Department of Computer Science, University College London, London, UK.
Institute of Neurology, University College London, London, UK.
Sci Rep. 2022 May 11;12(1):7692. doi: 10.1038/s41598-022-11786-5.
How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its topology. In this paper, we deploy simulations to understand and quantify the impact on disease transmission of a set of topological network features, building a dataset of 9000 interaction graphs using generators of different types of synthetic social networks. Independently of the topology of the network, we maintain constant the total volume of social interactions in our simulations, to show how even with the same social contact some network structures are more or less resilient to the spread. We find a suitable intervention to be specific suppression of unfamiliar and casual interactions that contribute to the network's global efficiency. This is, pathogen spread is significantly reduced by limiting specific kinds of contact rather than their global number. Our numerical studies might inspire further investigation in connection to public health, as an integrative framework to craft and evaluate social interventions in communicable diseases with different social graphs or as a highlight of network metrics that should be captured in social studies.
我们如何通过限制社交互动来最大程度地减少传染病的传播？无差别地抑制（社交互动）从长期来看是不可持续的，而且假设所有的互动都具有同等重要性。相反，已经证明社交网络内的传播取决于其拓扑结构。在本文中，我们通过模拟来理解和量化一系列拓扑网络特征对疾病传播的影响，使用不同类型的合成社交网络生成器构建了一个包含 9000 个交互图的数据集。独立于网络的拓扑结构，我们在模拟中保持社交互动的总数量不变，以展示即使在相同的社交接触下，某些网络结构对传播的抵抗力更强或更弱。我们发现一种合适的干预措施是有针对性地抑制那些对网络全局效率有贡献的不熟悉和随意的互动。也就是说，通过限制特定类型的接触而不是接触的总数，可以显著减少病原体的传播。我们的数值研究可能会激发与公共卫生相关的进一步研究，作为一种综合框架，用于在具有不同社交图的传染病中制定和评估社会干预措施，或者作为应该在社交研究中捕捉的网络指标的重点。
