Model-based Geostatistics for Global Public Health

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.

• Introduction. Regression modelling for spatially referenced data. Theory. Linear models. Generalized linear models. Geostatistical design. Preferential sampling. Zero-inflation. More complex problems. Appendix.

"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