Model-based Geostatistics for Global Public Health
Methods and Applications
1st Edition
Published on 11. March 2019
Book
Hardback
274 pages
978-1-138-73235-3 (ISBN)
Description
Model-based Geostatistics for Global Public Health: Methods and Applications provides an introductory account of model-based geostatistics, its implementation in open-source software and its application in public health research. In the public health problems that are the focus of this book, the authors describe and explain the pattern of spatial variation in a health outcome or exposure measurement of interest. Model-based geostatistics uses explicit probability models and established principles of statistical inference to address questions of this kind.
Features:
Presents state-of-the-art methods in model-based geostatistics.
Discusses the application these methods some of the most challenging global public health problems including disease mapping, exposure mapping and environmental epidemiology.
Describes exploratory methods for analysing geostatistical data, including: diagnostic checking of residuals standard linear and generalized linear models; variogram analysis; Gaussian process models and geostatistical design issues.
Includes a range of more complex geostatistical problems where research is ongoing.
All of the results in the book are reproducible using publicly available R code and data-sets, as well as a dedicated R package.
This book has been written to be accessible not only to statisticians but also to students and researchers in the public health sciences.
The Authors
Peter Diggle is Distinguished University Professor of Statistics in the Faculty of Health and Medicine, Lancaster University. He also holds honorary positions at the Johns Hopkins University School of Public Health, Columbia University International Research Institute for Climate and Society, and Yale University School of Public Health. His research involves the development of statistical methods for analyzing spatial and longitudinal data and their applications in the biomedical and health sciences.
Dr Emanuele Giorgi is a Lecturer in Biostatistics and member of the CHICAS research group at Lancaster University, where he formerly obtained a PhD in Statistics and Epidemiology in 2015. His research interests involve the development of novel geostatistical methods for disease mapping, with a special focus on malaria and other tropical diseases. In 2018, Dr Giorgi was awarded the Royal Statistical Society Research Prize "for outstanding published contribution at the interface of statistics and epidemiology." He is also the lead developer of PrevMap, an R package where all the methodology found in this book has been implemented.
Features:
Presents state-of-the-art methods in model-based geostatistics.
Discusses the application these methods some of the most challenging global public health problems including disease mapping, exposure mapping and environmental epidemiology.
Describes exploratory methods for analysing geostatistical data, including: diagnostic checking of residuals standard linear and generalized linear models; variogram analysis; Gaussian process models and geostatistical design issues.
Includes a range of more complex geostatistical problems where research is ongoing.
All of the results in the book are reproducible using publicly available R code and data-sets, as well as a dedicated R package.
This book has been written to be accessible not only to statisticians but also to students and researchers in the public health sciences.
The Authors
Peter Diggle is Distinguished University Professor of Statistics in the Faculty of Health and Medicine, Lancaster University. He also holds honorary positions at the Johns Hopkins University School of Public Health, Columbia University International Research Institute for Climate and Society, and Yale University School of Public Health. His research involves the development of statistical methods for analyzing spatial and longitudinal data and their applications in the biomedical and health sciences.
Dr Emanuele Giorgi is a Lecturer in Biostatistics and member of the CHICAS research group at Lancaster University, where he formerly obtained a PhD in Statistics and Epidemiology in 2015. His research interests involve the development of novel geostatistical methods for disease mapping, with a special focus on malaria and other tropical diseases. In 2018, Dr Giorgi was awarded the Royal Statistical Society Research Prize "for outstanding published contribution at the interface of statistics and epidemiology." He is also the lead developer of PrevMap, an R package where all the methodology found in this book has been implemented.
Reviews / Votes
"This is an excellent source for public health professionals so far as the needed state-of-the-art concepts and methods that are needed to analyse and interpret geostatistical data. Basic knowledge of mathematical statistics is necessary to read through this well-written book... Focus has been made on disease mapping, environmental epidemiology, generalized linear models, variogram, and R-codes. The references are thorough and up-to-date. The examples are real-life oriented and interesting... Some unique features of this well-written book are the illustrations and they include river blindness in Liberia, heavy metal monitoring in Galicia, malnutrition in Ghana, rolling malaria in Malawi, ozone concentration in Eastern United States, prevalence and intensity of infection among others.This book is quite suitable to be a textbook for a graduate level course in global public health or geo-statistics. Researchers and doctoral graduate students seeking thesis topic ought to read this book. I enjoyed reading this book. I recommend this book to statistics and computing professionals."- Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, April 2020
"This book was written primarily to introduce geostatistics to public health researchers...The text goes beyond introductory descriptions and provides a fairly comprehensive guide to geostatistics, ranging from the design of geostatistical experiments to the analysis of complicated datasets. While the book's target audience is mainly public health researchers, the material is also helpful to PhD students and even statistics faculty that want an introduction to geostatistics. Each chapter can be read as an independent guide or read jointly to gain a more complete understanding of geostatistical research from data collection to analysis... The text is well-written and genuinely enjoyable to read. One of the main attractions of the book is that the authors offer tidbits of advice from their own expert experience analyzing geostatistical data...While other texts can lose the readers in the seemingly endless modeling choices, Diggle and Giorgi guide their audience to make informed decisions from the first design stages to the final visualizations."
