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Higher-order networks reveal how group size affects infectious disease spread. Transmission probabilities may be constant, with variations arising from additive effects and time scales, impacting epidemic dynamics.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Infectious diseases are a major global health threat.
  • Higher-order networks model group-based transmission dynamics.
  • The influence of group size on transmission probabilities is not fully understood.

Purpose of the Study:

  • To investigate the multiscale influence of group size on epidemic dynamics.
  • To propose a multiscale epidemic model on hypergraphs incorporating two- and three-body interactions.
  • To analyze the impact of additive effects and heterogeneous temporal scales on disease spread.

Main Methods:

  • Developed a multiscale epidemic model on hypergraphs with two- and three-body interactions.
  • Unified heterogeneous temporal scales using transmission intensities.
  • Derived the basic reproduction number (R0) and performed bifurcation analysis.
  • Conducted Monte Carlo and numerical simulations.

Main Results:

  • R0 depends on pairwise and triadic transmission intensities.
  • Individual transmission shows forward bifurcation; group transmission exhibits backward bifurcation.
  • Triadic transmission intensity significantly influences R0, steady states, and solution distributions.

Conclusions:

  • Additive effects of group interactions drive multiscale epidemic dynamics.
  • Higher-order interactions, particularly triadic, are crucial for understanding disease spread.
  • Findings offer new insights into the mechanisms of infectious disease transmission in groups.