On the effect of the path length and transitivity of small-world networks on epidemic dynamics

TL;DR

This paper presents algorithms to systematically analyze how the topological features of small-world networks, specifically path length and transitivity, influence epidemic dynamics, aiding in understanding and controlling disease spread.

Contribution

It introduces a framework for tuning network properties like clustering and path length to study their effects on epidemic models.

Findings

Transitivity and path length significantly affect epidemic spread.

Framework enables systematic exploration of network effects on epidemics.

Potential to inform better contagion control strategies.

Abstract

We show how one can trace in a systematic way the coarse-grained solutions of individual-based stochastic epidemic models evolving on heterogeneous complex networks with respect to their topological characteristics. In particular, we have developed algorithms that allow the tuning of the transitivity (clustering coefficient) and the average mean-path length allowing the investigation of the "pure" impacts of the two characteristics on the emergent behavior of detailed epidemic models. The framework could be used to shed more light into the influence of weak and strong social ties on epidemic spread within small-world network structures, and ultimately to provide novel systematic computational modeling and exploration of better contagion control strategies.