The resurgence of measles is largely attributed to the decline in vaccine adoption and the increase in mobility. Although the vaccine for measles is readily available and highly successful, its current adoption is not adequate to prevent epidemics. Vaccine adoption is directly affected by individual vaccination decisions, and has a complex interplay with the spatial spread of disease shaped by an underlying mobility (travelling) network. In this paper, we model the travelling connectivity as a scale-free network, and investigate dependencies between the network’s assortativity and the resultant epidemic and vaccination dynamics. In doing so we extend an SIR-network model with game-theoretic components, capturing the imitation dynamics under a voluntary vaccination scheme. Our results show a correlation between the epidemic dynamics and the network’s assortativity, highlighting that networks with high assortativity tend to suppress epidemics under certain conditions. In highly assortative networks, the suppression is sustained producing an early convergence to equilibrium. In highly disassortative networks, however, the suppression effect diminishes over time due to scattering of non-vaccinating nodes, and frequent switching between the predominantly vaccinating and non-vaccinating phases of the dynamics.

麻疹的复兴主要归因于疫苗采用率的下降和流动性的增加。尽管麻疹疫苗很容易获得并且非常成功，但是目前采用它不足以预防流行病。疫苗的采用直接受到个体疫苗接种决定的影响，并且与由潜在移动性（旅行）网络塑造的疾病的空间传播具有复杂的相互作用。在本文中，我们将旅行连通性建模为无标度网络，并研究了网络的分类性与由此产生的流行病和疫苗接种动态之间的依赖性。为此，我们扩展了具有博弈论组成部分的SIR网络模型，在自愿接种计划下捕获了模仿动态。我们的结果表明，流行病动态与网络的分类性之间存在相关性，这表明具有高分类性的网络在某些条件下倾向于抑制流行病。在高度分类的网络中，抑制作用持续存在，从而尽早收敛到平衡。但是，在高度分散的网络中，由于非疫苗接种节点的分散以及动力学的主要疫苗接种阶段和非疫苗接种阶段之间的频繁切换，抑制效果会随着时间而减弱。