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Activity Number: 19 - Advances in Statistical Modeling and Inference for Phylodynamics and Molecular Epidemiology
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #324607
Title: Bayesian Phylodynamic Inference for Infectious Disease Dynamics Using the Linear Noise Approximation
Author(s): Mingwei Tang* and Vladimir Minin
Companies: Department of Statistics, University of Washington and University of Washington
Keywords: Coalescent ; effective population ; SIR model ; Linear Noise Approximation ; MCMC
Abstract:

Phylodynamics is an area at the intersection of phylogenetics and population genetics that aims at reconstructing population size trajectories based on genetic data. Phylodynamic method relies on the coalescent, a stochastic point process that generates genealogies connecting randomly sampled individuals from the population of interest. Current existing approaches use non-parametric Gaussian process to estimate the effective population trajectory. However, those models cannot give further inference on attributes of the epidemiology, eg. infection rate. In this paper, we propose a parametric bayesian framework that combines phylodynamics inference and stochastic epidemiological model. In our framework, the population trajectory is model by Susceptible-Infected-Recovered (SIR) model. We use the Linear Noise Approximation (LNA) approach to approximate the SIR dynamics with a multivariate Gaussian process and use Markov chain Monte Carlo (MCMC) method to obtain posterior samples for the disease transmission parameters and latent population trajectory. Furthermore, we apply a period parameterization that allows inference infectious disease dynamics over multiple seasons.


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