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 investigate risk factors for Dengue fever in Thailand and to select an appropriate model. The generalized estimating equation (GEE) whose dependent variable has a Poisson distribution and a negative binomial distribution were studied. The secondary data consisting of the number of Dengue fever patients rainfall average temperature forest area household income were collected. Regions and seasons were also considered. The research found that the GEE whose dependent variable has a negative binomial is more appropriate to be used for Dengue patient data analysis. Central region and the season during Feb-Apr were assigned to be reference groups. Risk factors for Dengue fever were rainfall (Relative Risk (RR) = 1.0009) average temperature (RR = 1.1736) forest area (RR = 1.0482) southern region (RR = 2.7390) eastern region (RR = 2.0489) western region (RR = 1.4051) the season during May-Jul (RR = 3.0526) and Aug-Oct (RR = 3.2677)