Modelling count data with overdispersion and spatial effects

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A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, background): “More recently, Fahrmeir and Osuna Echavarrı́a (2006) and Gschlößl and Gzado (2008) have introduced zero-inflated models for spatially correlated data by specifying region-specific random effects to account for geographical dependencies.”
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “Popular models include the BYM model for disease-mapping with extensions for regional data (Besag et al., 1991; Held et al., 2005; Weir and Pettitt, 2000; Gschlößl and Czado, 2007; Wakefield, 2007), continuous-indexed Gaussian models (Banerjee et al.”
Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates (2007, unknown): “…--- 2007/8/7 --- page 126 --- #2 large amount of zeros (for score tests, see Famoye and Singh, 2003; Gupta et al., 2004; Bae et al., 2005; Famoye and Singh, 2006; for mixing property, see Joe and Zhu, 2005; and for a Bayesian approach, see Gschlößl and Czado, 2006).”