Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.
Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.
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在流行病学和宏观进化中统一的系统发育死亡模型。
DOI：
10.1093/sysbio/syab049
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发表时间：
2021-12-16
影响因子：
6.5
通讯作者：
Pennell MW
中科院分区：
文献类型：
--
作者：
MacPherson A;Louca S;McLaughlin A;Joy JB;Pennell MW
死亡随机过程是许多系统发育模型的基础，被广泛用于流行病学和宏观进化动力学的推断。有大量的出生-死亡模型的变种，已经开发出来，这些强加不同的假设的时间动态的参数和采样过程。由于这些变体中的每一个都是单独衍生的，因此很难理解它们之间的关系以及它们精确的生物学和数学假设。没有共同的数学基础，推导新的模型是不平凡的。在这里，我们将这些模型统一到一个框架中，证明了许多以前开发的流行病学和宏观进化模型都是一个更一般的模型的特例，并说明了这些变体之间的联系。这种统一包括两种模式，即所有血统的过程都是相同的，以及不同类型的过程不同。我们还概述了一个简单的程序推导出任意复杂的出生-死亡（抽样）模型，希望能让研究人员探索更广泛的场景比以前可能的似然函数。通过重新推导现有的单一类型的出生-死亡抽样模型，我们澄清和合成的显式和隐式的假设，这些模型。[死亡过程;流行病学;宏观进化;遗传学;统计推断。]
Birth–death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth–death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth–death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth–death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth–death processes; epidemiology; macroevolution; phylogenetics; statistical inference.]
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影响因子：
6.5
作者：
Etienne RS;Rosindell J
通讯作者：
Rosindell J
年龄依赖性物种可以解释经验系统发育的形状。
影响因子：
6.5
作者：
Hagen O;Hartmann K;Steel M;Stadler T
通讯作者：
Stadler T
影响因子：
10.7
作者：
Drummond, Alexei J.;Suchard, Marc A.;Rambaut, Andrew
通讯作者：
Rambaut, Andrew
影响因子：
3.9
作者：
Barido-Sottani, Joelle;Vaughan, Timothy G.;Stadler, Tanja
通讯作者：
Stadler, Tanja
采样祖先树的贝叶斯推断用于流行病学和化石校准。
影响因子：
4.3
作者：
Gavryushkina A;Welch D;Stadler T;Drummond AJ
通讯作者：
Drummond AJ