Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans
作者： Lingling Zhou , Jing Xia , Lijing Yu , Ying Wang , Yun Shi
关键词: Autoregressive integrated moving average 、 Statistics 、 Schistosomiasis 、 Artificial neural network 、 Reliability (statistics) 、 Hybrid model 、 Mean absolute percentage error 、 Mean squared error 、 Autoregressive model 、 Mathematics
摘要: Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and nonlinear neural network (NARNN) models in forecasting schistosomiasis. Our purpose current study was to forecast annual prevalence of human schistosomiasis Yangxin County, using our ARIMA-NARNN model, thereby further certifying reliability model. Methods: used ARIMA, NARNN fit The modeling time range included from 1956 2008 while testing 2009 2012. mean square error (MSE), absolute (MAE) percentage (MAPE) were measure performance. reconstructed 2013 2016. Results: errors generated by lower than those obtained either single ARIMA or models. predicted 2016 demonstrated an initial decreasing trend, followed increase. Conclusions: can be well applied analyze surveillance data for early warning systems control elimination
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