用线性噪声近似拟合随机流行病模型的基因谱系。

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Annals of Applied Statistics Pub Date : 2023-03-01 DOI:10.1214/21-aoas1583
Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N Minin
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引用次数: 3

摘要

系统动力学是一套群体遗传学工具,旨在根据从感兴趣的群体中抽样的个体的分子序列重建群体的人口统计学历史。系统动力学的一个重要任务是估计(有效)种群大小的变化。当应用于传染病序列时,这种对种群大小轨迹的估计可以提供有关感染数量变化的信息。为了模拟感染人数的变化,目前的系统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)和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很难从流行病学上解释。第二类方法提供了重要的流行病学参数的估计,例如感染率和清除/恢复率,但忽略了传染病传播动态的变化。第三类方法在统计上是最有利的,但依赖于计算密集型的粒子滤波技术,限制了它的应用。我们提出了一种贝叶斯模型,结合了系统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)实现了计算可追溯性-一种允许我们近似随机流行病模型轨迹的概率密度的技术。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和描述随机流行病模型隔室大小(例如,感染和易感个体的数量)中未观察到的变化的高维向量的联合后向分布打开了大门。仿真研究表明,该方法可以成功地恢复随机流行病模型的参数。我们利用2014年塞拉利昂和利比里亚疫情的病毒遗传数据,将我们的估计技术应用于埃博拉谱系估计。
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Fitting stochastic epidemic models to gene genealogies using linear noise approximation.

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.

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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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