When survival times are spatially referenced, some evidence of clustering of high or low times might be apparent on a visual inspection of the data. The question naturally arises as to whether these observed spatial survival patterns can be explained by incorporating appropriate covariates into the model or whether to obtain reliable inferences for model parameters of interest, it is necessary to explicitly model the unexplained spatial variation. In this talk, we consider different random effects regression models for spatially correlated survival data. In these models, the large-scale variations are characterized by a linear function of explanatory variables and small-scale variations are characterized by spatial processes. We compare these different approaches in the context of a dataset on COVID-19 mortality data. The main objective is to explain the pattern of COVID-19 mortality using important covariates while accounting for possible (spatially correlated) differences in hazard among the districts.