Model-based Geostatistics for Global Public Health - Diggle, Peter J. (Lancaster University, UK); Giorgi, Emanuele (Lancaster University)
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State-of-the-art methods in model-based geostatistics (MBG) and its application to problems in global public health. Scientific objective is to describe the pattern of spatial variation in a health outcome using explicit probability models and established principles of statistical inference.

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Produktbeschreibung
State-of-the-art methods in model-based geostatistics (MBG) and its application to problems in global public health. Scientific objective is to describe the pattern of spatial variation in a health outcome using explicit probability models and established principles of statistical inference.
Autorenporträt
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.