Eight challenges in phylodynamic inference
Eight challenges in phylodynamic inference
复制标题
DOI：
10.1016/j.epidem.2014.09.001
复制
发表时间：
2015-03-01
期刊：
影响因子：
3.8
通讯作者：
Bedford, Trevor
中科院分区：
文献类型：
--
作者：
Frost, Simon D. W.;Pybus, Oliver G.;Bedford, Trevor
病原体动力学领域，试图提高我们的理解传染病的动力学使用病原体的病原体，在过去的十年中取得了长足的进步。基本的流行病学和进化模型现在已经有了很好的特征，并有了适当的推理框架。然而，在将动态推理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，如变化的突变率，选择，重配和重组，以及流行病学的复杂性，如随机种群动态，宿主种群结构，以及在宿主内和宿主间尺度的不同模式。另外的挑战存在于从不断增加的序列数据语料库进行有效推断中。(C)2014作者由爱思唯尔公司出版
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. (C) 2014 The Authors. Published by Elsevier B.V.