Consistent Approximation of Epidemic Dynamics on Degree-heterogeneous Clustered Networks

TL;DR

This paper introduces a new moment closure model for epidemic dynamics on highly clustered, degree-heterogeneous networks, demonstrating improved accuracy over existing models through simulation comparisons.

Contribution

It reformulates the effective degree model with a novel moment closure to better approximate epidemic spread on complex networks with clustering.

Findings

The new model outperforms existing approaches in accuracy.

Simulation results support the conjectured error behavior.

The model effectively captures joint effects of degree heterogeneity and clustering.

Abstract

Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the conjectured error behaviour in Pellis et al., providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.