Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks

• Activity Number: 543 - SBSS Student Paper Competition I
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Fan Bu* and Allison E. Aiello and Jason Xu and Alexander Volfovsky
• Companies: Duke University and University of North Carolina at Chapel Hill and Duke University and Duke University
• Keywords: stochastic susceptible-infectious-recovered (SIR) model; continuous-time Markov chains; Bayesian data augmentation; conditional simulation; mobile healthcare

Abstract

We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, which is then coupled to an individual-level stochastic SIR model, in order to describe the interplay between epidemic dynamics on a network and network link changes. A Markov chain Monte Carlo framework is developed for likelihood-based inference from partial epidemic observations, with a novel data augmentation algorithm specifically designed to deal with missing individual recovery times under the dynamic network setting. Through a series of simulation experiments, we demonstrate the validity and flexibility of the model as well as the efficacy and efficiency of the data augmentation inference scheme. The model is also applied to a recent real-world dataset on influenza-like-illness transmission with high-resolution social contact tracking records.