Many statistical models have been proposed to analyse small area disease data with the
aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian …

With the advent of routine health data indexed at a fine geographical resolution, small area
disease mapping studies have become an established technique in geographical …

There has been substantial progress in the development of Bayesian spatial modelling and
estimation in recent years to overcome the problem of the sparseness of data across small …

In spatial epidemiology, detecting areas with high ratio of disease is important as it may lead
to identifying risk factors associated with disease. This in turn may lead to further …

Statistical methodology for the analysis of small area health data has seen considerable
advances over the last 15 years. This development has been marked by the publication of a …

Disease mapping is the field of spatial epidemiology interested in estimating the spatial
pattern in disease risk across areal units. One aim is to identify units exhibiting elevated …

Disease mapping tackles the problem of providing a description of the geographical
variation of disease by means of estimating disease risk in small areas. Given the number of …

Detection of clustering and estimation of incidence risks from disease data are important in
public health and epidemiological research. The popular models for disease risks such as …

Several factors which affect road safety have a spatial dimension. Therefore roadway
accidents have been studied in different spatial units from point events to area levels, such …

Disease mapping aims to estimate the spatial pattern in disease risk across an area,
identifying units which have elevated disease risk. Existing methods use Bayesian …