| 非稳定性疟区用时间序列模型预测疟疾发病率的可行性研究 |
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| 引用本文： | 朱继民,汤林华,周水森,黄芳. 非稳定性疟区用时间序列模型预测疟疾发病率的可行性研究[J]. 中国寄生虫学与寄生虫病杂志, 2007, 25(3): 232-236 |
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| 作者姓名： | 朱继民 汤林华 周水森 黄芳 |
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| 作者单位： | 1. 中国疾病预防控制中心寄生虫病预防控制所,上海,200025;安徽中医学院中西医结合临床学院,合肥,230038
2. 中国疾病预防控制中心寄生虫病预防控制所,上海,200025 |
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| 基金项目： | 科技部科研院所社会公益研究专项基金 |
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| 摘 要： | 目的 探讨应用季节性时间序列ARIMA模型预测非稳定性疟区疟疾发病率的可行性。 方法 应用SPSS13.0软件对淮河流域河南省桐柏县及安徽省怀远县1998-2005年逐月发病率进行ARIMA建模拟合；按照残差不相关和简洁的原则确定模型结构，依据赤池信息准则（AIC）与贝叶斯信息准则（BIC）确定模型的优度。用所得模型预测2006年两县的月发病率，比较预测值与实际值，检验预测效果；再以1998-2006年的发病率数据构建ARIMA模型，预测2007年疟疾发病率。 结果 模型ARIMA（1，0，0）（0，1，1）12的自回归参数（AR1=0.512）与季节平均移动参数（SMA1=0.609）均通过了统计学检验（P<0.01），AIC=67.01，BIC=71.87，模型残差为白噪声（P>0.05）；该模型很好地拟合了既往时段上的发病率序列，2006年各月疟疾发病率预测值符合实际发病率的变动趋势。预测2007年的疟疾发病率为106.50/10万，发病高峰在7～10月份（占总发病的74.81%）。 结论 ARIMA模型可以很好地模拟疟疾发病率在时间序列上的变动趋势，可用于预测未来的疟疾发病率进行，是一种短期预测精度较高的预测模型。
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| 关 键 词： | 时间序列 ARIMA模型 预测 疟疾 发病率 |
| 文章编号： | 1000-7423（2007）-03-0232-05 |
| 收稿时间： | 2007-01-24 |
| 修稿时间： | 2007-01-24 |
Study on the Feasibility for ARIMA Model Application to Predict Malaria Incidence in an Unstable Malaria Area |
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| ZHU Ji-min,TANG Lin-hua,ZHOU Shui-sen,HUANG Fang. Study on the Feasibility for ARIMA Model Application to Predict Malaria Incidence in an Unstable Malaria Area[J]. Chinese Journal of Parasitology and Parasitic Diseases, 2007, 25(3): 232-236 |
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| Authors: | ZHU Ji-min TANG Lin-hua ZHOU Shui-sen HUANG Fang |
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| Affiliation: | 1 National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; WHO Collaborating Centre for Malaria, Schistosomiasis and Filariasis, Shanghai 200025, China; 2 School of Integrated Traditional and Western Medicine, Anhui College of Traditional Chinese Medicine, Hefei 230038, China. |
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| Abstract: | OBJECTIVE: To explore the application of seasonal time series ARIMA model in prediction of malaria incidence in an unstable malaria area. METHODS: SPSS13.0 software was used to construct the ARIMA model based on the monthly malaria incidence of Huaiyuan and Tongbai counties in Huaihe River Valley, from Jan. 1998 to Dec. 2005, with consideration of residual un-correlation and concision. Akaike's information criterion (AIC) and Bayesian information criterion (BIC) were used to confirm the fitness of model. The constructed model was then applied to predict the monthly malaria incidence in 2006 and the incidence from ARIMA model was compared with the actual incidence, so as to evaluate the model's validity. Malaria incidence of 2007 was predicted by ARIMA model based on malaria incidence from 1998 to 2006. RESULTS: Statistics assisted estimation of the significance of the fitted autoregressive and seasonal moving average coefficients (AR1=0.512, SMA1=0.609, P<0.01). ARIMA (1,0,0)(0,1,1)12 model, with AIC=67.01, BIC= 71.87 and white noise for predicting error, exactly fitted the incidence of the previous monthly incidence from Jan. 1998 to Dec. 2005, and the predicted monthly incidence in 2006 by the model was consistent with the actual incidence. Malaria incidence of 2007 would be 106.50/100 000, with a peak incidence during July and October. CONCLUSION: The model of ARIMA seems to be an appropriate model to fit exactly the changes of malaria incidence and to predict the future incidence trend, with a high prediction precision of short term time series. |
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| Keywords: | Time series ARIMA model Prediction Malaria Incidence |
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