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

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.

Climatic variability and dengue virus transmission in Chiang Rai, Thailand

The objective of this study was to assess the impact of climate variation on the dengue virus transmission in Chiang Rai, Thailand. We obtained population - based information on monthly variation in monthly dengue cases and climatic factors. A time series analysis was conducted by using cross-correlation function and seasonal auto-regressive integrated moving average [SARIMA] modeling. Our findings indicate that rainfall and minimum temperature seem to have played an important role in the transmission of dengue in Chiang Rai. Such model may be used to assist public health decision - making and environmental health risk management. Early warning based on forecasts could assist in improving vector control, community intervention, and personal protection