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| Prediction of the incidence of hepatitis A with optimal linear composite prediction model |
| Lu Miaomiao1, Zhang Xingyu2 |
1 Office of the President, Shanghai Business School, Shanghai 201400, China;
2 West China School ofPublic Health Sichuan University |
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Abstract Objective To explore the application value of optimal linear composite prediction model in forecasting the incidence number of hepatitis A (HA). Methods Seasonal autoregressive integrated moving average (SARIMA) model and radial basis function neural network (RBFNN) were developed based on the monthly incidence of HA in mainland China from 2005 to 2010. Linear regression model between the true incidence and the simulated values of SARIMA and RBFNN were also developed. The incidence values of 2011 were predicted with the three models and their forecasting efficiency was compared. Results The mean average error (MAE), mean average percentage error (MAPE) and root mean square error (RMSE) of predicted values by SARIMA model were 413.667, 0.154, 0.392 respectively. The MAE, MAPE, RMSE of predicted values by RBFNN were 291.833,0.118,0.344 respectively. The MAE, MAPE, RMSE of predicted values by optimal linear composite prediction model were 202.333,0.082,0.286 respectively. So the MAE, MAPE, RMSE of predicted values by optimal linear composite prediction model were all lower than those individual models. Conclusion The result indicates that the optimal linear composite prediction model can be well applied to forecast the incidence of HA.
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Received: 25 May 2015
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