Modern epidemiological studies involve understanding individual and social level inferences and their role in the transmission and distribution of disease instances. The geographic relevance in epidemiology has been analysed in concurrence with these inferences. The substantial amount of data involved in an epidemiological study is usually very large and intuitively involves missing values and uncertainty. Rough Set Theory (RST) has been used in medical informatics for ‘outcome prediction' and ‘feature selection'. It can be used to construct the decision system involving spatial, medical and demographic data effectively. This chapter proposes the use of rough sets in conjunction with parallel techniques like Fuzzy sets, Intuitionistic systems and Granular (Neighborhood Approximation) computing for the classic problem of data representation, dimensionality reduction, generation and harvest of minimal rules. RST handles missing values and uncertainty more specific to spatial and medical features of data.