This handbook explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. The book explores the use of GIS and spatial statistics as tools for the analysis of spatial epidemiological data. It covers methodological advances as well as applications in human and veterinary epidemiology. A supplementary website provides color figures, program code, and datasets.
This handbook explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. The book explores the use of GIS and spatial statistics as tools for the analysis of spatial epidemiological data. It covers methodological advances as well as applications in human and veterinary epidemiology. A supplementary website provides color figures, program code, and datasets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Andrew B. Lawson is a professor of biostatistics in the Division of Biostatistics, Department of Public Health Sciences, College of Medicine at the Medical University of South Carolina (MUSC). He is an MUSC eminent scholar and American Statistical Association (ASA) fellow. He is also an advisor in disease mapping and risk assessment for the World Health Organization, the founding editor of the journal Spatial and Spatio-Temporal Epidemiology, and the author of eight books, including the highly regarded Chapman & Hall/CRC book Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. He has published more than 150 journal articles on spatial epidemiology, spatial statistics, and related areas. He earned a PhD in spatial statistics from the University of St. Andrews. Sudipto Banerjee is a professor and chair in the Department of Biostatistics at the University of California, Los Angeles. He is an elected fellow of the ASA, the Institute of Mathematical Statistics, and the International Statistical Institute. He is also a recipient of the Mortimer Spiegelman Award from the American Public Health Association. He is the author/coauthor of more than 100 peer-reviewed publications and two highly regarded Chapman & Hall/CRC books: Hierarchical Modeling and Analysis for Spatial Data, Second Edition and Linear Algebra and Matrix Analysis for Statistics. His research interests include hierarchical modeling and Bayesian inference for spatially referenced data. Robert Haining retired as a professor of human geography from the University of Cambridge in September 2015. He is the author/coauthor of more than 150 articles and two books. His research focuses on the quantitative analysis of geographical data, including the geography of health, spatial representation, spatial sampling, exploratory data analysis, small-area estimation and hypothesis testing, spatial data analysis, and spatial econometrics. His past work has involved the evaluation of the impact of air pollution on health status using small-area statistics as well as the development of new methods for evaluating the effectiveness of small-area targeted police interventions. María Dolores Ugarte is a professor of statistics at the Public University of Navarre. She is the author/coauthor of many papers on statistics and epidemiology and several books, including the recent Chapman & Hall/CRC book Probability and Statistics with R, Second Edition. She is also an associate editor for Statistical Modelling, TEST, and Computational Statistics and Data Analysis as well as an editorial panel member of Spatial and Spatio-Temporal Epidemiology. Her research focuses on spatiotemporal disease mapping and small-area estimation with applications in several fields. She earned a PhD in statistics from the Public University of Navarre.
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