mapping spatial variation of disease using classic and bayesian models

abstract   disease mapping includes a set of statistical techniques that lead to provide clean maps based on estimation of the incidence, prevalence and mortality rates for the users to be able to estimate the distribution of disease reliably. the main aims of disease mapping are to: describe the spatial variation in disease incidence for the formulation of etiological hypotheses; identify areas of unusually high risk so that action may be taken; provide a clean map of disease risk in a region to allow better resource allocation and risk assessment; disease atlas construction. so far, several case studies have been conducted in which the applications of disease mapping models have been used. however, except in a few small studies and in limited situations, disease mapping models have not been compared and their general terms have not been studied. according to the importance of disease mapping and its growing use, in the present study the most important models have been introduced and their theoretical foundations, applications, advantages and disadvantages have been mentioned briefly and their various aspects have been compared