Comparison of three time series models in predicting the incidence of healthcare-associated infection / 中国感染控制杂志

Objective To compare and evaluate the effect of different time series models in predicting incidence of healthcare-associated infection (HAI), and explore the best model for predicting incidence of HAI.Methods Seasonal autoregressive integrated moving average (ARIMA) model, nonlinear autoregressive neural network (NARNN), and ARIMA-back propagation neural network (ARIMA-BPNN) combination model were constructed based on fitting dataset of monthly HAI incidence from 2011 to 2016 (72 months) in a tertiary first-class hospital in Shanghai, predicting dataset of monthly infection incidence from January to December 2017 were used to test the predictive effect of model, the predictive effect of different models was evaluated and compared.Results For the fitting dataset, mean absolute percentage error (MAPE) of ARIMA, NARNN, and ARIMA-BPNN combination model were 13.00％, 14.61％, and 11.95％respectively;and for the predicting dataset, MAPE of ARIMA, NARNN, and ARIMA-BPNN combination model were 15.42％, 26.31％, and 14.87％ respectively.Conclusion Three time series models can effectively predict the incidence of HAI, of which the ARIMA-BPNN combination model showed the best performance in fitting and predicting the occurrence of HAI in this hospital, and can provide data support for the hospital decision-making.

Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model / 中国血吸虫病防治杂志

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.