Development of temporal modeling for prediction of dengue infection in Northeastern Thailand

• 作 者: Siriwan Wongkoon Mullica Jaroensutasinee Krisanadej Jaroensutasinee
• 机 构: Center of Excellence for Ecoinformatics,School of Science,Walailak University,Thasala,Nakhon Si Tham
• 出 处: Asian Pacific Journal of Tropical Medicine
• 发表日期: 2012年03期
• 关键词: dengue Thailand ARIMA validated Criterion northeastern assist warning umber iter

Objective:To model the monthly number of dengue fever cases in northeastern Thailand using time series analysis.Methods:Autoregressive Integrated Moving Average(ARIMA) models have been developed on the monthly data collected from January 1981 to December 2006 and validated using the data from January 2007 to April 2010.Results:The ARIMA(3,1,4) model has been found as the most suitalile model with the least Akaike Information Criterion(AIC) of 14.060 and Mean Absolute Percent Error(MAPE) of 7.000.The model was fiuther validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times.Conclusions: Early warning based on the data in the previous months could assist in improving vector control, community intervention,and personal protection.

Objective: To model the monthly number of dengue fever cases in northeastern Thailand using time series analysis. Methods: Autoregressive Integrated Moving Average (ARIMA) models have been developed on the monthly data collected from January 1981 to December 2006 and validated using the data from January 2007 to April 2010. Results: The ARIMA (3,1,4) model has been found as the most suitalile model with the least Akaike Information Criterion (AIC) of 14.060 and Mean Absolute Percent Error (MAPE) of 7.000.The model was was fiuther validated by the Portmanteau test with no significant autocorrelation between residuals at different lag times. Conclusions: Early warning based on the data in the previous months could assist in improving vector control, community intervention, and personal protection.