NIRG - Generating Socially Realistic Synthetic Networks

NIRG - 生成社会现实的合成网络

• 批准号: MR/W02974X/1
• 负责人: Jennifer Badham
• 金额: $ 68.53万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW02974X%2F1
• 关键词: NIRG; Generating; Socially; Realistic; Synthetic

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.

持续不断的 COVID-19 疫情证明了模拟可以通过多种方式在快速变化的世界中为决策者提供帮助。有据可查的是，为了响应尼尔·弗格森和他在帝国理工学院的团队的建模工作，从 2020 年 3 月 23 日开始实施第一次封锁。他们的模型显示，如果疫情得不到控制，英国可能会导致数十万人死亡。在整个疫情期间，人们一直在使用模拟来调整社交距离措施，在医院容量和限制活动的经济影响之间保持谨慎的平衡。衡量 COVID-19 传播和影响的最全面指标是入院情况，因为并非所有病例都能被发现。然而，暴露和潜在入院之间的延迟意味着，即使是实时入院数据，也要在必须做出决定之后才能获得。模拟能够指导政策，因为它们可以将理论过程与当前数据结合起来，构建关于合理未来的合理故事。即使未来的政策措施与生成数据的政策措施不同，此类故事也能够提供洞察力。大数据举措使得将学校、交通和其他基础设施纳入这些模拟变得相对简单。同样，人口普查数据可用于构建模拟家庭，模拟人们去上班、上学或休闲活动。我们正在努力将更详细的高分辨率信息纳入流行病模型中。然而，缺少的是有关社交网络的类似分辨率数据。通过一些大型研究，我们知道有多少人相互接触，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知之甚少。与构建合成家庭的现有方法并行，我们需要新的方法来构建与模拟中使用的真实网络足够相似的合成社交网络。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了。该项目将开发构建合成社交网络的方法，这些网络可以再现我们所知道的在真实社交网络中很重要的结构属性，例如共同的朋友。