Fitting Stochastic Epidemic Models to Gene Genealogies Using Linear Noise Approximation

• Activity Number: 231 - SBSS Student Paper Award Session II
• Type: Topic Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Mingwei Tang* and Gytis Dudas and Trevor Bedford and Vladimir Minin
• Companies: University of Washington and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and University of California, Irvine
• Keywords: phylodynamics; stochastic epidemic model; linear noise approximation; Markov chain Monte Carlo; Ebola virus

Abstract

Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. When applied to infectious disease sequences such estimation of population history can provide information about changes in the number of infections. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate density of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.