Forecasting influenza incidence by SARIMA model in Nong Khai Province
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Abstract
The aims of this study were to determine the suitable model for forecasting the influenza incidence in Nong Khai province by using the monthly influenza data from 2016 to 2019. The Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to determine the efficient of the suitable model. The results were showed that the best model for forecasting influenza incidence was SARIMA (0,1,0)(2,1,0)12, providing the lowest AIC (102.37), BIC (107.04) with the RMSE value of 0.65 and MAPE value of 8.30%. It was indicated that SARIMA (0,1,0)(2,1,0)12 could be used to predict the influenza incidence in the future. The forecast information could be used to plan the influenza prevention and control, including the surveillance system for the area.
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