On December 17, Dr. Rob Deardon will present "Data uncertainty in population level infectious disease transmission modelling" at 1:30 pm in the AVC Learning Commons, 287N. The analysis of infectious disease data is complicated by the fact that much of the information we would like to have about disease spread is hard, or even impossible, to obtain. For example, dates of infection are almost never observed; at least not with any great accuracy. Similarly, information about various potential risk factors (e.g. vaccination status, genetics, contact network information) may also be missing or prone to measurement error. A Bayesian approach to the statistical modelling of infectious disease spread offers a conceptually simple and sound method of dealing with such missing or uncertain data. Here we detail how this approach can be used in modelling infectious disease transmission data, and discuss one or two specific examples. Dr. Deardon is an associate professor at the University of Guelph.