Data from: Phylodynamic model adequacy using posterior predictive simulations

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 (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing 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 was best described by the birth-death SIR model.

快速进化的病原体（如病毒与细菌）的遗传变异积累速率与其流行病学传播进程的时间尺度高度匹配，因此可借助系统发育时间树（phylogenetic time-trees）对其感染传播情况进行推断。为此，需选取恰当的病原系统动力学模型。然而，由此得到的推断结果的可靠性，取决于模型是否能够充分描述数据集的关键特征。模型适配性检验方法可在模型无法复现数据集核心特征时，正式拒绝该模型。我们开发了适用于主流BEAST2软件的TreeModelAdequacy（TMA）工具包，用于评估病原系统动力学模型的适配性。我们通过分析埃博拉（Ebola）与H1N1甲型流感两次病毒暴发的系统发育树，展示了该工具包的实用价值。埃博拉数据集的核心特征可通过溯祖指数增长模型得到充分拟合，而H1N1流感数据集的最佳拟合模型为出生死亡SIR模型。