Spatio-Temporal Methods in Environmental Epidemiology with R, Second Edition 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 MED, 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.
New to this edition:
- Includes a new chapter on data science
- Updated material on measurement error, deterministic modeling, infectious diseases, and preferential sampling
- Introduces modern computational methods, including INLA, together with code for implementation
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.
About the Author: Professor Gavin Shaddick is the Executive Dean of the School of Engineering, Mathematical and Physical Sciences and a Professor of Data Science and Statistics at Royal Holloway, University of London and a Turing Fellow at The Alan Turing Institute. His research interests lie at the interface of statistics, AI, epidemiology and environmental science. He is a member of the UK government's Committee on the Medical Effects of Air Pollutants (COMEAP) and the sub-group on Quantification of Air Pollution Risk (QUARK). He leads the World Health Organization's Data Integration Taskforce for Global Air Quality and led the development of the Data Integration Model for Air Quality (DIMAQ) that is used to calculate a number of air pollution related to United Nations Sustainable Development Goals indicators.
Professor James V. Zidek is Professor Emeritus at the University of British Columbia. He received his M.Sc. and Ph.D. from the University of Alberta and Stanford University, both in Statistics. His research interests include the foundations of environmetrics, notably on the design of environmental monitoring networks, and spatio-temporal modelling of environmental processes. His contributions to statistics have been recognized by a number of awards including Fellowships of the ASA and IMI, the Gold Medal of the Statistical Society of Canada and Fellowship in the Royal Society of Canada, one of that country's highest honors for a scientist.
Professor Alex Schmidt has joined the Shaddick-Zidek team of co-authors. She is Professor of Biostatistics at McGill University. She is distinguished for her work in the theory of
spatio-temporal modelling and more recently for that in biostatistics as well as epidemiology. In recognition of that work, she received awards from The International Environmetrics Society (TIES) and the American Statistical Association's Section on Statistics and the Environment (ENVR-ASA). She was the 2015 President of the International Society for Bayesian Analysis and Chair of the Local Organizing Committee for the 2022 ISBA meeting. Her current topics of research include non-normal models for spatio-temporal processes and the analysis of joint epidemics of dengue, Zika and chikungunya in Latin America.
