The objectives of this research are to propose a model for risk assessment of Measles and Rubella morbidity in Thailand to investigate factors influencing on the Measles and Rubella morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects follow a multivariate conditional autoregressive model (MCAR) are adopted. The dependent variables are the number of Measles and Rubella disease patients in each province. The factors considered are average temperature the amount of rainfall and regions. The results show that the temperature rainfall and regions influence on the Measles and Rubella morbidity rates. Measles and Rubella maps are easy for readers to see which areas are at high risk and to compare the morbidity rates among those areas by looking at those different colors.

The objectives of this research were to propose a spatial analysis using Bayesian generalized linear mixed model for predicting drought in Thailand. The spatial effect was assumed to be a conditional autoregressive model (CAR model). The dependent variables was a drought or non-drought occurrence which was assumed to have a Bernolli distribution with logistic link function. Factors considered were rain temperature number of rainy days number of large reservoirs and number of medium reservoirs. The results show that the rain temperature number of rainy days number of large reservoirs and number of medium reservoirs influence on the drought occurrence. This study is useful for planning making decision preventing and remedying the drought problem.

The objectives of this research are to propose a linear mixed model (LMM) with spatial correlation for an analysis of rice yields in Thailand to estimate the rice yield in each month of all provinces in Thailand to investigate factors influencing on the rice yields and to construct the maps of rice yields. A linear mixed model (LMM) in which the spatial effects follow the conditional autoregressive model (CAR) is used. The estimated rice yields are used to construct the rice yield maps. The dependent variables are the rice yield in each month of each province. The data are secondary data at a provincial level. The factors considered are rainfall averaged temperatures and regions. The results show that the factors influencing on the rice yields are rainfall averaged temperature and region. The amount of regional effects ordering from largest to smallest values are northestern region southern region western region central region eastern region and northern region respectively. The top ten provinces and months with high yields ranking from largest to smallest values are Ubon Ratchathani in November (1328000 ton) Surin in Novermber (1070000 ton) Si Sa Ket in Novermber (1023000 ton) Buri Ram in November (939400 ton) Nakhon Ratchasima in November (907500 ton) Roi Et in Novermber (852800 ton) Chiang Rai in Novermber (769800 ton) Khon Kaen in Novermber (692800 ton) Maha Sarakham in Novermber (676200 ton) Udon Thani in Novermber (620600 ton) respectively. The rice yeilds maps are easy for readers to identify which areas have high or low yields. They are a useful tool for planning decision making and implementing plans to help rice famers to increase their rice production.

The objectives of this research are to propose a linear mixed model (LMM) for spatial time series with a seasonal component to apply the proposed model to monthly rubber yields in southern provinces of Thailand to forecast rubber yields in southern provinces of Thailand and to compare the performance of the proposed model to the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model is a linear mixed model (LMM) with spatial effects following a conditional autoregressive model (CAR model). Seasonal dummy variables and Fourier terms are two methods used to account for the seasonal effects. A Bayesian method is used for parameter estimation. The estimated monthly yields are used to forecast the monthly rubber yields. The dependent variables are the monthly rubber yields in each province. The effects considered are spatial effects heterogeneity effects and seasonal effects. The data are secondary data at a provincial level. The results show that the effects influencing on the amount of rubber yields are spatial heterogeneity and seasonal effects. The proposed model with sesonal dummy variables is the most appropriate model copared to the LMM with Fourier sesonal effects Holt-Withers ES and SARIMA. The mean absolute errors (MAE) are smallest in both model fitting and model validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.

The objectives of this research are to estimate the morbidity rates of Leptospirosis in each month of all provinces in Thailand to determine the trend of leptospirosis over time to investigate factors influencing on the morbidity rates and to construct the disease maps of the leptospirosis. The generalized linear mixed model (GLMM) in which the spatial effects follow the conditional autoregressive model (CAR) and the temporal effects follow a linear trend. The estimated morbidity rates are used to construct the disease maps. The dependent variables are the numbers of leptospirosis patients in each month of each province and are assumed to have a Poisson distribution. The data are secondary data at a provincial level. The factors considered are rainfall averaged temperatures and regions. The results show that the factors influencing on the morbidity rates are rainfall (Relative Risk (RR) = 1.0013) averaged temperature (RR = 0.9922) north region (RR = 10.6440) northeast region (RR = 14.3249) southern region (RR = 15.5491) western region (RR = 3.6219) and eastern region (RR = 1.0103) where the central region is a reference region and a linear trend (RR = 1.0183). The top ten province and months with high morbidity rates (per 1000000 population) ranking from largest to smallest values are Phangnga in September (59.00) Phangnga in August (33.49) Sisaket in October (23.14) Sisaket in August ศรีสะเกษ (22.60) Sisaket in September (22.57) Phngnga in October (22.55) Loei in September (21.11) Phangnga in July (20.81) Phangnga in May (20.72) and Phangnga in June (20.47) respectively. The leptospirosis maps are easy for readers to identify which areas are at high risk. They are a useful tool for planning and controlling the leptospirosis.