Focusing on data commonly found in public health databases and clinical settings, <b>Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. <p>
Spatial Epidemiological Approaches in Disease Mapping and Analysis
โ Scribed by Poh-Chin Lai, Fun-Mun So, Ka-Wing Chan
- Publisher
- CRC Press
- Year
- 2008
- Tongue
- English
- Leaves
- 206
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries. Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data. Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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"Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in
Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments,<b>Bayesian Disease Mapping: Hierarchical Modeling i
Focusing on data commonly found in public health databases and clinical settings,<strong>Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology</strong>provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of diseas
<p>Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling i