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Spatio-Temporal Methods in Environmental Epidemiology (Hardcover)
Loot Price: R2,836
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Spatio-Temporal Methods in Environmental Epidemiology (Hardcover)
Series: Chapman & Hall/CRC Texts in Statistical Science
Expected to ship within 12 - 17 working days
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Teaches Students How to Perform Spatio-Temporal Analyses within
Epidemiological StudiesSpatio-Temporal Methods in Environmental
Epidemiology is the first book of its kind to specifically address
the interface between environmental epidemiology and
spatio-temporal modeling. In response to the growing need for
collaboration between statisticians and environmental
epidemiologists, the book links recent developments in
spatio-temporal methodology with epidemiological applications.
Drawing on real-life problems, it provides the necessary tools to
exploit advances in methodology when assessing the health risks
associated with environmental hazards. The book's clear guidelines
enable the implementation of the methodology and estimation of
risks in practice. Designed for graduate students in both
epidemiology and statistics, the text covers a wide range of
topics, from an introduction to epidemiological principles and the
foundations of spatio-temporal modeling to new research directions.
It describes traditional and Bayesian approaches and presents the
theory of spatial, temporal, and spatio-temporal modeling in the
context of its application to environmental epidemiology. The text
includes practical examples together with embedded R code, details
of specific R packages, and the use of other software, such as
WinBUGS/OpenBUGS and integrated nested Laplace approximations
(INLA). A supplementary website provides additional code, data,
examples, exercises, lab projects, and more. Representing a major
new direction in environmental epidemiology, this book-in full
color throughout-underscores the increasing need to consider
dependencies in both space and time when modeling epidemiological
data. Students will learn how to identify and model patterns in
spatio-temporal data as well as exploit dependencies over space and
time to reduce bias and inefficiency.
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