Age-specific representations of social contact networks using egocentric survey data

当对疾病爆发的传播进行建模时，流行病学家经常使用这样的假设，即感兴趣的人群是“混合良好的”。在这一假设下，所有个体以相同的速率相互作用，提供了一种在社会层面上预测疾病流行的强大机制。然而，在许多情况下，存在个体群体（“超级传播者”），他们感染的人比平均人数多得多。继发病例分布的这种异质性在不同的病原体和环境中普遍存在，流行病学文献中广泛使用80/20规则（80%的病例可归因于20%的感染个体）。控制超级传播者识别的因素还不是很清楚，可能是行为的、生物的或两者的混合。在建模过程中纳入继发性病例分布异质性的一种广泛使用的方法是在接触网络中诱导疾病传播。该项目将实施一种基于个人的网络方法，以更好地理解行为异质性。特别是，接触数据将从CoMix研究中推断出来，也可能从其他covid前调查中推断出来，如POLYMOD、BBC大流行调查[https://doi.org/10.1016/j.epidem.2018.03.003]]和沃里克接触调查。这些数据集为英国以自我为中心的接触结构提供了丰富的信息（例如，受访者的接触数量和类型），然而，从这类数据构建完整的人口层面接触网络的标准化机制并不存在。从这种以自我为中心的数据中创建具有代表性的联系网络有许多相互竞争的方法，其中许多方法未能提供已知存在于人类社交网络中的聚类或分类混合。另一个困难来自于真实链接权重的难以确定，由于相互作用的持续时间、相互作用的密切程度或其他因素，某些相互作用产生的链接权重更有可能传播疾病。该项目将探讨在网络链接结构和最终流行病动态方面进行接触的持续时间、物理性质和环境的重要性。它将弥合社会中普遍报道的网络的幂律度分布与广泛的病原体描述的负二项二次病例分布之间的差距。这可以通过结合基于每个接触者特征的链接权重，并使用流行病模型将这些权重拟合到已知的暴发继发性病例分布中来实现。Danon等人提出了将连接属性转换为链接权重的方法，我们可以在COVID-19爆发的情况下以及这些封锁造成的新型混合结构中进行探索。该项目将进一步考虑网络集群的影响（通常被观察为高度相互关联的个人的小集团）。在整个过程中，我将使用简单的分析方法和模拟模型的混合来获得更好的理解；两者都将由正在收集的大量观测资料提供信息或加以拟合。

When modelling the spread of a disease outbreak epidemiologists often use the assumption that the population of interest is 'well-mixed'. Under this assumption, all individuals interact at the same rate, providing a powerful mechanism to predict disease prevalence at a societal level. However, in many cases there exists groups of individuals ('superspreaders'), who infect disproportionately many more people than the mean. These heterogeneities in the secondary case distribution are pervasive in different pathogens and settings, with widespread use of the 80/20 rule throughout epidemiological literature (80% of cases are attributable to 20% of infected individuals). The factors governing the identification of superspreaders are not well understood, and could be behavioural, biological or a mixture of both. A widely used approach to incorporate heterogeneity of secondary case distribution in the modelling process is to induce disease transmission on a contact network. This project will implement an individual-based network approach, to better understand behavioural heterogeneities. In particular, contact data will be inferred from the CoMix study, and possibly other pre-COVID surveys such as POLYMOD, the BBC pandemic survey [https://doi.org/10.1016/j.epidem.2018.03.003] and the Warwick contact survey. These data sets provide a rich body of information on egocentric contact structures in the UK (e.g. the number and type of contacts from a respondent), however, a standardised mechanism for constructing the complete population level contact network from this type of data does not exist. There are many competing approaches to create a representative contact network from this egocentric data, with many failing to provide the clustering or assortative mixing known to be present in human social networks. One further difficulty arises from the intractability of true link weights, which emerge from some interactions being much more likely to spread disease, due to duration of interaction, the closeness of the interaction or other factors. This project will address the importance of duration, physicality and setting in which a contact is made on the network link structure and ultimately epidemicdynamics. It will bridge the gap between the power law degree distributions of networks commonly reported in society and the negative binomial secondary case distributions described for a wide range of pathogens.This can be achieved by incorporating link weights based on each contacts characteristics and fitting these using an epidemic model to known outbreak secondary case distributions. Danon et al. proposed methods to translate connection properties to link weights, which we can explore in the case of the COVID-19 outbreak and the novel mixing structures these lockdowns caused. The project will further consider the implications of network clustering (often observed as cliques of highly interconnected individuals). Throughout, I will use a mixture of simple analytical approaches and simulation models to gain a greater understanding; both will be informed by or fitted to the wealth of observations that are being collected.