Phylodynamics is used to estimate epidemic dynamics from phylogenetic trees or genomic sequences of pathogens, but the likelihood calculations needed can be challenging for complex models. We present a neural Bayes estimator (NBE) for key epidemic quantities: the reproduction number, prevalence, and cumulative infections through time. By performing quantile regression over tree space, the NBE allows us to estimate posterior medians and credible intervals directly from a reconstructed tree. Our approach uses a recursive neural network as a tree embedding network with a prediction network conditioned on time and quantile level to generate the estimates. In simulation studies, the NBE achieves good predictive performance, with conservative uncertainty estimates. Compared with a BEAST2 fixed-tree analysis, the NBE gives less biased estimates of time-varying reproduction numbers in our test setting. Under a misspecified sampling model, the NBE performance degrades (as expected) but remains reasonable, and fine-tuning a pre-trained model yields estimates comparable to those from a model trained from scratch, at substantially lower computational cost.

翻译：系统发育动力学用于从病原体的系统发育树或基因组序列中估计流行病动态，但对于复杂模型，所需的似然计算可能具有挑战性。我们提出了一种用于关键流行病学量的神经贝叶斯估计器（NBE）：随时间变化的再生数、流行率和累计感染数。通过在树空间上进行分位数回归，NBE使我们能够直接从重建的树中估计后验中位数和可信区间。我们的方法使用递归神经网络作为树嵌入网络，并结合一个以时间和分位数水平为条件的预测网络来生成估计值。在模拟研究中，NBE取得了良好的预测性能，并提供了保守的不确定性估计。与BEAST2的固定树分析相比，在我们的测试设置中，NBE对时变再生数的估计偏差更小。在错误设定的抽样模型下，NBE的性能（如预期）有所下降但仍保持合理，并且对预训练模型进行微调所产生的估计结果与从头训练的模型相当，而计算成本显著降低。