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Eugene Tan¹, Michael Small², Shannon D Algar²

¹The Kids Research Institute Australia, Nedlands 6009, Australia.

Physical Review. E

|February 20, 2026

Summary

This summary is machine-generated.

Disease spreading models can mimic complex network dynamics by adjusting parameters in simpler models. This research unifies compartmental and simplicial contagion models, showing pairwise interactions can replicate higher-order effects.

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Area of Science:

Epidemiology
Network Science
Computational Modeling

Background:

Compartmental models like SIS/SIR are standard for epidemic and information diffusion studies.
Network structures enhance realism by simulating pairwise interactions.
Simplicial contagion models (SCM) use hypergraphs for higher-order interactions (groups > 2).

Purpose of the Study:

To unify classical compartmental models with simplicial contagion models.
To extend models to arbitrary-order hyperedge interactions.
To investigate if higher-order interactions yield fundamentally different dynamics than pairwise interactions.

Main Methods:

Developed an agent-based model unifying SIS/SIR and SCM.
Extended the model for interactions on hyperedges of any order.
Demonstrated replication of higher-order dynamics using scaled pairwise interactions.

Main Results:

Steady-state dynamics of pairwise interactions can replicate higher-order simulations via scaled parameters.
Dynamically varying disease parameters allow pairwise models to mimic transient and steady-state higher-order dynamics.
The approximation is robust to model misspecification and applies to complex, heterogeneous hypergraphs.

Conclusions:

Pairwise interaction models can approximate higher-order contagion dynamics through parameter scaling.
Higher-order network topologies can be effectively represented by modified lower-order models.
Hypergraph topology heterogeneities can impact the accuracy of pairwise approximations.