  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.
