Epidemic spreading in modular time-varying networks

TL;DR

This paper explores how modular and time-varying connectivity patterns influence epidemic spreading, revealing that clusters can inhibit or accelerate disease spread depending on the model, with implications for understanding real-world networks.

Contribution

It introduces an analytically characterized model of time-varying networks with tunable modularity and studies its impact on epidemic thresholds and sizes for SIR and SIS models.

Findings

Tightly connected clusters inhibit SIR epidemic spread.

Modular structures lower the epidemic threshold in SIS models.

Heterogeneous temporal patterns and modularity accelerate SIS epidemics.

Abstract

We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibit SIR processes, it speeds up SIS diseases. In this case, we observe that heterogeneous temporal connectivity patterns and modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal network.