Waddel Hannah, Koelle Katia, Lau Max S Y
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
Department of Biology, Emory University, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2025 Jun 10;21(6):e1012657. doi: 10.1371/journal.pcbi.1012657. eCollection 2025 Jun.
Phylodynamic models capture joint epidemiological-evolutionary dynamics during an outbreak, providing a powerful tool to enhance understanding and management of disease transmission. Existing phylodynamic approaches, however, mostly rely on various non-mechanistic or semi-mechanistic approximations of the underlying epidemiological-evolutionary process. Previous work by Lau and colleagues has shown that full Bayesian mechanistic models, without relying on these approximations, can enable highly accurate joint inference of the epidemiological-evolutionary dynamics including the unobserved transmission tree. However, the Lau method faces major computational bottlenecks. As the volume of genomic data collected during outbreaks continues to grow, it is crucial to develop scalable yet accurate phylodynamic methods. Here we propose a new Bayesian phylodynamic model, overcoming the major scalability issue in the previous method and enabling a readily deployable, yet accurate, phylodynamic modeling framework. Specifically, we develop a scalable spatio-temporal phylodynamic framework for inferring the transmission tree (ScITree) and other key epidemiological parameters considering the infinite sites assumption in modeling mutation on the sequence level, in contrast to the Lau method in which mutation was modeled explicitly on the nucleotide level. Our approach features full Bayesian implementation utilizing an exact likelihood to mechanistically integrate epidemiological and evolutionary processes. We develop a computationally-efficient data-augmentation Markov Chain Monte Carlo algorithm, inferring key model parameters and unobserved dynamics including the transmission tree. We assess performance of our method using multiple simulated outbreak datasets. Our results indicate that our method can achieve high inference accuracy, comparable to the performance of the Lau method. Additionally, our method scales significantly more efficiently for large outbreaks, with computing time increasing linearly with outbreak size, compared to the exponential scaling of the Lau method. We also demonstrate our method's utility by applying our validated modeling framework to a dataset describing a foot-and-mouth disease outbreak in the UK. Our results show that our method is able to generate estimates of the transmission dynamics consistent with those from the prior method, further demonstrating the robustness of our new approach. In summary, our method provides a computationally-efficient, highly scalable, accurate modeling framework for inferring the joint spatio-temporal dynamics of epidemiological and evolutionary processes, facilitating timely and effective outbreak responses in space and time. Our method is implemented in our R package ScITree.
系统动力学模型能够捕捉疾病爆发期间联合的流行病学 - 进化动力学，为加强对疾病传播的理解和管理提供了一个强大的工具。然而，现有的系统动力学方法大多依赖于对潜在的流行病学 - 进化过程的各种非机械或半机械近似。Lau及其同事之前的工作表明，完全贝叶斯机械模型在不依赖这些近似的情况下，能够对包括未观察到的传播树在内的流行病学 - 进化动力学进行高度准确的联合推断。然而，Lau方法面临着重大的计算瓶颈。随着疾病爆发期间收集的基因组数据量持续增长，开发可扩展且准确的系统动力学方法至关重要。在此，我们提出了一种新的贝叶斯系统动力学模型，克服了先前方法中的主要可扩展性问题，并实现了一个易于部署且准确的系统动力学建模框架。具体而言，我们开发了一个可扩展的时空系统动力学框架来推断传播树（ScITree）以及其他关键的流行病学参数，该框架在序列水平上考虑了建模突变时的无限位点假设，这与Lau方法不同，后者是在核苷酸水平上明确建模突变。我们的方法具有完全贝叶斯实现，利用精确似然性来机械地整合流行病学和进化过程。我们开发了一种计算效率高的数据增强马尔可夫链蒙特卡罗算法，用于推断关键模型参数和包括传播树在内的未观察到的动力学。我们使用多个模拟的疾病爆发数据集评估了我们方法的性能。我们的结果表明，我们的方法能够实现高推断准确性，与Lau方法的性能相当。此外，对于大规模爆发，我们的方法扩展性显著更高，计算时间随爆发规模呈线性增加，而Lau方法的计算时间呈指数增长。我们还通过将经过验证的建模框架应用于描述英国口蹄疫爆发的数据集，展示了我们方法的实用性。我们的结果表明，我们的方法能够生成与先前方法一致的传播动力学估计值，进一步证明了我们新方法的稳健性。总之，我们的方法为推断流行病学和进化过程的联合时空动力学提供了一个计算效率高、高度可扩展且准确的建模框架，有助于在空间和时间上及时有效地应对疾病爆发。我们的方法在我们的R包ScITree中实现。
