Bayesian Workshop 2024

Announcing a virtual course sequence:

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling Parts I, II

March 4 and March 5, 2024

Course Content

These courses are designed to provide a comprehensive introduction to the area of Bayesian disease mapping using R in applications to Public Health and Epidemiology: Part I will run on March 4 and Part II on March 5, 2024.

Part I consists of sessions dealing with:

Morning Sessions

• Basic concepts of Bayesian methods and disease mapping
• Bayesian computation: MCMC and alternatives

Afternoon Sessions

• R graphics for spatial health data
• Bayesian Hierarchical Models for disease mapping (BHMs):
• Simple models: Poisson-gamma; log-normal, convolution.
• Variants: Leroux, mixture, BYM2.
• Model goodness of fit
• Demonstration of risk estimation and using R2OpenBUGS/OpenBUGS

Part II consists of sessions dealing with:

Morning Sessions

• Nimble
• CARBayes
• INLA

Afternoon Sessions

• Space-time modelling with McMC (Nimble)
• Space-time modelling with INLA
• Clustering in space and space-time
• Infectious disease modelling and surveillance

This workshop sequence 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, but also practical elements and participants will be involved in hands-on in the use of R, R2OpenBUGS(OpenBUGS), Nimble, CARBayes and INLA in disease mapping applications. Both human and veterinary examples will be covered in the course as well as simple space-time modelling. Examples will range over congenital anomaly birth data, a lung, larynx and oral cancer example, foot and mouth disease in the UK, and influenza and Covid-19 space-time modeling in South Carolina.

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 third edition of the book, "Bayesian Disease Mapping", appeared in 2018.

The Recent Addition

The course text for the workshop will be "Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" by A.B Lawson. A copy of the e-book is included in the fee for the courses. If requested, participants can also request the paperback version for an added fee.

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.

Course Fee and Requirements

Booking of the two-day course sequence is $500.

An early bird rate of $400 is available prior to February 20. After that date the full fee is payable.

A student rate of $300 for the course sequence is available with evidence of a registration receipt.

Attendees are recommended to download OpenBUGS (most recent version). Datasets will be provided. Download R software on the University of Cambridge website.

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 maptools, sp, spdep, sf, tmap, and ggplot2 will also be used.

R2OpenBUGS, Nimble, CARBayes, can all be installed from CRAN repositories in standard way.

Additional R packages will be needed, and notification of these will be sent to participants in the joining instructions.

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 Eden Mekonnen (mekonnen@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 February 20, 2024.

Conference registration per person:

• Complete course sequence – $500
• Early Bird – $400 (before February 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 February 20.

No refunds can be given after February 20.

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