标题： 随机时间偏移近似方法实现了在大规模数据集上对机制性宿主内病毒动力学模型的分层贝叶斯推断 显示英文标题

标题： Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets

摘要： 机制性数学模型是研究病毒生物学及其与免疫系统相互作用如何影响宿主传染性的工具。 该过程的生物学特性由模型的结构和参数编码，这些参数可以通过对病毒载量数据进行拟合来统计推断。 机制模型的主要缺点是这种推断计算成本高，因为必须反复求解模型。 这限制了可以考虑的数据集大小或所拟合模型的复杂性。 在本文中，我们通过实现一种新的模型动态近似方法，开发了一种更便宜的推断方法，该方法结合了随机和确定性过程。 这种近似方法在感染早期正确考虑了细胞和病毒粒子数量较少时的过程噪声，这对病毒动力学很重要，但常常被忽视。 我们的方法可以在普通笔记本电脑上运行，并且足够快，可以促进对该问题的完整分层贝叶斯处理，通过信息共享来允许个体水平的参数差异。 我们将该方法应用于模拟数据以及对163名个体的国家篮球协会队列中的新冠状病毒监测数据的重新分析。 显示英文摘要

摘要： Mechanistic mathematical models of within-host viral dynamics are tools for understanding how a virus' biology and its interaction with the immune system shape the infectivity of a host. The biology of the process is encoded by the structure and parameters of the model that can be inferred statistically by fitting to viral load data. The main drawback of mechanistic models is that this inference is computationally expensive because the model must be repeatedly solved. This limits the size of the datasets that can be considered or the complexity of the models fitted. In this paper we develop a much cheaper inference method by implementing a novel approximation of the model dynamics that uses a combination of random and deterministic processes. This approximation also properly accounts for process noise early in the infection when cell and virion numbers are small, which is important for the viral dynamics but often overlooked. Our method runs on a consumer laptop and is fast enough to facilitate a full hierarchical Bayesian treatment of the problem with sharing of information to allow for individual level parameter differences. We apply our method to simulated data and a reanalysis of COVID-19 monitoring data in an National Basketball Association cohort of 163 individuals.