Networked Infectiousness: Cascades, Power Laws, and Kinetics

TL;DR

This paper reveals a universal scale-invariant pattern of infectiousness in networked epidemic models, characterized by cascading power-law distributions, which has implications for understanding disease spread and designing control strategies.

Contribution

It uncovers a self-organized power-law distribution of infectiousness in networked SIR models, confirmed through simulations and stochastic modeling, highlighting a universal feature of epidemic dynamics.

Findings

Infection strength follows a cascading power-law distribution.

The scale-invariant feature is confirmed with stochastic modeling.

This pattern is expected to be universally significant in epidemic evolution.

Abstract

Networked SIR models have become essential workhorses in the modeling of epidemics, their inception, propagation and control. Here, and building on this venerable tradition, we report on the emergence of a remarkable self-organization of infectiousness in the wake of a propagating disease front. It manifests as a cascading power-law distribution of disease strength in networked SIR simulations, and is then confirmed with suitably defined kinetics, then stochastic modeling of surveillance data. Given the success of the networked SIR models which brought it to light, we expect this scale-invariant feature to be of universal significance, characterizing the evolution of disease within and across transportation networks, informing the design of control strategies, and providing a litmus test for the soundness of disease propagation models.