A two-stage model for incidence and prevalence in point-level spatial count data

We consider the problem of modeling point-level spatial count data with a large number of zeros. We develop a model that is compatible with scientific assumptions about the underlying data generating process. We utilize a two-stage spatial generalized linear mixed model framework for the counts, modeling incidence, resulting in 0-1 outcomes, and prevalence, resulting in positive counts, as separate but dependent processes, and utilize a Gaussian process model for characterizing the underlying spatial dependence. We describe a Bayesian approach, and study