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Activity Number: 400 - Recent Advances in Bayesian Computation and Modeling of High-Dimensional Multivariate Data
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #330131 Presentation
Title: Bayesian Inference of Spreading Processes on Networks
Author(s): Antonietta Mira* and Ritabrata Dutta and Jukka-Pekka Onnela
Companies: Università della Svizzera italiana and Università della Svizzera italiana and Harvard T. H. Chan School of Public Health
Keywords: Approximate Bayesian Computation; Networks; Spreading Process; Epidemics; Bayesian Inference; Likelihood Free Inference

Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict risk and course of future outbreaks. Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social/contact network in an Indian village and an online social network in the U.S. Our goal is to learn simultaneously the spreading process parameters and the source node of the epidemic, given a fixed and known network structure, and observations on state of nodes at several points in time. Our inference scheme is based on approximate Bayesian computation (ABC), a technique for complex models with likelihood functions that are either expensive to evaluate or analytically intractable. ABC enables us to adopt a Bayesian approach to the problem despite the posterior distribution being very complex. Our method is agnostic about the topology of the network and the nature of the spreading process.

Authors who are presenting talks have a * after their name.

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