2012 through 2016 in Liaoning province using Excel and SPSS 23.0 software.Then the model was used to predict monthly incidents of HFMD from July 2017 to December 2018 and the prediction efficiency was evaluated.Results Yearly cycle and 12-month seasonal period of HFMD epidemic were observed.There was an incidence peak for HFMD epidemic from June to September during a year in Liaoning pro-vince.The established optimum model was SARIMA(0,1,0)×(1,1,0)12.The Ljung-Box Q statistics is 18.564(P=0.354).The noise of residual series of the ARIMA model was white noise and the mean error was 0.229.Conclusion The established seasonal ARIMA model could well fit the monthly data of HFMD epidemic in Liaoning province and the model could be applied to predict short-term incident trend of HFMD in the province.-
Key words:
- hand-foot-mouth disease /
- seasonal ARIMA /
- model /
- prediction
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[1] The Ministry of Health of the People's Republic of China.Guideline of hand foot and mouth disease prevention and control[S].Beijing:The Ministry of Health of the People's Republic of China,2014. [2] Vcntarola D,Bordonc L,Silverberg N.Update on hand-foot-and-mouth disease[J].Clin Dermatol,2015,33(3):340-346. [3] Wang ZL,Xia AM,Li YF,et al.Socioeconomic burden of hand,foot and mouth disease in children in Shanghai,China[J].Epidemiol Infect,2016,144(1):138-143. [4] 王永斌,柴峰,李向文,等.ARIMA模型与残差自回归模型在手足口病发病预测中的应用[J].中华疾病控制杂志,2016(3):303-306. [5] 陈银苹,吴爱萍,余亮科.组合模型对乙肝发病趋势的预测研究[J].解放军医学杂志,2014(1):52-56. [6] 潘浩,胡家瑜,吴寰宇,等.GM(1,1)灰色模型和ARIMA模型在上海市手足口病发病率预测应用中的比较研究[J].中华疾病控制杂志,2011(5):445-448. [7] Earnest A,Chen MI,Ng D,et al.Using autoregressive integrated moving average(AR IMA)models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore[J].BMC Health Serv Res,2005,5(1):36. [8] Qunel P,Dab W.Influenza A and B epidemic criteria based on time series analysis of health services surveillance data[J].Eur J Epidemiol,1998,14(3):275-285. [9] Liu Q,Liu X,Jiang B,et al.Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model[J].BMC lnfect Dis,2011,11(1):218. [10] 陈纯,肖新才.季节性ARIMA模型在广州市手足口病疫情预测中的应用[J].中国预防医学杂志,2016( 2):90-94. [11] 李标,李雪梅,古丽斯.ARIMA模型在手足口病预测预警中的应用[J].中国卫生产业,2014(23):26-27. [12] 陆波,闵思韬,闵红星,等.应用ARIMA模型预测麻疹发病率的可行性研究[J].中国卫生统计,2015(1):106-107. [13] Zhang X,Zhang T,Young AA,et al.Applications and comparisons of four time series models in epidemiological surveillance data[J].PLoS One,2014,9(2):e88075. [14] 孙佰红,安庆玉,田疆,等.单一ARIMA模型和联合模型比较预测辽宁省感染性腹泻疫情[J].职业与健康,2016(1):76-79. [15] 郑庆鸣,王铁强,刘义,等.ARIMA模型在预测2013年深圳市光明新区手足口病发病趋势中的应用[J].职业与健康,2014(9):1198-1201,1205. [16] 安淑一,赵卓,郭军巧,等.应用时间序列模型预测辽宁省麻疹疫情[J].中国卫生统计,2014(5):781-783. [17] 朋文佳,朱玉,何倩,等.ARIMA乘积季节模型在细菌性痢疾月发病率预测中的应用[J].中国卫生统计,2011(6):645-647. [18] 袁国平,郭祖鹏,杨兴堂,等.手足口病发病影响因素病例对照研究[J].中国公共卫生,2011,27(11):1407-1409. [19] 郭泽强.传染病预测方法的研究[J].职业与健康,2012(5):610-612. [20] Bo YC,Song C,Wang JF,et al.Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand,foot and mouth disease(HFMD)in Mainland China[J].BMC Public Health,2014,14(1):358.
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