Synergistic epidemic spreading in correlated networks
Synergistic epidemic spreading in correlated networks
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相关网络中的协同流行病传播
DOI：
10.1103/physreve.106.034305
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发表时间：
2022
影响因子：
2.4
通讯作者：
Hasegawa Takehisa
中科院分区：
文献类型：
--
作者：
Mizutaka Shogo;Mori Kizashi;Hasegawa Takehisa
研究了传染病传播中具有非线性协同效应的易感-感染-易感（SIS）模型的度相关效应。在一个平均场处理的协同SIS模型的双峰网络与可调度相关性，我们确定了一个不连续的过渡，是独立的度相关强度，除非协同作用是不存在的或非常弱。无论协同作用（不存在或存在），在模型中的正和负的程度相关性降低和提高的流行阈值，分别。对于具有强正度相关性的网络，平均场处理预测了稳态感染密度的两个不连续跳跃的出现。为了测试平均场处理，我们提供了本模型的近似主方程。我们定量地证实，近似主方程同意不仅所有的定性预测的平均场治疗，但也相应的蒙特卡罗模拟。
We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect (synergy) in infectious transmissions. In a mean-field treatment of the synergistic SIS model on a bimodal network with tunable degree correlation, we identify a discontinuous transition that is independent of the degree correlation strength unless the synergy is absent or extremely weak. Regardless of synergy (absent or present), a positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the mean-field treatment predicts the emergence of two discontinuous jumps in the steady-state infected density. To test the mean-field treatment, we provide approximate master equations of the present model. We quantitatively confirm that the approximate master equations agree with not only all qualitative predictions of the mean-field treatment but also corresponding Monte Carlo simulations.