The impending age of big data has been inescapable in recent discourse, both scientific and otherwise. The prevailing metaphors cast big data as a tsunami or an avalanche, suggesting natural disaster poised to dash hapless researchers against the rocks. They are, of course, no such thing, and offer many opportunities provided that one is prepared. Some of these opportunities were on show at the Royal Society discussion meeting on “Next-generation molecular and evolutionary epidemiology of infectious disease”.

一focus, inevitably, was next-generation sequencing, with Paul Kellam speaking about its importance in tracking the spread of the three waves of the 2009 H1N1 pandemic at a population level; but also for following the rapid spread of polymorphisms though the virus population within a single patient. Bill Hanage discussed the use of whole-genome data to investigate phylogenetic relationships within highly recombinogenic bacteria like肺炎链球菌，传统的遗传方法根本无法削减它。

一些约束的笔记也听起来很像。丹·海顿（Dan Haydon）讲述了一个警告性故事，说明当前深层测序无法分离个体内部的脚和口腔病毒与技术噪声的变化，并建议在对该病毒的进化研究中，个体之间的变化是我们可以在其中的最高分辨率。currently make reliable inferences (the resulting graphs using “1 cow” as a basic unit of time amused, although we don’t imagine it shall become an SI unit any time soon). Contrary to the “sequence everything” school of thought, Sharon Peacock of the Health Protection Agency made the case for sparing use of whole-genome sequencing in clinical microbiology, where phenotypic tests still offer good effectiveness for their cost and in the majority of cases sequencing is an unnecessary expense.

鉴于明确跟踪最近疾病传播史的能力，流行病学家也许对共同映射系统发育和空间数据有独特的兴趣，并且在建模包括狂犬病和流感等疾病的传播时，有许多关于空间系统动力学传播的可能性的谈判。。沙龙·孔雀（Sharon Peacock）给出了这种方法功效的最引人注目的例子。

但是，收集空间数据并不是直接的任务，监视的未来是一个受欢迎的主题。西蒙·海（Simon Hay）通过对现有数据和文献的调查进行仔细的策划，讨论了一个项目，以更新疾病的全球风险地图，这是“常常是恶魔般”的 - 但这在时间和资源上是非常昂贵的，以及这种策划的未来可能是由PubMed和GenBank等资源的自动数据挖掘驱动的。拉里·布莱特（Larry Brillirl）谈到了Google.org的Flu Trends，，，，which tracks outbreaks through users’ flu-related search terms with surprising success – often reporting peaks in flu activity a week or two ahead of the CDC’s GP-reported data – and went on to give an overview and endorsement of the current trend for web-based crowd-sourcing of reports through sites likeHealthMapand诺言。

Readers will, of course, be wondering about the privacy issues related to these new kinds of data-collection methods, and this was on attendees’ minds too. Nowhere is the discord between the need for patient privacy and the public health benefits of data release more apparent than epidemiology, where the geographic location of a patient – a key piece of information – goes a considerable way to revealing their identity. One unsavoury possibility is the future prospect of using a combination of genetic and epidemiological data to personally identify a key patient; say, “patient zero” for a particular pandemic, or an infection-multiplying “superspreader” for HIV – although it is important to emphasise that neither of these is likely at present. The discussion of these issues was only one aspect of a lively panel discussion to close the meeting, which also took in issues of data quality and accessibility, and how encouraging data citability might be one way to solve them. (Those interested in data citation might like to read the recentblog post并关联BMC Research Notes文章在当前的黄金标准上）。

For those whose interest in evolutionary epidemiology has been piqued, suggested further reading inBMC生物学来自Trevor Bedford及其同事的recent research建模H3N2流感病毒中静态多样性的进化原因；还有诺贝尔奖获得者彼得·多赫蒂（Peter Doherty）和同事保罗·托马斯（Paul Thomas）comment关于为什么知道在天然H5N1流感库中寻找哪些突变的原因比导致对特别有毒的实验室菌株的描述的重新描述的感知危险更为重要。