• [Baysian] Using R for Bayesian Spatial and Spatio-Temporal Health Modeling A virtual offering June 3rd - 6th 2025 COURSE CONTENT This course is designed to provide a comprehensive introduction to the area of Bayesian disease mapping using R in applications to Public Health and Epidemiology: There are 8 sessions occupying 3 hours with each morning including two sessions (9am - 12 noon EST) June 3rd Session I consists of: • Basic concepts of Bayesian methods and disease mapping Session II consists of: • Bayesian computation: MCMC and alternatives June 4th Session III consists of: • R graphics for spatial health data Session IV consists of: • Bayesian Hierarchical Models for disease mapping (BHMs): Demo of models with R2OpenBUGS Model goodness of fit and risk estimation using R2OpenBUGS Simple models: Poisson-gamma; log-normal, convolution. Variants: Leroux, mixture, BYM2. June 5th Session V consist of: Nimble for BHMs Session VI consists of • CARBayes • INLA June 6th Session VII consists of: • Space-time modelling and clustering in Nimble Session VIII consists of: • Space-time mechanistic (infectious disease) modeling in Nimble and Q&A

THE SPEAKER Andrew B. Lawson (Department of Public Health Sciences, College of Medicine, Medical University of South Carolina) is a MUSC Distinguished Professor Emeritus and a World Health Organization (WHO) advisor on Disease Mapping and organized with WHO an international workshop on this topic which has led to an edited volume "Disease Mapping and Risk Assessment for Public Health". He recently acted as chief editor of the CRC Handbook of Spatial Epidemiology (2016). He has published a number of books focused on disease mapping and spatial epidemiology. In particular, the 3rd edition of the book: Lawson, A. B. Bayesian Disease Mapping CRC Press, appeared in 2018. The recent addition: Lawson, A. B. (2021) Using R for Bayesian Spatial and Spatio-Temporal Health Modeling. CRC Press. will be a course text for the workshop. An e-book or paperback version is included in the online registration. COURSE FEE AND REQUIREMENTS Booking of the course is $500. **Early Bird rate of$400 before May 20th** New student rate (With proof of status): $300 A variety of R packages will be used in the workshop. The main R packages used will be R2OpenBUGS, Nimble, CARBayes, and INLA. The graphics libraries DCluster, sp, spdep, sf, and tmap will also be used.

Registration queries can be made with Kristen DelliColli (dellicolli@musc.edu) Technical queries can be made to Andrew Lawson (lawsonab@musc.edu) Registration form at : Bayesian Workshop 2025 https://medicine.musc.edu/departments/phs/news-and-events/bayesian-workshop