Application of ARIMA model to predict number of malaria cases in China
10.16250/j.32.1374.2017088
- VernacularTitle:我国疟疾报告病例数自回归移动平均模型预测研究
- Author:
Huiyu HOU
;
Huaqin SONG
;
Shunxian ZHANG
;
Lin AI
;
Yan LU
;
Yuchun CAI
;
Shizhu LI
;
Xuejiao TENG
;
Chunli YANG
;
Wei HU
;
Jiaxu CHEN
- Keywords:
Malaria;
Autoregressive integrated moving average(ARIMA)model;
Prediction
- From:
Chinese Journal of Schistosomiasis Control
2017;29(4):436-440,458
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the application of autoregressive integrated moving average(ARIMA)model to predict the monthly reported malaria cases in China,so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 2006-2015 and 2011-2015,respectively. The data of malaria cases from January to December,2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA(2,1,1)(1,1,0)12 and ARIMA(1,0,0)(1,1,0)12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher ac-curacy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process,which needs to be adjusted unceasingly according to the accumulated data,and in addi-tion,the major changes of epidemic characteristics of infectious diseases must be considered.
