Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …

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 …

In recent years, disease mapping studies have become a routine application within
geographical epidemiology and are typically analysed within a Bayesian hierarchical model …

There is currently much interest in conducting spatial analyses of health outcomes at the
small-area scale. This requires sophisticated statistical techniques, usually involving …

On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of
Bayesian disease mapping and look into its future. I focus on some key developments of …

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from
the basics of disease mapping to the most advanced topics in this field. A multidimensional …

Background The use of Bayesian hierarchical spatial models has become widespread in
disease mapping and ecologic studies of health–environment associations. In this type of …

Disease mapping is the area of epidemiology that estimates the spatial pattern in disease
risk over an extended geographical region, so that areas with elevated risk levels can be …

14.1 Background The mapping of disease incidence and prevalence has long been a part of
public health, epidemiology, and the study of disease in human populations (Koch, 2005). In …

Disease mapping involves the analysis of geo-referenced disease incidence data and has
many applications, for example within resource allocation, cluster alarm analysis, and …