Tang Mingwei, Dudas Gytis, Bedford Trevor, Minin Vladimir N
Department of Statistics, University of Washington, Seattle.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center.
Ann Appl Stat. 2023 Mar;17(1):1-22. doi: 10.1214/21-aoas1583. Epub 2023 Jan 24.
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. 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 infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches (e.g., Bayesian curve-fitting based on change-point models or Gaussian process priors), parametric approaches (e.g., based on differential equations), and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard to interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, 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 particle filtering techniques that limits its applications. 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 probability densities 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). In a simulation study, we show that our method can successfully recover parameters 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.
系统发育动力学是一组群体遗传学工具，旨在根据从感兴趣的群体中采样的个体的分子序列重建该群体的人口统计学历史。系统发育动力学中的一项重要任务是估计（有效）种群大小的变化。当应用于传染病序列时，这种种群大小轨迹的估计可以提供有关感染数量变化的信息。为了模拟感染个体数量的变化，当前的系统发育动力学方法使用非参数方法（例如，基于变化点模型或高斯过程先验的贝叶斯曲线拟合）、参数方法（例如，基于微分方程）以及结合无似然贝叶斯方法的随机建模。第一类方法产生的结果在流行病学上难以解释。第二类方法提供了重要流行病学参数的估计值，如感染率和清除/恢复率，但忽略了传染病传播动态中的变化。第三类方法在统计上最具优势，但依赖于计算密集型的粒子滤波技术，这限制了其应用。我们提出了一种贝叶斯模型，该模型结合了系统发育动力学推断和随机流行病模型，并通过使用线性噪声近似（LNA）实现了计算上的易处理性——这是一种使我们能够近似随机流行病模型轨迹概率密度的技术。LNA为使用现代马尔可夫链蒙特卡罗工具来近似疾病传播参数以及描述随机流行病模型区室大小（例如，感染个体和易感个体的数量）未观察到变化的高维向量的联合后验分布打开了大门。在一项模拟研究中，我们表明我们的方法能够成功恢复随机流行病模型的参数。我们将我们的估计技术应用于使用来自2014年塞拉利昂和利比里亚埃博拉疫情的病毒基因数据估计的埃博拉谱系。
