Phylogenetic and epidemic modeling of rapidly evolving infectious diseases.
Phylogenetic and epidemic modeling of rapidly evolving infectious diseases.
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DOI：
10.1016/j.meegid.2011.08.005
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发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Drummond AJ
中科院分区：
文献类型：
--
作者：
Kühnert D;Wu CH;Drummond AJ
快速进化的病毒具有相同的生态和进化时间尺度。聚结分析对病毒进化和种群历史有很大的启示。流行病学动力学的新生领域统一了进化和流行病学模型。当前的建模进展集中在流行病的空间分布性质上。达尔文的各种形式的选择是对进化动力学方法的一个关键挑战。传染病的流行病学建模在理论和实证研究方面都有着悠久的历史。然而，最近爆炸的遗传数据揭示了快速的进化速度，许多人口的传染性病原体经历，并强调需要考虑进化和生态过程在同一时间尺度上。数学流行病学应用动力学模型来研究传染性流行病，但这些模型往往没有利用-或考虑-进化变化及其对生态过程和传染因子种群动态的影响。另一方面，统计遗传学已越来越多地应用于传染性病原体的研究。该方法基于遗传学、分子钟、基于系谱的群体遗传学和遗传地理学。贝叶斯马尔可夫链蒙特卡罗和相关的计算工具已经在这些统计系统发育方法的进步的主要来源。最近已经采取了初步的尝试性步骤来调和这两种理论方法。我们调查的贝叶斯系统发育方法传染病的流行病建模，并描述了它提供的数学流行病学的对比，以及强调这两个领域的未来统一的意义。
► Rapidly evolving viruses have the same timescale for their ecology and evolution. ► Coalescent analyses have shed much light on virus evolution and population history. ► The nascent field of phylodynamics unifies evolutionary and epidemiological models. ► Current modeling advances focus on the spatially distributed nature of epidemics. ► Darwinian selection in all its forms is a key challenge for phylodynamic methods. Epidemic modeling of infectious diseases has a long history in both theoretical and empirical research. However the recent explosion of genetic data has revealed the rapid rate of evolution that many populations of infectious agents undergo and has underscored the need to consider both evolutionary and ecological processes on the same time scale. Mathematical epidemiology has applied dynamical models to study infectious epidemics, but these models have tended not to exploit – or take into account – evolutionary changes and their effect on the ecological processes and population dynamics of the infectious agent. On the other hand, statistical phylogenetics has increasingly been applied to the study of infectious agents. This approach is based on phylogenetics, molecular clocks, genealogy-based population genetics and phylogeography. Bayesian Markov chain Monte Carlo and related computational tools have been the primary source of advances in these statistical phylogenetic approaches. Recently the first tentative steps have been taken to reconcile these two theoretical approaches. We survey the Bayesian phylogenetic approach to epidemic modeling of infection diseases and describe the contrasts it provides to mathematical epidemiology as well as emphasize the significance of the future unification of these two fields.
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影响因子：
3.9
作者：
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DOI：
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