Bayesian Workshop 2025

A novel development: virtual access to this important event.

A new online low student rate is also now available.

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

There are 8 sessions over 4 days, from June 3 to 6, 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 (9 a.m. to 12 noon EST).

June 3

• Session 1 consists of:
  • Basic concepts of Bayesian methods and disease mapping
• Session 2 consists of:
  • Bayesian computation: MCMC and alternatives

June 4

• Session 3 consists of:
  • R graphics for spatial health data
• Session 4 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 5

• Session 5 consist of:
  • Nimble for BHMs
• Session 6 consists of
  • CARBayes
  • INLA

June 6

• Session 7 consists of:
  • Space-time modelling and clustering in Nimble
• Session 8 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. A e-book or paperback version is included in the online registration.

Who Should Attend

The course is intended for epidemiologists and public health workers who need to analyze geographical disease incidence. In addition, the course sequence may be of interest to statisticians or geographers and planners who deal with spatial disease data. Some statistical/epidemiological background would be beneficial but not essential. Experience of basic R use is assumed.

Why Attend

Participants will gain an in-depth understanding of the basic issues, methods and techniques used in the analysis of spatial health data using a Bayesian approach on R. They will gain insight into the detailed analysis of practical problems in risk estimation and cluster detection. The course is presented by an acknowledged expert in the field of disease mapping and spatial epidemiology.

The Venue

This workshop will be hosted by the Medical University of South Carolina, Department of Public Health Sciences, 135 Cannon Street, Charleston, South Carolina. This year the workshop will be held virtually..

The course sequence will be accessed by a hyperlink. This will be sent to participants just prior to the event.

Registration queries can be made with Kristen DelliColli (dellicolli@musc.edu)

Technical queries can be made to Andrew Lawson (lawsonab@musc.edu).

Registration

To register, please complete the registration payment form.

Registration is not limited currently but a limit may be set later depending on demand.

The deadline for registration is May 20, 2025.

Conference registration per person:

• Workshop registration – $500 (after May 20)
• Early Bird – $400 (before May 20)
• Student rate – $300 (with registration receipt)

Refund Policy

Requests for refunds must be made in writing. There will be a $50 processing fee for cancellations before May 20.

No refunds can be given after May 20.

We reserve the right to reschedule the course or courses should circumstances dictate, giving reasonable notice to participants.