| Abstract : | The mapping of disease incidence and prevalence has long been a part ofpublic health, epidemiology and the study of disease in human populations.The disease maps are visual representations of intricate geographic data andthey can provide a quick overview of the disease information. They are quiteuseful in the field of Spatial Epidemiology which focuses on the descriptionand examination of disease and its geographic variations. In order to representand visualize our results in disease mapping, we have to make use ofhierarchical models which can either be spatial or non spatial. In the termsof spatial models we mean that we take into account the spatial correlationbetween the areas while in the non spatial models we consider that there isno structured spatial correlation between them. We will investigate methodsthat can be used to analyse both univariate and multivariate disease maps.Furthermore we are going to compare these models and discuss the resultsof the relative risks between the areas in order to highlight those areas withelevated or lowered risk.
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