Dengue disease mapping in Malaysia based on stochastic SIR models in human populations

Relative risk estimation is one of the most important issues in the study of geographical distributions of disease occurrence or disease mapping. For the case of dengue, there are only a few studies that use statistical methods to estimate the relative risk for disease mapping. Therefore this research will introduce an alternative method to estimate the relative risk of dengue occurrence based initially on discrete-time, discrete-space stochastic SIR models (Susceptible-Infective-Removed) in human populations for dengue disease transmission, to overcome the drawbacks of relative risk estimation in disease mapping using a classical method based on standardized morbidity ratio (SMR), and the earliest example of Bayesian mapping which involves a Poisson-Gamma model. The estimation of relative risk is applied to dengue data in Malaysia which will then be displayed in a map to represent the high and low risk areas of dengue occurrence.