In disease mapping, we estimate the relative risk of a disease across different areas within a region of interest. The number of cases in an area is often modelled through a Poisson distribution with mean given by the product between an offset and the logarithm of the relative risk of the disease. The Besag, York and Mollié model, commonly used to account for potential overdispersion and a spatial correlation structure among the counts, does not accommodate outliers. We define outliers in two ways: areas with extreme risks and areas with different latent behaviours compared to the region of interest. We build on the Bayesian hierarchical model proposed by Riebler et al. (2016) and assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We compare our approach with that proposed by Congdon (2017), in an analysis of cases of Zika during the 2015-2016 epidemic in Rio de Janeiro.