Data-driven prediction and origin identification of epidemics in population networks

10 Jul 2018, 11:10
New Law School/--020 (University of Sydney)

New Law School/--020

University of Sydney

Oral Presentation Disease - infectious Epidemiology


Karen Larson (Brown University)


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.

Primary authors

Karen Larson (Brown University) Zhizhong Chen (Brown University) Clark Bowman (Brown University) Panagiotis Hadjidoukas (ETH Zürich) Costas Papadimitriou (University of Thessaly) Petros Koumoutsakos (ETH Zürich) Anastasios Matzavinos (Brown University)

Presentation Materials

There are no materials yet.