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Application of ARIMA model to prediction of tuberculosis incidence |
HU Bi-bo,FU Ke-ben,XU Liang-liang,HE Li-ping
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Yuyao Center for Disease Control and Prevention,Yuyao,Zhejiang 315400,China |
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Abstract Objective To apply the autoregressive integrated moving average (ARIMA) model to predicting the tuberculosis (TB) incidence. Methods Based on the data of TB incidence in Yuyao from 2006 to 2016,the ARIMA model was established using the Expert Modeler and the traditional modeling process. The optimal model was selected according to the minimum value of Bayesian information criterion (BIC) to fit the monthly incidence rate of TB from 2006 to 2016 and to predict the incidence of TB in 2017. Results The traditional modeling process established ARIMA(0,1,1) (0,1,1)12 and the Expert Modeler established ARIMA(0,0,1) (0,1,1) 12 . The two models' residual sequences did not break through the confidence intervals and both were appropriate. The ARIMA (0,0,1) (0,1,1) 12 was the optimal model due to a smaller standardized BIC value. When fitting the monthly incidence of TB in Yuyao from 2006 to 2016,the actual incidence of TB fell into 95% confidence intervals of the fitting value and the predicted value could fit the original data. When predicting the monthly incidence of TB in 2017,the mean relative error was 9.05%. Conclusio The ARIMA model constructed by Expert Modeler is suitable for predicting TB incidence.
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Published: 26 September 2018
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[1] World Health Organization. Global tuberculosis report 2017[R] . Geneva: WHO,2017. [2] 钟威,毛宁,曹宏伟, 等. 辽宁省2006—2015年流动人口结核病流行病学特征分析[J] . 中华疾病控制杂志,2017,21(10):1044-1047. [3] AGARWAL S K,SINGH A,ANURADHA S,et a1.Cytokine profile in human immunodeficiency virus positive patients with and without tuberculosis[J] . The Journal of the Association of Physicians of India,2001,49:799-802. [4] 张国钦,钟达. 耐药肺结核发生和流行的危险因素[J] . 中国慢性病预防与控制,2017,25(7):557-560. [5] 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, 32(1):106-107. [6] 高雅,王伶,吴伟,等. 辽宁省手足口病疫情季节性ARIMA模型预测效果评价[J] . 中国公共卫生,2017,33(10):1482- 1484. [7] LUZ P M,MENDES B V M,CODE?O C T,et al. Time series analysis of dengue incidence in Rio de Janeiro,Brazil[J] . Am J Trop Med Hyg,2008,79(6):933-939. [8] 王金娜,徐若君,黄大锟,等. ARIMA乘积季节模型预测永嘉县其他感染性腹泻的流行[J] . 预防医学,2017,29(2):150-154. [9] 李杰,顾月. ARIMA模型在预测手足口病发病中的应用[J] . 预防医学,2016,28(10):987-991. [10] 任江萍,陈直平,孙继民,等. 全国人间狂犬病疫情的时间序列分析[J] . 中国人兽共患病学报,2018,34(3):239-242. [11] 李鹏,杨世宏,马磊,等. 基于时间序列的云南省乙类传染病分析预测[J] . 病毒学报,2018,34(2):201-207. [12] LI Q,GUO N N,HAN Z Y,et al. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome[J] . Am J Trop Med Hyg,2012,87(2):364-370. [13] 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. [14] 姚梦雷,刘天,黄继贵,等. 自回归求和移动平均模型在荆州市手足口病疫情预测预警中的应用[J] . 预防医学论坛,2017, 23(11):804-806. [15] 杨小兵,汪鹏,江高峰. ARIMA乘积季节模型在流行性腮腺炎发病率预测中的应用[J] . 公共卫生与预防医学,2013,24(6):39-42. [16] 王平. 三种预测模型在主要传染病发病率预测中的应用[D] . 杭州:浙江大学,2010. [17] 尹遵栋,罗会明,李艺星,等. 时间序列分析(自回归求和移动平均模型)在流行性乙型脑炎预测中的应用[J] . 中国疫苗和免疫,2010,16(5):457-461. [18] 王怡,张震,范俊杰,等. ARIMA模型在传染病预测中的应用[J] . 中国预防医学杂志,2015,16(6):424-428. |
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