Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.
Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.
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DOI：
10.1371/journal.pcbi.1005416
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
Alizon S
中科院分区：
文献类型：
--
作者：
Saulnier E;Gascuel O;Alizon S
推断流行病学参数，如R 0从时间尺度的致病性是一个及时的挑战。大多数当前的方法依赖于似然函数，这提出了从计算这些函数到在数值上找到它们的最大值的具体问题。在这里，我们提出了一种新的基于回归的近似贝叶斯计算（ABC）的方法，我们基于各种各样的汇总统计数据，旨在捕获的信息中所包含的遗传和相应的血统，通过时间图。回归步骤涉及最小绝对收缩和选择算子（LASSO）方法，这是一种强大的机器学习技术。它使我们能够很容易地处理大量的汇总统计量，同时避免诉诸马尔可夫链蒙特卡罗（MCMC）技术。为了将我们的方法与现有方法进行比较，我们在各种流行病学模型和设置下模拟了目标树，并使用相同的先验知识推断了感兴趣的参数。我们发现，对于大型的遗传学，我们的回归ABC的准确性与BEAST 2中实施的涉及出生-死亡过程的基于可能性的方法相当。我们的方法甚至优于这些推断的主机人口规模与易感-感染-删除流行病学模型。它也明显优于最近的内核ABC方法时，假设一个易感感染的流行病学模型与两个主机类型。最后，通过重新分析塞拉利昂最近埃博拉疫情早期阶段的数据，我们发现回归ABC比基于可能性的方法提供了更现实的持续时间参数（潜伏期和传染性）估计。总的来说，ABC基于大量的汇总统计量和回归方法，能够进行变量选择，避免过度拟合是一种有前途的方法来分析大的遗传。鉴于许多病原体的快速进化，通过基因组学分析它们的基因组可以告诉我们它们是如何传播的。这是被称为“动力学”的领域的焦点。大多数现有的方法推断流行病学参数从病毒的致病性是有限的处理复杂的似然函数，其中通常包括潜在的变量的困难。在这里，我们使用一种称为基于回归的近似贝叶斯计算（ABC）的替代方法，它通过使用模拟和数据集比较来规避这个问题。由于遗传学很难相互比较，我们引入了许多汇总统计来描述它们，并利用当前的机器学习技术来进行变量选择。我们表明，我们达到的精度是现有的方法相媲美。这种准确性增加与遗传的大小，甚至可以高于现有的方法的某些参数。总的来说，基于回归的ABC开辟了新的视角来推断流行病学参数，从大的遗传。
Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies. Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.