柳叶刀：你的朋友们或可帮助你抵御流感

  近日，发表在国际杂志The Lancet上的一篇研究报告中，来自兰卡斯特大学的研究人员通过研究表示，有众多朋友的人们应该优先考虑接种流感疫苗，因为接种流感疫苗很有可能会影响其他人也进行疫苗的接种。

  流感是影响全球人类健康的一种疾病，其每年大约影响着300至500万人的健康，其往往会引发老年人较高的死亡率；文章中研究者通过研究分析了如何改善医护人员接种流感疫苗的效率从而来避免其向病人传染流感。每年政府都会制定一个目标让75%的医护人员接种流感疫苗，但实际上仅有一半人进行了疫苗的接种。

  Rhiannon Edge， Joseph Heath， Barry Rowlingson， Drs Thomas Keegan及Rachel Isba都是马上毕业即将进入医疗战线成为医护人员的博士，他们对当前医学院学生流感疫苗的接种情况进行了相关调查。文章中，研究者询问学生同其不同朋友之间的关系以及其疫苗的接种情况，结果显示，和社会网络存在一定关联的个体可能会影响其朋友的行为，甚至在并不存在直接关系的情况下也会使得其朋友圈中的个体的行为受到影响。如果一个个体进行了疫苗的接种，那么他的朋友圈中未进行接种的个体也都会相继接种疫苗从而有效抑制流感的传播和爆发。

  研究人员开发并且对流感爆发进行了计算机模拟，来揭示人际关系较好的个体在疫苗接种后如何来通过其人际圈影响其周围朋友疫苗的接种率；本文研究揭示了在一个社交网络中对高度连接个体（即人际关系好的个体）进行靶向疫苗注射策略或许可以有效改善疫苗的接种率，从而有效抑制感染性疾病的扩散。（转化医学网360zhyx.com）

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转化医学网推荐的原文摘要：

Seasonal influenza in medical students: an outbreak simulation model based on a social network approach
The Lancet doi:10.1016/S0140-6736(14)62155-3
Rhiannon Edge, BSc, Joseph Heath, Barry Rowlingson, BSc, Thomas Keegan, PhD, Rachel Isba, PhD
Background
There is increasing interest in the effects of social networks on disease dynamics. We simulated the spread of influenza through a population of medical students where transmission was related to the social network structure and vaccination status of individuals.

Methods
All students at Lancaster Medical School, Lancaster, UK (n=253) were asked to rate the strength of their relationship with all other students from the medical school. Students also self-reported their influenza vaccination status. An individual-based outbreak model was developed using R statistical software. Using these data, combined with appropriate transmission parameters, we simulated an influenza outbreak and assessed the effects of preferentially vaccinating according to the social network analysis data. We ran the simulation 1500 times. Each simulation selected a random student to introduce the virus into the population. For each vaccination strategy, the likelihood of each individual being infected was calculated as a percentage of the number of times they were infected in the 1500 possible outbreaks.

Findings
215 students (85%) responded. Non-responders were assumed to have reciprocal relationships with responders; therefore it was possible to construct the entire medical student network. We found that the outcomes of vaccination strategies based on between-ness (the extent to which an individual lies between others in the network) and degree (the number of connections an individual has), which are both measures of connectedness, quickly converged. As more individuals were vaccinated, the likelihood of individuals contracting the infection tended to be similar, irrespective of vaccination based on between-ness or degree. After vaccination of an additional 8% of the population (20 students) the outcome of the experimental influenza outbreak was similar for both strategies.

Interpretation
Our results add to a small pool of evidence supporting targeting vaccination of individuals according to between-ness in an attempt to reduce the spread of influenza. However, in small, densely connected populations, vaccination according to degree might be preferential because of the rapid convergence and the relative ease of locating individuals with a high degree versus those with high between-ness. This study suggests that vaccination strategies that target highly connected individuals within a network might limit spread of infectious disease. Future work could include evaluating current vaccination approaches using social network analysis.

Funding
University Hospitals of Morecambe Bay NHS Foundation Trust funded data collection.