论文标题
随机网络上的自我适应传染性动态
Self-adapting infectious dynamics on random networks
论文作者
论文摘要
自适应动态发生在许多物理系统中，例如社会经济，神经科学或生物物理学。我们正式化了一种自适应建模方法，该方法是根据系统状态的一组策略进行适应的。这会导致分段确定性的马尔可维亚动力学与非马克维亚自适应机制结合。我们将此框架应用于随机网络上的基本流行模型（SIS，SIR）。我们考虑了一个共同进化的动力网络，其中节点态通过流行病和网络拓扑而变化，而通过创建和删除边缘变化。对于锁定措施的简单阈值基础应用，我们观察到具有振荡行为的参数空间中的大区域。对于SIS流行模型，我们从成对封闭模型中得出振荡期的分析表达式。此外，我们表明，随着基本繁殖数量在一个左右波动时，与自组织的批判性有联系。我们还研究了结果对基础网络结构的依赖性。
Self-adaptive dynamics occurs in many physical systems such as socio-economics, neuroscience, or biophysics. We formalize a self-adaptive modeling approach, where adaptation takes place within a set of strategies based on the history of the state of the system. This leads to piecewise deterministic Markovian dynamics coupled to a non-Markovian adaptive mechanism. We apply this framework to basic epidemic models (SIS, SIR) on random networks. We consider a co-evolutionary dynamical network where node-states change through the epidemics and network topology changes through creation and deletion of edges. For a simple threshold base application of lockdown measures we observe large regions in parameter space with oscillatory behavior. For the SIS epidemic model, we derive analytic expressions for the oscillation period from a pairwise closed model. Furthermore, we show that there is a link to self-organized criticality as the basic reproduction number fluctuates around one. We also study the dependence of our results on the underlying network structure.
