Coupled disease-behavior dynamics on complex networks: A review.
Coupled disease-behavior dynamics on complex networks: A review.
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复杂网络上的耦合疾病与行为动力学：综述
DOI：
10.1016/j.plrev.2015.07.006
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发表时间：
2015-12
影响因子：
11.7
通讯作者：
Bauch CT
中科院分区：
文献类型：
--
作者：
Wang Z;Andrews MA;Wu ZX;Wang L;Bauch CT
人们日益认识到，成功的感染控制工作的一个关键组成部分是了解疾病动态与人类行为和社会动态之间复杂的双向相互作用。接触预防措施和社会距离等人类行为明显影响疾病流行，但疾病流行反过来又可以改变人类行为，形成一个耦合的非线性系统。此外，在许多情况下，人口的空间结构不能被忽视，因此，社会和行为过程和/或感染的传播必须用复杂的网络来表示。特别是对复杂网络中耦合疾病行为动力学的研究正在迅速发展，并且经常使用统计物理的分析方法和概念。在这里，我们回顾了一些在这一领域不断增长的文献。我们将基于网络的方法与同质混合方法进行了对比，指出它们的预测有何不同，并描述了复杂网络中丰富且经常令人惊讶的疾病-行为动力学行为，并将它们与统计物理过程进行了比较。我们讨论了这些模型如何能够捕捉到许多现实世界情景的动态特征，从而为政策制定者更好地设计有效的预防策略提供了建议。我们还描述了促进这一领域研究的日益增长的数字数据来源。最后，我们提出了该领域研究人员可能面临的陷阱，并提出了该领域在未来几年向前发展的几种方法。我们系统地调查了这种行为如何在混合良好和网络化的人群中影响疾病的传播和预防。疾病-行为耦合动力学与进化或经济规律密切相关，进而影响模式的形成。理论预测通过数字数据或实验得到实证验证。许多新的发现或现象需要统计物理方法的支持。
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. We systematically survey how the behavior affects disease spreading and prevention in well-mixed and networked populations. The coupled disease–behavior dynamics is closely related with evolution or economic rules, and further influences the pattern formation. Theoretical prediction gets the empirical validation via digital data or experiments. Many novel findings or phenomena need the support of methods in statistical physics.
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影响因子：
9.8
作者：
BRITO, DL;SHESHINSKI, E;INTRILIGATOR, MD
通讯作者：
INTRILIGATOR, MD
DOI：
10.1126/science.1244492
发表时间：
2013-10-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bauch CT;Galvani AP
通讯作者：
Galvani AP
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
DOI：
10.1145/1284680.1284681
发表时间：
2008-01-01
影响因子：
--
作者：
Chakrabarti, Deepayan;Wang, Yang;Faloutsos, Christos
通讯作者：
Faloutsos, Christos
影响因子：
3.5
作者：
Auld, MC
通讯作者：
Auld, MC