Frost Simon D W, Pybus Oliver G, Gog Julia R, Viboud Cecile, Bonhoeffer Sebastian, Bedford Trevor
Department of Veterinary Medicine, University of Cambridge, Cambridge, UK; Institute of Public Health, University of Cambridge, Cambridge, UK.
Department of Zoology, University of Oxford, Oxford, UK.
Epidemics. 2015 Mar;10:88-92. doi: 10.1016/j.epidem.2014.09.001. Epub 2014 Sep 16.
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.
系统发育动力学领域试图利用病原体系统发育来增进我们对传染病动态的理解，在过去十年中取得了长足进展。现在，基本的流行病学和进化模型已通过适当的推理框架得到了很好的描述。然而，将系统发育动力学推断扩展到更复杂的系统仍面临重大挑战。这些挑战包括考虑进化复杂性，如变化的突变率、选择、重配和重组，以及流行病学复杂性，如随机种群动态、宿主种群结构以及宿主内和宿主间尺度的不同模式。从不断增加的序列数据集中进行有效推断还存在另一个挑战。
