Speaker
Description
While there exist a number of mathematical approaches to modelling the spread of disease on a network, analyzing such systems in the presence of uncertainty introduces significant complexity. In scenarios where system parameters must be inferred from limited observations, general approaches to uncertainty quantification can generate approximate distributions of the unknown parameters, but these methods often become computationally expensive if the underlying disease model is complex. In this talk, I will apply transitional Markov chain Monte Carlo (TMCMC) using a massively parallelizable Bayesian uncertainty quantification framework to a model of disease spreading on a network of communities, showing that the method accurately and tractably recovers system parameters and selects optimal models in this setting.
