icon: Fast Simulation of Epidemics on Coevolving Networks

TL;DR

This paper presents a fast, rejection-based simulation method for modeling epidemic spread on adaptive networks, capturing co-evolution of network structure and disease dynamics more efficiently than existing methods.

Contribution

It introduces a novel, efficient simulation algorithm for co-evolving epidemic networks, extending the SIS model with stochastic rules for node association and dissociation.

Findings

Outperforms standard simulation baselines in speed

Reveals new emergent epidemic patterns

Provides open-source code for implementation

Abstract

We introduce a fast simulation technique for modeling epidemics on adaptive networks. Our rejection-based algorithm efficiently simulates the co-evolution of the network structure and the epidemic dynamics. We extend the classical SIS model by incorporating stochastic rules that allow for the association of susceptible nodes and the dissociation of infected nodes. The method outperforms standard baselines in terms of computational efficiency while revealing new emergent patterns in epidemic spread. Code is made available at github.com/GerritGr/icon.