Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Australia.
Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.
Syst Biol. 2019 Mar 1;68(2):358-364. doi: 10.1093/sysbio/syy048.
Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data were best described by the birth-death susceptible-infected-recovered model.
快速进化的病原体，如病毒和细菌，在其流行病学过程发生的相似时间尺度上积累遗传变化，因此，使用系统发育时间树可以对其传染性传播进行推断。为此，有必要选择一个系统发育动力学模型。然而，这种推断取决于模型是否能充分描述数据的关键特征。模型适当性方法允许如果模型不能生成数据的主要特征，则正式拒绝模型。我们提出了 TreeModelAdequacy，这是一个流行的 BEAST2 软件的包，它允许评估系统发育模型的适当性。我们通过分析埃博拉病毒和 H1N1 流感的两种病毒爆发的系统发育树来说明其效用。埃博拉病毒数据的主要特征被合并的指数增长模型充分描述，而 H1N1 流感数据则被出生-死亡易感-感染-恢复模型更好地描述。
