Statistical Methods in Spatial Epidemiology
Spatial Epidemiology is a rapidly growing field of research concerned with the analysis of the geographical distribution of disease. This principally involves mapping the location of disease cases and the analysis of the mapped data using spatial statistical methods. The growth of the field looks set to continue in line with increasing public, government and media concern about environmental and health issues.
* Comprehensive overview of the main statistical methods used in spatial epidemiology
* Contains many data examples - each represents a different approach to the analysis, and provides an insight into the various modelling techniques
* Describes modern simulation-based methods suitable for highly complex modelling problems
* Discusses the wide range of software available for analysing spatial data
* Contains an extensive bibliography
The first part of the book provides all the necessary definitions and terminology, introduces some data examples, and considers map construction along with some basic models. The second part covers important problems in spatial epidemiology, with detailed coverage of disease mapping, ecological analysis, disease clustering and infectious disease modelling.
Primarily aimed at medical statisticians, epidemiologists, environmental statisticians, and researchers in public health, this text will also appeal to postgraduate students of statistics or epidemiology.
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Scales of Measurement and Data Availability
Geographical Representation and Mapping
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aggregation algorithm analysis applied approach approximation assess assumed assumption at-risk background Bayesian models Besag census tracts components computed consider control disease correlated heterogeneity count data covariates Cressie defined density estimation design matrix Diggle disease incidence disease mapping displays distance ecological edge effects event data examined example expected count exploratory Falkirk Figure frequentist function geostatistical guard area Hence hypothesis infection inference intensity interpolation Lawson and Williams likelihood models locations MCMC methods non-parametric null hypothesis observed parameters point process Poisson distribution Poisson process pollution source population possible posterior distribution prior distribution problems putative source random effects random-effect models ratio realisation regression relative risk represent residual respiratory cancer S-Plus sampling scale Section simulation smoothing space-time space-time clustering spatial correlation spatial distribution spatial epidemiology spatially dependent spatio-temporal specific standardised statistical Stochastic structure study region study window surface temporal tract counts usually variables variation