Malaria pandemic (MP) has been linked to a range of serious health problems including premature mortality. The main objective of this research is to quantify uncertainties about impacts of malaria on mortality. A multivariate spatial regression model was developed for estimation of the risk of mortality associated with malaria across Ogun State in Nigeria, West Africa. We characterize different local governments in the data and model the spatial structure of the mortality data in infants and pregnant women. A flexible Bayesian hierarchical model was considered for a space-time series of counts (mortality) by constructing a likelihood-based version of a generalized Poisson regression model that combines methods for point-level misaligned data and change of support regression. A simple two-stage procedure for producing maps of predicted risk is described. Logistic regression modeling was used to determine an approximate risk on a larger scale, and geo-statistical ("Kriging") approaches were used to improve prediction at a local level. The results suggest improvement of risk prediction brought about in the second stage. The advantages and shortcomings of this approach highlight the need for further development of a better analytical methodology.