传染性人格

CHANCES are your friends are more popular than you are. It is a basic feature of social networks that has been known about for some time. Consider both an avid cocktail party hostess with hundreds of acquaintances and a grumpy misanthrope, who may have one or two friends. Statistically speaking, the average person is much more likely to know the hostess simply because she has so many more friends. This, in essence, is what is called the “friendship paradox”: the friends of any random individual are likely to be more central to the social web than the individual himself.
可能您的朋友比您人缘好——这是我们一段时间来所知社交网络的基本特征之一。试想一位热情的鸡尾酒会女主人和一位性情乖戾的厌世人物，前者朋友数以百计，后者便是有也不过一二。概率上说，大家更可能认识那位女主人，仅仅因为她朋友更多。实际上，这就是我们所谓的“朋友悖论说”：社交网络中，任何人的朋友都可能比他（她）个人处于更中心的位置。

Now researchers think this seemingly depressing fact can be made to work as an early warning system to detect outbreaks of contagious diseases. By studying the friends of a randomly selected group of individuals, epidemiologists can isolate those people who are more connected to one another and are therefore more likely to catch diseases like the flu early. This could allow health authorities to spot outbreaks weeks in advance of current surveillance methods.
现在，科学家认为，这看似压抑的事实能作为早期预警系统，发现传染病疫情。传染病学家通过对一组随机挑选个体的朋友进行研究，将交际更广的人们隔离，从而更容易在早期窥见流感一类的疾病，卫生机构也能比现行的监测方法早几个周发现疫情。

In a report just posted on arXiv, an online repository of research papers, and which has been submitted to the Proceedings of the National Academy of Sciences, Nicholas Christakis from Harvard University and James Fowler from the University of California, San Diego put the friendship paradox to good use. In a trial carried out last autumn, they monitored the spread of both seasonal flu and H1N1, popularly known as swine flu, through students and their friends at Harvard University.
哈佛大学尼古拉斯•克里斯塔斯基教授和加州大学圣地亚哥分校詹姆士•福勒教授在研究性论文在线信息库arXiv发表的一篇报告中，很好地利用了“朋友悖论说”，此文已提交《美国国家科学院学报》。去年秋的试验中，他们在哈佛以学生和学生朋友为样本，监测了季节性流感和甲流——也就是人们常说的猪流感——的传播。更多信息请访问：http://www.24en.com/

Dr Christakis and Dr Fowler selected a random group of 319 undergraduates and asked each to nominate up to three friends. Using these names, they collected another group of 425 friends. As the friend paradox predicts, the second group were both more popular (named more times by the random group) and more central to the connections among Harvard students. Flu infections were monitored from September 1st 2009 to the end of December by identifying those diagnosed by the university’s health services and by e-mail responses to a twice-weekly health survey. Overall, 8% of the students were formally diagnosed with the flu and 32% were self-diagnosed. But the infection rate peaked two weeks earlier among the group of more-connected friends. Their social links were indeed causing them to get infected sooner.
克里斯塔斯基博士和福勒博士随机挑选了319名大学生，要求他们列出不多于三个朋友的名字，然后又用这些名字找到了另外425个朋友。按照“朋友悖论说”，第二组应该更受欢迎（被随机组点出次数更多），在哈佛学生交际网中处于更中心的位置。从2009年9月1日到12月末，这些学生都在接受流感感染的监测，一种方法是通过学校卫生部门的诊断，另一种方法是通过一周两次的健康调查电邮回复。总体说来，8%的学生被正式诊断出患有流感，另外32%也自称患有流感。但感染率在交际更广的群体中早两周达到顶峰。他们的社交纽带着实使得他们更早感染。

Early warning
早期预警

As this result came with the benefit of hindsight, the researchers tried to come up with a real-time measure that could potentially provide an early warning sign of an outbreak as it began to spread. To do this they went back to the beginning and compared diagnoses between the two groups on a daily basis for each of the 122 days of the study. A significant difference between the two groups was first detectable a full 46 days before visits to health services peaked for the random group. For those with self-reported symptoms, there was a noticeable difference 83 days before the peak in self-reported symptoms.
试验结果得出后，科学家们尝试在现实生活中总结措施，尽可能在疾病传播时提出疫情早期警示。因此，他们回归研究伊始，将122天每天两组样本的诊断结果进行了比较。较之随机组去卫生机构进行身体检查的高峰时段，两组的显著区别整整提前了46天。而对于自称出现症状的人们说来，显著区别在报告症状高峰前83天就已然出现。

These early results are impressive. Currently, the methods used to assess an infection by America"s Centres for Disease Control and Prevention lag an outbreak by a week or two. Google"s Flu Trends, which monitors millions of queries submitted to the giant search engine for the occurrence of flu-related keywords, is at best contemporaneous with an outbreak. Dr Christakis and Dr Fowler suggest that a hybrid method might be developed in which the search queries of a group of highly connected (ie, popular) individuals could be scanned for signs of the flu.
这些早期结果让人非常震撼。现在，美国疾病控制及预防中心判断传染性的方法要拖后疫情一到两周。谷歌“流感趋势”对提交到该搜索巨头流感出现相关关键字的上百万词条监测，至多也是与疫情同行。克里斯塔斯基博士和福勒博士认为，可以研究出一种混合方法，对一组广交际个体的搜索结果进行审视，寻找流感迹象。

Although the technique has so far only been demonstrated for the flu and in the social milieu of a university, the researchers nevertheless think that it could help predict other infectious diseases and do so on a larger scale, such as in cities and across regions. Nor should it be difficult to implement. Public-health officials already conduct random sampling, so getting the participants to name a few friends too should not be onerous. When it comes to infectious diseases, your friends really do say a lot about you.
这种方法迄今仅在大学环境下的流感疫情中使用，但是科学家认为这能够为其他传染性疾病的预测提供帮助，使用范围也可以推广到城市，乃至地区。推行应该也不会困难。公共卫生官员已经可以随机抽样，那么让样本个体说出几个朋友的名字应该也没什么麻烦。传染病方面，朋友可是真能提供您的不少信息呢。