- Ian Laga and Xiaoyue Niu, JASA 2020
"The book provides an integrated mix of statistical theory and applications, working up from linear regression through to generalised geostatistical models and on to specialised topics, such as zero-inflation in geostatistical models, spatiotemporal models and approaches to combining data from multiple sources...The relevant case studies developed throughout the course of the book provide an excellent demonstration of the methods and potential insight available from using geostatistical approaches. Furthermore, the emphasis on the communication of model results is a beneficial addition for any statistician working in a collaborative environment. Model-based Geostatistics for Global Public Health provides a good grounding in geostatistical modelling with excellent worked case studies in the global public health domain. It offers particular value to applied statisticians with its technical detail and thorough case studies. The book is supported by an open-source R package, PrevMap."
- Kirsty L. Hassall, Rothamsted Research, Harpenden, UK "This is an excellent source for public health professionals so far as the needed state-of-the-art concepts and methods that are needed to analyse and interpret geostatistical data. Basic knowledge of mathematical statistics is necessary to read through this well-written book... Focus has been made on disease mapping, environmental epidemiology, generalized linear models, variogram, and R-codes. The references are thorough and up-to-date. The examples are real-life oriented and interesting... Some unique features of this well-written book are the illustrations and they include river blindness in Liberia, heavy metal monitoring in Galicia, malnutrition in Ghana, rolling malaria in Malawi, ozone concentration in Eastern United States, prevalence and intensity of infection among others.This book is quite suitable to be a textbook for a graduate level course in global public health or geo-statistics. Researchers and doctoral graduate students seeking thesis topic ought to read this book. I enjoyed reading this book. I recommend this book to statistics and computing professionals."
- Ramalingam Shanmugam, in the Journal of Statistical Computation and Simulation, April 2020
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Dimensions
Height: 234 mm
Width: 156 mm
Weight
608 gr
ISBN-13
978-1-138-73235-3 (9781138732353)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Peter J. Diggle | Emanuele Giorgi
Model-based Geostatistics for Global Public Health
Methods and Applications
Book
06/2021
1st Edition
Chapman & Hall/CRC
€94.90
Shipment within 10-20 days
Peter J. Diggle | Emanuele Giorgi
Model-based Geostatistics for Global Public Health
Methods and Applications
E-Book
03/2019
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Peter J. Diggle | Emanuele Giorgi
Model-based Geostatistics for Global Public Health
Methods and Applications
E-Book
03/2019
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Persons
Peter Diggle is Distinguished University Professor of Statistics in the Faculty of Health and Medicine, Lancaster University. He also holds honorary positions at the Johns Hopkins University School of Public Health, Columbia University International Research Institute for Climate and Society, and Yale University School of Public Health. His research involves the development of statistical methods for analyzing spatial and longitudinal data and their applications in the biomedical and health sciences.
Dr Emanuele Giorgi is a Lecturer in Biostatistics and member of the CHICAS research group at Lancaster University, where he formerly obtained a PhD in Statistics and Epidemiology in 2015. His research interests involve the development of novel geostatistical methods for disease mapping, with a special focus on malaria and other tropical diseases. In 2018, Dr Giorgi was awarded the Royal Statistical Society Research Prize "for outstanding published contribution at the interface of statistics and epidemiology." He is also the lead developer of PrevMap, an R package where all the methodology found in this book has been implemented.
Dr Emanuele Giorgi is a Lecturer in Biostatistics and member of the CHICAS research group at Lancaster University, where he formerly obtained a PhD in Statistics and Epidemiology in 2015. His research interests involve the development of novel geostatistical methods for disease mapping, with a special focus on malaria and other tropical diseases. In 2018, Dr Giorgi was awarded the Royal Statistical Society Research Prize "for outstanding published contribution at the interface of statistics and epidemiology." He is also the lead developer of PrevMap, an R package where all the methodology found in this book has been implemented.
Content
Introduction. Regression modelling for spatially referenced data. Theory. Linear models. Generalized linear models. Geostatistical design. Preferential sampling. Zero-inflation. More complex problems. Appendix.