Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom.
Proc Natl Acad Sci U S A. 2012 Sep 11;109(37):15066-71. doi: 10.1073/pnas.1206598109. Epub 2012 Aug 27.
We introduce a conceptual bridge between the previously unlinked fields of phylogenetics and mathematical spatial ecology, which enables the spatial parameters of an emerging epidemic to be directly estimated from sampled pathogen genome sequences. By using phylogenetic history to correct for spatial autocorrelation, we illustrate how a fundamental spatial variable, the diffusion coefficient, can be estimated using robust nonparametric statistics, and how heterogeneity in dispersal can be readily quantified. We apply this framework to the spread of the West Nile virus across North America, an important recent instance of spatial invasion by an emerging infectious disease. We demonstrate that the dispersal of West Nile virus is greater and far more variable than previously measured, such that its dissemination was critically determined by rare, long-range movements that are unlikely to be discerned during field observations. Our results indicate that, by ignoring this heterogeneity, previous models of the epidemic have substantially overestimated its basic reproductive number. More generally, our approach demonstrates that easily obtainable genetic data can be used to measure the spatial dynamics of natural populations that are otherwise difficult or costly to quantify.
我们在系统发生学和数学空间生态学这两个以前没有联系的领域之间建立了一个概念上的桥梁,使新兴传染病的空间参数能够直接从采样病原体基因组序列中估计出来。通过利用系统发生历史来纠正空间自相关,我们说明了如何使用稳健的非参数统计来估计扩散系数等基本空间变量,以及如何轻松量化扩散异质性。我们将该框架应用于西尼罗河病毒在北美的传播,这是一种新兴传染病在空间上入侵的重要实例。我们表明,西尼罗河病毒的传播速度比以前测量的要快得多,而且变化幅度也大得多,因此其传播速度极快,传播范围很广,这是在实地观察中不太可能发现的罕见的远距离传播所决定的。我们的研究结果表明,由于忽略了这种异质性,先前的传染病模型大大高估了其基本繁殖数。更一般地说,我们的方法表明,容易获得的遗传数据可用于测量自然种群的空间动态,而这些种群否则难以或难以量化。