Supersize me: how whole-genome sequencing and big data are transforming epidemiology.
Supersize me: how whole-genome sequencing and big data are transforming epidemiology.
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DOI：
10.1016/j.tim.2014.02.011
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发表时间：
2014-05
影响因子：
15.9
通讯作者：
Murcia PR
中科院分区：
文献类型：
--
作者：
Kao RR;Haydon DT;Lycett SJ;Murcia PR
全基因组测序用于法医流行病学。大数据可以改变法医流行病学。集群、偏见、野生动物宿主和新出现的感染都可以得到解决。整合流行病学和进化数据的系统动力学方法非常成功，但仍然面临科学挑战。在流行病学中，识别“谁感染了谁”使我们能够量化关键特征，例如潜伏期、传播率的异质性、传染性持续时间以及高危人群的存在。尽管其价值无可估量，但许多看似合理的感染途径的存在使得这一过程变得困难，而且流行病学接触者追踪要么不确定，要么在后勤上令人望而却步，或者两者兼而有之。最近出现的下一代测序技术可以识别病原体基因组中可追踪的差异，这正在改变我们理解高分辨率疾病传播的能力，有时甚至可以深入到宿主到宿主的范围。我们回顾了最近使用病原体全基因组测序来对传播途径进行法医追踪的例子，重点关注进化动力学必须辅以有关最可能的事件发生时间和可能的传播途径的流行病学信息的特定问题。我们还讨论了过度解释这些数据的潜在陷阱，并强调了这项技术与复杂的数学和统计方法的融合有可能使我们对传染病传播和控制的理解发生范式转变。
Whole-genome sequencing is used for forensic epidemiology. Big data can transform forensic epidemiology. Clustering, biases, wildlife reservoirs, and emerging infections can all be addressed. Phylodynamics approaches to integrate epidemiological and evolutionary data have been highly successful but still face scientific challenges. In epidemiology, the identification of ‘who infected whom’ allows us to quantify key characteristics such as incubation periods, heterogeneity in transmission rates, duration of infectiousness, and the existence of high-risk groups. Although invaluable, the existence of many plausible infection pathways makes this difficult, and epidemiological contact tracing either uncertain, logistically prohibitive, or both. The recent advent of next-generation sequencing technology allows the identification of traceable differences in the pathogen genome that are transforming our ability to understand high-resolution disease transmission, sometimes even down to the host-to-host scale. We review recent examples of the use of pathogen whole-genome sequencing for the purpose of forensic tracing of transmission pathways, focusing on the particular problems where evolutionary dynamics must be supplemented by epidemiological information on the most likely timing of events as well as possible transmission pathways. We also discuss potential pitfalls in the over-interpretation of these data, and highlight the manner in which a confluence of this technology with sophisticated mathematical and statistical approaches has the potential to produce a paradigm shift in our understanding of infectious disease transmission and control.
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影响因子：
6.7
作者：
Biek, Roman;O'Hare, Anthony;Kao, Rowland R.
通讯作者：
Kao, Rowland R.
影响因子：
10.7
作者：
Bhatt, Samir;Holmes, Edward C.;Pybus, Oliver G.
通讯作者：
Pybus, Oliver G.
DOI：
10.1098/rspb.2002.2191
发表时间：
2003-01-22
影响因子：
4.7
作者：
Haydon, DT;Chase-Topping, M;Woolhouse, MEJ
通讯作者：
Woolhouse, MEJ
DOI：
10.1126/science.1182395
发表时间：
2010-01-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Harris SR;Feil EJ;Holden MT;Quail MA;Nickerson EK;Chantratita N;Gardete S;Tavares A;Day N;Lindsay JA;Edgeworth JD;de Lencastre H;Parkhill J;Peacock SJ;Bentley SD
通讯作者：
Bentley SD
影响因子：
9.8
作者：
Hampson K;Dushoff J;Cleaveland S;Haydon DT;Kaare M;Packer C;Dobson A
通讯作者：
Dobson A