NIRG - Generating Socially Realistic Synthetic Networks

正在进行的2019冠状病毒病大流行表明，模拟可以通过多种方式在快速变化的世界中帮助决策者。有充分证据表明，为了响应尼尔·弗格森及其团队在帝国理工学院的建模工作，从2020年3月23日开始实施第一次封锁。他们的模型显示，如果疫情不受控制，英国可能会有数十万人死亡。在整个疫情期间，一直使用模拟来调整社会距离措施，在医院能力和限制活动的经济影响之间取得谨慎的平衡。衡量COVID-19传播和影响的最完整指标是住院率，因为并非所有病例都被发现。然而，暴露和可能入院之间的延迟意味着，即使是实时的住院数据，也要在必须做出决定之后才能获得。模拟之所以能够指导政策，是因为它们可以将理论过程与当前数据结合起来，构建关于可信未来的合理故事。即使未来的政策措施与产生数据的政策措施不同，这些故事也能够提供洞察力。大数据计划使得将学校、交通和其他基础设施纳入这些模拟相对简单。类似地，人口普查数据可以用来构建人工家庭，模拟人们去上班、上学或参加休闲活动。目前正在努力将更详细的高分辨率信息纳入流行病模型。然而，缺少的是关于社交网络的类似分辨率数据。多亏了一些大型研究，我们知道了有多少人彼此接触，以及这种接触模式的年龄和性别组合。但我们对更广泛的接触结构了解不多。与构建合成家庭的现有方法类似，我们需要新的方法来构建与真实网络足够相似的合成社会网络，以便在模拟中使用。例如，重要的是要包括识别共同朋友的结构，因为与感染者接触的一些人可能已经暴露。这个项目将开发构建合成社会网络的方法，再现我们知道在真实社会网络中很重要的结构属性，比如共同的朋友。

The ongoing COVID-19 epidemic has demonstrated the many ways in which simulations can assist policy makers in a rapidly changing world. It is well documented that the first lockdown was introduced from 23 March 2020 in response to modelling work by Neil Ferguson and his team at Imperial College. Their model showed that hundreds of thousands of deaths were likely in the UK if the epidemic was left uncontrolled. Simulations have been used throughout the epidemic to adjust social distancing measures in a careful balance between hospital capacity and the economic impact of restricting activities. The most complete measure of COVID-19 spread and impact is hospital admissions, because not all cases are detected. However, the delay between exposure and potential admission means that even real time hospital admission data are not available until after decisions must be made. Simulations are able to guide policy because they can combine theoretical processes with current data to construct justified stories about plausible futures. Such stories are able to provide insight even if the future policy measures are different than those that generated the data.Big data initiatives have made it relatively straightforward to include schools, transport and other infrastructure into these simulations. Similarly, census data can be used to construct synthetic households with simulated people who go to work or school or leisure activities. Efforts are ongoing to include more detailed high resolution information into epidemic models. What is missing, however, is similar resolution data about social networks. Thanks to some big studies, we know how many people come in contact with each other and the age and gender mix of such contact patterns. But we don't know much about the broader structure of contacts. In a parallel to existing methods to construct synthetic households, we need new methods to construct synthetic social networks that are similar enough to real networks to be used in simulations. It is important, for example, to include structures that recognise mutual friends because some of the people in contact with an infected person may be already exposed. This project will develop methods to build synthetic social networks that reproduce structural properties that we know are important in real social networks, like mutual friends.