An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

TL;DR

This paper introduces a computationally efficient Bayesian inference framework using Hamiltonian Monte Carlo for coalescent models in phylodynamics, enabling faster and accurate reconstruction of population size dynamics from genetic data.

Contribution

It presents a novel Hamiltonian Monte Carlo-based method with Hamiltonian splitting to improve inference efficiency in coalescent-based phylodynamics, handling large datasets effectively.

Findings

Faster inference compared to existing methods

Accurate estimation of population size trajectories

Effective on both simulated and real datasets

Abstract

Phylodynamics focuses on the problem of reconstructing past population size dynamics from current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology, but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. While this approach is quite powerful, large data sets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand computationally more efficient inference framework. To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm.