Department of Biology, Duke University, Durham, NC 27708, USA.
J R Soc Interface. 2012 May 7;9(70):997-1007. doi: 10.1098/rsif.2011.0495. Epub 2011 Sep 15.
Coalescent theory provides a mathematical framework for quantitatively interpreting gene genealogies. With the increased availability of molecular sequence data, disease ecologists now regularly apply this body of theory to viral phylogenies, most commonly in attempts to reconstruct demographic histories of infected individuals and to estimate parameters such as the basic reproduction number. However, with few exceptions, the mathematical expressions at the core of coalescent theory have not been explicitly linked to the structure of epidemiological models, which are commonly used to mathematically describe the transmission dynamics of a pathogen. Here, we aim to make progress towards establishing this link by presenting a general approach for deriving a model's rate of coalescence under the assumption that the disease dynamics are at their endemic equilibrium. We apply this approach to four common families of epidemiological models: standard susceptible-infected-susceptible/susceptible-infected-recovered/susceptible-infected-recovered-susceptible models, models with individual heterogeneity in infectivity, models with an exposed but not yet infectious class and models with variable distributions of the infectious period. These results improve our understanding of how epidemiological processes shape viral genealogies, as well as how these processes affect levels of viral diversity and rates of genetic drift. Finally, we discuss how a subset of these coalescent rate expressions can be used for phylodynamic inference in non-equilibrium settings. For the ones that are limited to equilibrium conditions, we also discuss why this is the case. These results, therefore, point towards necessary future work while providing intuition on how epidemiological characteristics of the infection process impact gene genealogies.
合并理论为定量解释基因谱系提供了数学框架。随着分子序列数据的可用性增加,疾病生态学家现在经常将这一理论应用于病毒系统发育学,最常见的是尝试重建感染个体的人口历史,并估计基本繁殖数等参数。然而,除了少数例外,合并理论的核心数学表达式尚未与流行病学模型的结构明确联系起来,流行病学模型通常用于数学描述病原体的传播动力学。在这里,我们旨在通过提出一种在疾病动力学处于地方性平衡假设下推导模型合并率的一般方法来取得这一联系的进展。我们将这种方法应用于四种常见的流行病学模型家族:标准易感-感染-易感/易感-感染-恢复-易感模型、传染性个体异质性模型、具有暴露但尚未感染的类别模型和具有传染性期分布变量的模型。这些结果提高了我们对流行病学过程如何塑造病毒谱系以及这些过程如何影响病毒多样性水平和遗传漂变率的理解。最后,我们讨论了如何在非平衡环境中使用这些合并率表达式的子集进行系统发育推断。对于仅限于平衡条件的那些,我们还讨论了为什么会这样。因此,这些结果指出了未来必要的工作,同时提供了关于感染过程的流行病学特征如何影响基因谱系的直觉。