SC04 - USING R FOR BAYESIAN SPATIAL AND SPATIO-TEMPORAL HEALTH DATA MODELING
International Biometrics Conference Atlanta December 8th 2024.
Full day course 0900 - 1800
Announcing an opportunity to gain insight into the application
of Bayesian Hierarchical modeling with geo-referenced health data.
R is commonly use now for advanced Biostatistical applications. Bayesian spatial and spatio-temporal modeling of health data is an important topic which can be addressed using tools in R. This course is designed for those who want to cover mapping methods, and the use of a variety of software and variants in application to small area health data. The course will include theoretical input, covering selected Bayesian spatial models, but also practical elements and participants will be involved in hands-on in the use of R, R2OpenBUGS, Nimble, and CARBayes in disease mapping applications. Examples will focus on aggregate spatial count data as well as simple space-time modelling. Examples will focus on county level respiratory cancer incidence (spatial) and variants and in US states. The course would be suitable for those with some R experience, but limited experience of spatial modeling in health applications. A recent text on this topic is
Lawson, A. B. (2023) Using R for Bayesian Spatial and Spatio-temporal Health Modeling, CRC Press (paperback)
has appeared and forms the basis of this course delivery.
Presenter:
Andrew B Lawson
Professor of Biostatistics in the Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, College of Medicine, MUSC and is an MUSC Distinguished Professor Emeritus and ASA Fellow. His PhD was in Spatial Statistics from the University of St. Andrews, UK.
He has over 200 journal papers on the subject of spatial epidemiology, spatial statistics and related areas. In addition to a number of book chapters, he is the author of 10 books in areas related to spatial epidemiology and health surveillance. The most recent of these is Lawson, A.B. et al (eds) (2016) Handbook of Spatial Epidemiology. CRC Press, New York, and in 2018 a 3rd edition of Bayesian Disease Mapping; hierarchical modeling in spatial epidemiology CRC Press. In 2021. a new volume entitled Using R Bayesian Spatial and Spatio-temporal Health modeling CRC Press appeared. He has acted as an advisor in disease mapping and risk assessment for the World Health Organization (WHO) and is founding editor of the Elsevier journal Spatial and Spatio-temporal Epidemiology. Dr Lawson has delivered many short courses in different locations over the last 20 years on Bayesian Disease Mapping with OpenBUGS, INLA, and Nimble, and more general spatial epidemiology topics.
Web site: http://people.musc.edu/~abl6/
Outline
AM
• Basic concepts of Bayesian methods and disease mapping
Epidemiological issues
Statistical issues
Bayesian inference
• Bayesian computation: MCMC and alternatives
Gibbs, MH, HMC, MALA
PM
• R graphics for spatial health data
Use of packages such as AKIMA, MBA, DCluster, sp, spdep, sf, tmap
• Bayesian Hierarchical Models for disease mapping (BHMs):
Simple models: Poisson-gamma; log-normal, convolution.
Variants: mixture, BYM2.
• Demonstration of risk estimation and using McMC packages
R2OpenBUGS
Nimble
CARBayes
• Basic space-time modelling (if time permits)
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Andrew Lawson
Distinguished Professor Emeritus
Medical University of South Carolina - Dept of Public Health Sciences
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