Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122.
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080.
Proc Natl Acad Sci U S A. 2024 Jan 2;121(1):e2313171120. doi: 10.1073/pnas.2313171120. Epub 2023 Dec 26.
Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.
网络使我们能够描述出在各种知识领域中出现的复杂系统中的广泛的相互作用现象，这些领域包括神经科学、工程学、生态学、金融学和社会科学。直到最近，网络模型和工具的主要关注点一直是描述系统实体之间的成对关系。然而，越来越多的研究表明，多个网络实体之间的多元或更高阶的群体关系可能是更好地理解复杂系统功能背后内在机制的关键。这种群体相互作用可以通过图的单纯复形以整体的方式来描述。受这些最近出现的关于复杂网络单纯复形在传染病传播中的效用的结果的启发，并利用美国弗吉尼亚州的人口的大规模合成社交接触网络（也称为数字孪生），在本文中，我们旨在深入了解高阶社交互动以及相关的变化的社会群体决定因素在 COVID-19 传播和缓解措施中的作用。
