A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.
A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.
复制标题
使用流行病学和遗传数据重建传输树的贝叶斯推理框架。
DOI：
10.1371/journal.pcbi.1002768
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发表时间：
2012
影响因子：
4.3
通讯作者：
Soubeyrand S
中科院分区：
文献类型：
--
作者：
Morelli MJ;Thébaud G;Chadœuf J;King DP;Haydon DT;Soubeyrand S
准确识别传染源通过宿主人群的传播途径对于了解其流行病学和告知其控制措施至关重要。然而，在流行病期间重建传播途径往往是一个未充分确定的问题：关于感染地点和时间的数据可能不完整、不准确，并且与大量不同的传播情景兼容。对于快速进化的病原体，如RNA病毒，可以通过使用基因数据来加强推断，这些数据现在很容易产生，而且价格低廉。然而，如果要可靠地估计传输树，则在这些不同数据类型的完全整合中仍然需要克服重大的统计挑战。在这里，我们提出了一个框架，导致一个baidu推理方案，结合遗传和流行病学数据，能够重建最有可能的传播模式和感染日期。在用模拟数据测试我们的方法后，我们将该方法应用于英国两次口蹄疫病毒（FMDV）流行：2007年爆发，以及2001年大流行的一个子集。在第一种情况下，我们能够确认一个特定前提作为流行病两个阶段之间联系的作用，而在空间和时间上更密集聚集的传播仍然难以解决。当我们考虑从2001年的流行病收集的数据在国家紧急状态的时候，我们的推理方案有力地推断出的传播链，并发现存在未被发现的前提下，从而提供了一个有用的工具，流行病学研究在真实的时间。在流行病学调查中，生成遗传数据已成为常规，但开发分析工具以最大限度地发挥这些数据的价值仍然是一个优先事项。我们的方法，而在这里应用的背景下，口蹄疫病毒，是一般的，稍加修改，可用于任何情况下，时空和遗传数据。为了最有效地控制传染病的传播，我们需要更好地了解病原体如何在宿主群体中传播，但我们对此知之甚少。在地点和时间上接近的病例通常被认为是有联系的，但单独的时间和地点通常与许多不同的谁感染谁的情况是一致的。许多病原体的基因组相对于它们传播的速度进化得如此之快，以至于即使在一次短暂的流行病中，我们也可以识别出哪些宿主含有彼此关系最密切的病原体。这些信息很有价值，因为当与空间和时间数据相结合时，它应该帮助我们更可靠地推断疾病爆发过程中谁传播给谁。然而，这样做，这三个不同的证据线得到适当的加权和解释仍然是一个重大的统计挑战。在我们的论文中，我们提出了一种新的统计方法，用于结合这些不同类型的数据和估计树，显示感染最有可能在宿主人群中的个体之间传播。因为基因物质测序已经变得如此便宜，我们认为像我们这样的方法对未来的流行病学将变得非常重要。
The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available. In order to most effectively control the spread of an infectious disease, we need to better understand how pathogens spread within a host population, yet this is something we know remarkably little about. Cases close together in their locations and timing are often thought to be linked, but timings and locations alone are usually consistent with many different scenarios of who-infected-who. The genome of many pathogens evolves so quickly relative to the rate that they are transmitted, that even over single short epidemics we can identify which hosts contain pathogens that are most closely related to each other. This information is valuable because when combined with the spatial and timing data it should help us infer more reliably who-transmitted-to-who over the course of a disease outbreak. However, doing this so that these three different lines of evidence are appropriately weighted and interpreted remains a major statistical challenge. In our paper we present a new statistical method for combining these different types of data and estimating trees that show how infection was most likely transmitted between individuals in a host population. Because sequencing genetic material has become so affordable, we think methods like ours will become very important for future epidemiology.
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DOI：
10.1126/science.1199884
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Charleston B;Bankowski BM;Gubbins S;Chase-Topping ME;Schley D;Howey R;Barnett PV;Gibson D;Juleff ND;Woolhouse ME
通讯作者：
Woolhouse ME
影响因子：
3.8
作者：
Jombart, T.;Eggo, R. M.;Dodd, P. J.;Balloux, F.
通讯作者：
Balloux, F.
影响因子：
4.9
作者：
Clement, M;Posada, D;Crandall, KA
通讯作者：
Crandall, KA
影响因子：
2.2
作者：
Gibbens, JC;Wilesmith, JW
通讯作者：
Wilesmith, JW
影响因子：
64.8
作者：
Rambaut, Andrew;Pybus, Oliver G.;Holmes, Edward C.
通讯作者：
Holmes, Edward C.