Management Science &Entrepreneurship, Essex Business School, University of Essex, Southend-on-Sea, UK.
Department of Engineering Mathematics, University of Bristol, Bristol, UK.
Sci Rep. 2017 Feb 10;7:42352. doi: 10.1038/srep42352.
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.
静态网络上的传染病研究揭示了网络拓扑特征对疾病流行的重要影响，例如度分布的方差，即节点邻居数量的分布，以及最大度数。在这里，我们分析了一个自适应网络，其中度分布不是独立于传染病的，而是通过疾病引起的动力学和死亡率的复杂相互作用而形成的。我们研究了根据优先连接规则增长的网络的动力学，同时由于疾病引起的死亡率，节点从网络中被删除。我们使用基于个体的模拟和异质节点近似来研究疾病的流行。我们的结果表明，在这个系统中，在热力学极限下不存在传染病阈值，而网络增长和传染病传播之间的相互作用导致在疾病持续存在时，任何有限的传染性率都会导致指数网络。
