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 are to estimate the morbidity rate of Dengue Hemorrhagic fever in each province of Thailand and to construct the map of the Dengue Hemorrhagic fever. A Bayesian model is used for the data analysis. The data are mathematical spatial correlated in the form of the conditional autoregressive model (CAR). The secondary data were collected at a provincial level. They were the number of Dengue Hemorrhagic fever patients rainfall and average temperature. The research found that the rainfall (RR = 1.0026) and the average temperature (RR = 1.2681) had influence on the morbidity of Dengue Hemorrhagic fever. The season during July to September had more influence on the morbidity of Dengue Hemorrhagic fever than during the other seasons. The top 5 highest morbidity rates per 100000 population were Chiangmai in October (1288.00) July (1096.00) September (804.30) Surin in October (772.30) and Nakhon Si Thammarat in August (666.20). The disease map constructed for showing the distribution of the disease is easy to understand and useful for public health.