Social Clustering in Epidemic Spread on Coevolving Networks

TL;DR

This paper introduces a coevolving network model for SIS epidemic spread that emphasizes the role of social transitivity, showing how increased clustering reduces infection levels and improving predictive modeling accuracy.

Contribution

It develops a novel adaptive SIS model incorporating triangle-closing rewiring, highlighting the impact of transitivity on epidemic dynamics and providing accurate analytical predictions.

Findings

Higher transitivity leads to lower infection prevalence.

Approximate Master Equations accurately predict stationary states.

The model offers insights for epidemic control strategies.

Abstract

Even though transitivity is a central structural feature of social networks, its influence on epidemic spread on coevolving networks has remained relatively unexplored. Here we introduce and study an adaptive SIS epidemic model wherein the infection and network coevolve with non-trivial probability to close triangles during edge rewiring, leading to substantial reinforcement of network transitivity. This new model provides a unique opportunity to study the role of transitivity in altering the SIS dynamics on a coevolving network. Using numerical simulations and Approximate Master Equations (AME), we identify and examine a rich set of dynamical features in the new model. In many cases, the AME including transitivity reinforcement provides accurate predictions of stationary-state disease prevalences and network degree distributions. Furthermore, for some parameter settings, the AME accurately trace the temporal evolution of the system. We show that higher transitivity reinforcement in the model leads to lower levels of infective individuals in the population, when closing a triangle is the dominant rewiring mechanism. These methods and results may be useful in developing ideas and modeling strategies for controlling SIS type epidemics.