Infection Percolation: A Dynamic Network Model of Disease Spreading

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