Unusual clusters of disease must be detected rapidly for effective public health interventions
to be introduced. Over the past decade there has been a surge in interest in statistical …

Spatio-temporal point process data have been analysed quite a bit in specialised fields, with
the aim of better understanding the inherent mechanisms that govern the temporal evolution …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Conventional geostatistical methodology solves the problem of predicting the realized value
of a linear functional of a Gaussian spatial stochastic process S (x) based on observations …

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 …

In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models
for spatial and spatio-temporal point process data. We discuss inference, with a particular …

Modern conflicts are characterized by an ever increasing use of information and sensing
technology, resulting in vast amounts of high resolution data. Modelling and prediction of …

A review of some methods for analysis of space–time disease surveillance data is
presented. Increasingly, surveillance systems are capturing spatial and temporal data on …

We investigate two options for performing Bayesian inference on spatial log-Gaussian Cox
processes assuming a spatially continuous latent field: Markov chain Monte Carlo (MCMC) …

Objectives Gangs are thought to enhance participation in violence. It is expected then that
gang-related violent crimes trigger additional crimes in a contagious manner, above and …