Infection fronts in randomly varying transmission-rate media

TL;DR

This study numerically analyzes how spatially correlated random transmission rates affect infection front geometry and dynamics in a 2D SIR model, revealing universal features and implications for vaccination strategies.

Contribution

It demonstrates that randomness significantly influences infection front properties and introduces universal geometric features, challenging mean-field predictions and linking to KPZ universality.

Findings

Critical transmission rate is overestimated by mean-field homogenization.

Front harmfulness decreases with more uniform transmission rates.

Universal roughness and dynamical exponents consistent with KPZ class.

Abstract

We numerically investigate the geometry and transport properties of infection fronts within the spatial SIR model in two dimensions. The model incorporates short-range correlated quenched random transmission rates. Our findings reveal that the critical average transmission rate for the steady-state propagation of the infection is overestimated by the naive mean-field homogenization. Furthermore, we observe that the velocity, profile, and harmfulness of the fronts, given a specific average transmission, are sensitive to the details of randomness. In particular, we find that the harmfulness of the front is larger the more uniform the transmission-rate is, suggesting potential optimization in vaccination strategies under constraints like fixed average-transmission-rates or limited vaccine resources. The large-scale geometry of the advancing fronts presents nevertheless robust universal features and, for a statistically isotropic and short-range correlated disorder, we get a roughness exponent $\alpha\approx 0.42 \pm 0.10$ and a dynamical exponent $z\approx 1.6 \pm 0.10$, which are roughly compatible with the one-dimensional Kardar-Parisi-Zhang (KPZ) universality class. We find that the KPZ term and the disorder-induced effective noise are present and have a kinematic origin.