BSTZINB: A Bayesian Framework for Negative-Binomial Modeling of Spatio-Temporal Zero-Inflated Count Data in Epidemiology
Modern Bayesian hierarchical methodologies allow us to leverage spatio-temporal dependencies between observations, enhancing both health effect estimation and map visualization in efficient and flexible ways. However, the necessary levels of statistical software are often unavailable or difficult to access. We have rec...