Infection percolation: A dynamic network model of disease spreading

TL;DR

This paper introduces a dynamic network model for disease spreading that captures individual variability and spatiotemporal infection history, revealing wave-like propagation and effects of spatial heterogeneity.

Contribution

It presents a novel dynamic network approach that unifies previous models, incorporating individual-level transmission and spatial heterogeneity for diverse diseases.

Findings

Disease spreads as a traveling wave of infection and recovery.

Spatial heterogeneity can either amplify or suppress disease spread.

A scaling theory predicts infection dynamics across different scenarios.

Abstract

Models of disease spreading are critical for predicting infection growth in a population and evaluating public health policies. However, standard models typically represent the dynamics of disease transmission between individuals using macroscopic parameters that do not accurately represent person-to-person variability. To address this issue, we present a dynamic network model that provides a straightforward way to incorporate both disease transmission dynamics at the individual scale as well as the full spatiotemporal history of infection at the population scale. We find that disease spreads through a social network as a traveling wave of infection, followed by a traveling wave of recovery, with the onset and dynamics of spreading determined by the interplay between disease transmission and recovery. We use these insights to develop a scaling theory that predicts the dynamics of infection for diverse diseases and populations. Furthermore, we show how spatial heterogeneities in susceptibility to infection can either exacerbate or quell the spread of disease, depending on its infectivity. Ultimately, our dynamic network approach provides a simple way to model disease spreading that unifies previous findings and can be generalized to diverse diseases, containment strategies, seasonal conditions, and community structures.