B1790
Title: Bayesian inference from multiple sources to inform infectious disease health policy
Authors: Daniela De Angelis - University of Cambridge (United Kingdom) [presenting]
Abstract: Health related policy decision making for epidemic control is increasingly evidence-based, relying on the use of defendable models that realistically approximate the processes of interest and, crucially, incorporate all available information. From a statistical point of view, assimilation of information from a variety of heterogeneous, incomplete and biased sources poses a number of problems. We describe how a Bayesian approach to such evidence synthesis can accommodate all information in a single coherent probabilistic model and give examples to illustrate current challenges in this area.
