Spatio-temporal data modeling with applications to weather and disease

Meteorological and epidemiological data are oftentimes collected over many years at various locations. In such cases, it is beneficial to use spatio-temporal modeling to account for trends and the correlation of nearby observations. This thesis explores applications to spatio-temporal modeling. First, a method is developed to model the marginal distribution of spatial extreme values at a large scale quickly while allowing flexibility by introducing a fused penalty for parameter regularization. Next, various models are considered and evaluated to compare county-level HIV prediction over the US to determine if spatial models are advantageous when an abundance of covariates are available that capture the data variability. Lastly, a generalized additive model with spatial and temporal covariates is utilized to evaluate the impact of adulticide spraying on gravid Culex mosquitoes in the North Shore Mosquito Abatement District of Illinois.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste