V. Gómez-Rubio, J. Ferrándiz and A. López
Detecting Clusters of Diseases with R
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One of the main concerns in Public Health surveillance is detection of clusters of
diseases, i. e., the presence of high incidence rates around a particular location,
which usually means a higher risk of suffering from the disease of study.

Many methods have been proposed for cluster detection, ranging from visual
inspection of disease maps to full Bayesian models estimated by using M.C.M.C.

In this paper we describe the use and implementation, as a package for R, of
several methods which have been widely used in the literature, such as
Openshaw's G.A.M., Stone's test and others.

Although some of the statistics involved in these methods have an asymptotic
distribution, bootstrap will be used to estimate their actual sampling distributions.