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Eight challenges in phylodynamic inference

机译:系统动力学推论的八个挑战

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摘要

The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
机译:在过去的十年中,系统动力学领域试图通过病原体系统发育来增进我们对传染病动力学的理解,并取得了长足的进步。基本的流行病学和进化模型现在已经有了适当的推论框架。但是,在将系统动力学推论扩展到更复杂的系统方面仍然存在重大挑战。这些挑战包括解释进化的复杂性,例如改变突变率,选择,重配和重组,以及流行病学的复杂性,例如随机种群动态,宿主种群结构以及宿主内和宿主间规模的不同模式。从不断增加的序列数据语料库进行有效推断存在着另一个挑战。

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