Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics

TL;DR

This paper introduces an efficient INLA-based method for Bayesian nonparametric phylodynamics, enabling accurate and faster population size trajectory estimation from genetic data compared to traditional MCMC approaches.

Contribution

The paper adapts INLA for nonparametric Bayesian phylodynamics, providing a faster alternative to MCMC with high accuracy in estimating population size trajectories.

Findings

INLA achieves high accuracy in population size inference.

INLA significantly reduces computational time compared to MCMC.

Validated on hepatitis C and influenza virus genealogies.

Abstract

The goal of phylodynamics, an area on the intersection of phylogenetics and population genetics, is to reconstruct population size dynamics from genetic data. Recently, a series of nonparametric Bayesian methods have been proposed for such demographic reconstructions. These methods rely on prior specifications based on Gaussian processes and proceed by approximating the posterior distribution of population size trajectories via Markov chain Monte Carlo (MCMC) methods. In this paper, we adapt an integrated nested Laplace approximation (INLA), a recently proposed approximate Bayesian inference for latent Gaussian models, to the estimation of population size trajectories. We show that when a genealogy of sampled individuals can be reliably estimated from genetic data, INLA enjoys high accuracy and can replace MCMC entirely. We demonstrate significant computational efficiency over the state-of-the-art MCMC methods. We illustrate INLA-based population size inference using simulations and genealogies of hepatitis C and human influenza viruses.