Risk assessment for measles and rubella in Thailand using multivariate poisson generalized linear mixed models

Abstract

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.