[Application of ARIMA model on prediction of malaria incidence].

Autor: Jing X; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Hua-Xun Z; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Wen L; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Su-Jian P; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Ling-Cong S; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Xiao-Rong D; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Mu-Min C; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Dong-Ni W; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China., Shunxiang C; Institute of Schistosomiasis Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China.
Jazyk: čínština
Zdroj: Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control [Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi] 2016 Jan 29; Vol. 28 (2), pp. 135-140.
DOI: 10.16250/j.32.1374.2015207
Abstrakt: Objective: To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA).
Methods: SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation.
Results: The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable.
Conclusions: The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.
Databáze: MEDLINE
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