This workshop will introduce the recommended workflow for applied Bayesian data analysis by working through an example analysis together. We will start with the simplest non-trivial model and use increasingly sophisticated models to explain the properties of our data set based on model diagnostics. We will also give an overview of the different probabilistic programming packages in Julia and show where we have advantages over other languages such as Stan and Python.

Designing vaccines is an expensive and time consuming process. This talk demonstrates how we can exploit automatic differentiation of ODEs, parallelization, stochastic search and Bayesian optimization to minimize post-vaccination invasive pneumococcal disease and antibiotic resistant strains in a bacteria population using a novel computational model of the bacterial population dynamics that integrates epidemiological and genomic data.