Advances in Geographical Information Systems (GIS) and related software have led to a burgeoning of spatial-temporal databases. Data analysts today routinely encounter situations where they seek to model relationships among variables across space and time. In the very pertinent context of viral pandemics, such as COVID-19, questions arise regarding how spatial information can be effectively used in managing such outbreaks. Statistical models can be devised to answer various questions at different stages: from incorporating spatial information in epidemic models to estimating hospitalization rates in different counties across a state. Hierarchical modeling and Bayesian inference have emerged as extremely viable for such analysis allowing us to embed underlying complex processes within a hierarchical setup. However, they can become impracticable when confronted with the computational challenges of massive spatial databases. I will attempt to address all of these issues to arrive at a cohesive data analytic framework for complex spatial-temporal data.