Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018
Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018
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复杂网络及其应用 VII - 第 1 卷论文集第七届复杂网络及其应用国际会议 COMPLEX NETWORKS 2018
DOI：
10.1007/978-3-030-05411-3_31
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Bishop A
中科院分区：
文献类型：
--
作者：
Bishop A
现实的人类接触网络能够传播传染病，例如在社会接触调查中的研究，表现出显着的程度异质性和聚类，这两者都极大地影响了流行病的动态。为了理解这两个网络属性对流行病动力学的联合影响，Lindquist等人的有效度模型。[28]用一个新的矩闭包重新表述，以适用于高度聚集的网络。针对SIR（易感-感染-移除）流行病动力学进行了一项模拟研究，比较了替代ODE模型和随机模拟，包括对[40]中的固定误差行为的测试，证明这种新模型可以比现有方法更准确地近似复杂网络上的流行病动力学。
Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. [28] is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible–Infected–Removed) epidemic dynamics, including a test for the conjectured error behaviour in [40], providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.