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Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) de Paula Moraga

Descripción - Reseña del editor Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulating and transforming point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches, Creating interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modelling, and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. Biografía del autor Paula Moraga is a Lecturer in the Department of Mathematical Sciences at the University of Bath. She received her Master’s in Biostatistics from Harvard University and her Ph.D. in Statistics from the University of Valencia. Dr. Moraga develops innovative statistical methods and open-source software for disease surveillance including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.

Geospatial health data modeling and visualization with r geospatial health data modeling and visualization with rinla and shiny paula moraga authorcreator moraga, paula author publication boca raton crc press, 2020 series chapman amp hallcrc biostatistics series chapman amp hallcrc biostatistics series formatdescription book 1 online resource xix, 274 pages subjects medical mapping Geospatial health data modeling and visualization with r modeling and visualization with rinla and shiny geospatial health data doi link for geospatial health data geospatial health data book modeling and visualization with rinla and shiny by paula moraga edition 1st edition first published 2019 ebook published 21 november 2019 pub location new york imprint chapman and hallcrc doi Geospatial health data modeling and visualization with r welcome the book geospatial health data modeling and visualization with rinla and shiny has been published by chapman amp hallcrc biostatistics series, and can be bought from crc press or the online version of the book can be read here, and it is licensed under a creative commons attributionnoncommercialnoderivatives 40 international license

Chapter 12 building a dashboard to visualize spatial data geospatial health data modeling and visualization with rinla and shiny chapter 12 building a dashboard to visualize spatial data with flexdashboard dashboards are tools for effective data visualization that help communicate information in an intuitive and insightful manner, and are essential to support datadriven decision making Geospatial health data modeling and visualization with r t1 geospatial health data modeling and visualization with rinla and shiny au moraga, paula py 20191120 y1 20191120 m3 book sn 9780367357955 bt geospatial health data modeling and visualization with rinla and shiny pb chapman and hallcrc er Pdf spatial and spatio temporal bayesian models with r geospatial health data are essential to inform public health and policy these data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities geospatial health data modeling and visualization with rinla and shiny describes spatial and spatiotemporal statistical

Detalles del Libro

  • Name: Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series)
  • Autor: Paula Moraga
  • Categoria: Libros,Libros universitarios y de estudios superiores,Medicina y ciencias de la salud
  • Tamaño del archivo: 16 MB
  • Tipos de archivo: PDF Document
  • Idioma: Español
  • Archivos de estado: AVAILABLE


Lee un libro Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC Biostatistics Series) de Paula Moraga Libros Gratis en EPUB

Preface geospatial health data modeling and preface geospatial health data modeling and visualization with rinla and shiny describes spatial and spatiotemporal statistical methods and visualization techniques to analyze georeferenced health data in r after a detailed introduction of geospatial data, the book shows how to develop bayesian hierarchical models for disease mapping and apply computational approaches such as the Chapter 13 introduction to shiny geospatial health data geospatial health data modeling and visualization with rinla and shiny chapter 13 introduction to shiny shiny chang et al 2019 is a web application framework for r that enables to build interactive web applications Geospatial health data modeling and visualization with r get this from a library geospatial health data modeling and visualization with rinla and shiny paula moraga this book shows how to model disease risk and quantify risk factors using areal and geostatistical data it also shows how to create interactive maps of disease risk and risk factors, and describes

Chapter 8 geostatistical data geospatial health data chapter 8 geostatistical data geostatistical data are measurements about a spatially continuous phenomenon that have been collected at particular sites this type of data may represent, for example, the disease risk measured using surveys at specific villages, the level of a pollutant recorded at several monitoring stations, and the density of mosquitoes responsible for disease transmission Geospatial health data modeling and visualization with r geospatial health data are essential to inform public health and policy these data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities geospatial health data modeling and visualization with rinla and shiny describes Geospatial health data modeling and visualization with r geospatial health data are essential to inform public health and policy these data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities geospatial health data modeling and visualization with rinla and shiny describes spatial and spatiotemporal statistical


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