Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model / 中国血吸虫病防治杂志
Chinese Journal of Schistosomiasis Control
;
(6): 630-634, 2016.
Artigo
em Chinês
| WPRIM
| ID: wpr-506528
ABSTRACT
Objective To explore the effect of the autoregressive integrated moving average model?nonlinear auto?regressive neural network(ARIMA?NARNN)model on predicting schistosomiasis infection rates of population. Methods The ARIMA model,NARNN model and ARIMA?NARNN model were established based on monthly schistosomiasis infection rates from Janu?ary 2005 to February 2015 in Jiangsu Province,China. The fitting and prediction performances of the three models were com?pared. Results Compared to the ARIMA model and NARNN model,the mean square error(MSE),mean absolute error (MAE)and mean absolute percentage error(MAPE)of the ARIMA?NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4,respectively. Conclusion The ARIMA?NARNN model could effectively fit and predict schistosomiasis in?fection rates of population,which might have a great application value for the prevention and control of schistosomiasis.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Tipo de estudo:
Estudo prognóstico
Idioma:
Chinês
Revista:
Chinese Journal of Schistosomiasis Control
Ano de publicação:
2016
Tipo de documento:
Artigo
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