Epidemic spreading on complex networks as front propagation into an unstable state

TL;DR

This paper analyzes epidemic spread on complex networks using front propagation theory, showing how linearization, higher-order interactions, and inhomogeneities influence invasion speeds and arrival times.

Contribution

It applies front propagation concepts to network epidemic models, extending predictions to complex scenarios including higher-order interactions and inhomogeneous infection rates.

Findings

Linear approximation predicts epidemic arrival times accurately.

Higher-order interactions accelerate invasion speeds.

Inhomogeneities in infection rates lead to faster spread.

Abstract

We study epidemic arrival times in meta-population disease models through the lens of front propagation into unstable states. We demonstrate that several features of invasion fronts in the PDE context are also relevant to the network case. We show that the susceptible-infected-recovered model on a network is linearly determined in the sense that the arrival times in the nonlinear system are approximated by the arrival times of the instability in the system linearized near the disease free state. Arrival time predictions are extended to an susceptible-exposed-infected-recovered model. We then study a recent model of social epidemics where high order interactions of individuals lead to faster invasion speeds. For these pushed fronts we compute corrections to the estimated arrival time in this case. Finally, we show how inhomogeneities in local infection rates lead to faster average arrival times.