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 suitable model with the least Akaike Information Criterion (AIC) of 14.060 and Mean Absolute Percent Error (MAPE) of 7.000. The model was further 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 suitable model with the least Akaike Information Criterion (AIC) of 14.060 and Mean Absolute Percent Error (MAPE) of 7.000. The model was further 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.
Authors: Michael A Johansson; Nicholas G Reich; Aditi Hota; John S Brownstein; Mauricio Santillana Journal: Sci Rep Date: 2016-09-26 Impact factor: 4.379