Department of Biology, Duke University, Durham, North Carolina, United States of America.
PLoS Comput Biol. 2011 Aug;7(8):e1002136. doi: 10.1371/journal.pcbi.1002136. Epub 2011 Aug 25.
Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.
系统发生动力学 - 旨在定量整合快速进化种群（如 RNA 病毒）的生态和进化动态的领域 - 越来越依赖于合并方法，从重建的系统发育推断过去的种群动态。随着序列数据变得更加丰富，这些方法开始用于经历快速而复杂动态的种群。在这种情况下，当前系统发生动力学方法所采用的简单人口统计模型可能具有局限性。首先，这些模型并不理想，无法深入了解驱动感兴趣种群动态的过程。其次，这些模型在形式上与当前广泛用于拟合时间序列数据的机械和随机人口动态模型不同。因此，它们的使用不允许在进行推断时同时考虑系统发育数据和时间序列数据。在这里，我们提出了一种灵活的系统发生动力学推断统计框架，超越了这些当前的局限性。我们提出的框架采用了最近开发的一种称为粒子 MCMC 的方法，以贝叶斯框架将复杂人口动态的随机、非线性机械模型拟合到基因系统发育和时间序列数据。我们使用传染病传播动力学的非线性易感 - 感染 - 恢复（SIR）模型来演示我们的方法，并通过模拟表明，它可以从有或没有伴随时间序列数据的系统发育中准确估计过去的疾病动态和关键流行病学参数。
