A simplicial epidemic model for COVID-19 spread analysis

Yuzhou Chen, Yulia R. Gel, Madhav V. Marathe, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article numbere2313171120
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number1
StatePublished - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


  • COVID-19
  • digital twin
  • forecasting disease dynamics
  • synthetic social contact network


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