FITTING STOCHASTIC EPIDEMIC MODELS TO GENE GENEALOGIES USING LINEAR NOISE APPROXIMATION

• 成果类型: Article
• 署名作者: Tang, Mingwei; Dudas, Gytis; Bedford, Trevor; Minin, Vladimir N.
• 署名单位: University of Washington; University of Washington Seattle; University of Gothenburg; Fred Hutchinson Cancer Center
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1583
• 发表日期: 2023
• 页码: 1-22
• 关键词: bayesian nonparametric-inference; population-dynamics; coalescent; disease; skyline; SPREAD; hiv

摘要

Phylodynamics is a set of population genetics tools that aim at recon-structing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences, such estimation of population size trajectories can provide information about changes in the number of infec-tions. To model changes in the number of infected individuals, current phylo-dynamic methods use nonparametric approaches (e.g., Bayesian curve-fitting based on change-point models or Gaussian process priors), parametric ap-proaches (e.g., based on differential equations), and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of meth-ods yields results that are hard to interpret epidemiologically. The second class of methods provides estimates of important epidemiological parame-ters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically but relies on computationally intensive par-ticle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic mod-els and achieves computational tractability by using a linear noise approxima-tion (LNA)-a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using mod-ern Markov chain Monte Carlo tools to approximate the joint posterior distri-bution of the disease transmission parameters and of high dimensional vec-tors describing unobserved changes in the stochastic epidemic model com-partment sizes (e.g., numbers of infectious and susceptible individuals). In a simulation study we show that our method can successfully recover param-eters of stochastic epidemic models. We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.