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Double-Edged Sword Effect of Social-Support on Epidemic Spreading on Correlated Multiplex Networks

机译:社会支持对相关多重网络上流行病传播的双刃剑效应

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Social support is an essential part in suppressing the outbreak of serious infectious diseases. With the development of network science, how social support affects the dynamics of disease spreading on complex networks has now been more and more attracting and important topics. In this paper, we propose an epidemic model that considers the resource support from social neighbors of infected individuals based on social-contact multiplex networks. A bias parameter is introduced in the model to regulate the resource contribution strategy. Through extensive simulations, we find on both uncorrelated and correlated multiplex networks, there is always an optimal resource contribution strategy that can suppress the disease spreading to the maximum extent. Interestingly, the strategies of social support on social subnetwork has a so called double-edged sword effect on the dynamics of epidemic spreading. When disease transmission rate is relatively small, the nodes with small degrees in the social subnetwork should contributed more resources to suppress the disease spreading. While when transmission rate is large, the nodes with large degrees in the subnetworks should contributed more resources. When considering the inter-layer degree correlated, there is double edged sword effect of inter-layer degree correlation on dynamics of epidemic spreading.
机译:社会支持是制止严重传染病暴发的重要组成部分。随着网络科学的发展,社会支持如何影响复杂网络上疾病传播的动态成为越来越引人入胜的重要话题。在本文中,我们提出了一种流行模型,该模型考虑了基于社会接触多路复用网络的受感染个体的社会邻居的资源支持。在模型中引入了一个偏差参数来调节资源贡献策略。通过广泛的仿真,我们发现在不相关和相关的多重网络上,总有一种最优的资源贡献策略可以最大程度地抑制疾病的传播。有趣的是,社会子网络上的社会支持策略对流行传播的动力学具有所谓的双刃剑效应。当疾病传播率相对较小时,社交子网中度数较小的节点应贡献更多资源来抑制疾病传播。当传输速率较大时,子网中度数较大的节点应该贡献更多的资源。当考虑层间关联度时,层间关联度对流行扩散的动力学具有双刃剑效应。

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