Module dates/times: Monday, July 22; 8:30 a.m. -5 p.m.; Tuesday, July 23, 8:30 a.m.-5 p.m., and Wednesday, July 24, 8:30 a.m.-Noon

Prerequisites: The course assumes all the material in Module 4: MCMC I for Infectious Diseases or the equivalent knowledge of MCMC. Students are expected to have a working knowledge of the R computing environment. Programming will be in R. Students new to R should complete an extensive tutorial before the module. This module assumes knowledge of the material in Module 1: Probability and Statistical Inference, though not necessarily from taking that module.

Recommended, but not required: Knowledge of the material from Module 2: Mathematical Models of Infectious Diseases or Module 5: Stochastic Epidemic Models with Inference, or the equivalent, would be helpful, but not required.

This module continues on from Module 4 by looking in detail at practical implementation issues for MCMC methods when applied to data from infectious disease outbreaks. The main focus will be towards inference for the SIR (susceptible-infected-removed) model. Topics include parameterization, methods for improving convergence, assessing MCMC output, and data augmentation methods. Programming will be carried out in R.

Access 2018 Course Materials (Links under Assets on right-hand side)