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Area of Science:

  • Complex systems analysis
  • Network science
  • Epidemiology

Background:

  • Traditional network models focus on pairwise interactions.
  • Higher-order group interactions are increasingly recognized as crucial for understanding complex systems.
  • Simplicial complexes offer a framework to model these group interactions holistically.

Purpose of the Study:

  • To investigate the role of higher-order social interactions in COVID-19 propagation.
  • To analyze the impact of varying social group determinants on disease spread and mitigation.
  • To leverage a synthetic social contact network of Virginia's population for insights.

Main Methods:

  • Utilizing simplicial complexes to represent higher-order interactions in social networks.
  • Employing a large-scale synthetic social contact network (digital twin) of Virginia.
  • Analyzing contagion propagation dynamics within this network structure.

Main Results:

  • Higher-order interactions play a significant role in COVID-19 transmission patterns.
  • Social group structures and their determinants influence disease propagation.
  • Insights gained can inform targeted mitigation strategies.

Conclusions:

  • Simplicial geometry provides a powerful lens for understanding contagion in complex social systems.
  • Accounting for group interactions is essential for accurate modeling of infectious disease dynamics.
  • Findings support the development of more nuanced public health interventions based on social network structures.