Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases
Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases
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模拟人类行为与传染病传播之间的相互作用
DOI：
10.1007/978-1-4614-5474-8_8
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Kiss I
中科院分区：
文献类型：
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作者：
Kiss I
在过去的几年里，已经看到了一套扩展的流行病模型，考虑到“活跃”的性质，个人和/或人口的发展。许多模型都是从一个自然的前提出发的，即个人不是“被动的”，而是相反地接收和处理关于潜在或正在发生的流行病的信息。因此，风险认知和行为改变在形成和改变流行病的结果方面发挥着重要作用。将这些方面纳入经典的流行病模型提出了许多挑战。首先，关于信息是如何产生的、在当地和全球的可得性、传播途径以及“旧”信息的收益递减，存在着许多悬而未决的问题。所有这些因素导致一个显着扩展的状态空间，与标准的流行病模型相比，有更多的变量和参数。因此，除了衡量和量化信息驱动的风险认知和/或行为变化的问题外，建模的一个主要挑战是模型的复杂性。更确切地说，如何在模型准确性和易处理性之间实现最佳平衡。在本章中，从考虑流行病和信息同时传播的成对模型开始，通过以下方式讨论建模复杂性和结果：（1）评估各种信息产生和传递机制的有效性，然后（2）将成对模型解构为更简单的变体，以及（3）讨论具体的建模替代方案（即，动态网络的成对和有效度模型）以及人类行为和疾病传播耦合模型领域未来可能的建模趋势。
The past few years have seen the development of a suite of extended epidemic models that take into account the “active” nature of individuals and/or population. Many models start from the natural premise that individuals are not “passive” but, on the contrary, receive and process information about potential or ongoing epidemics. Therefore, risk perception and behaviour change play a major role in shaping and changing the outcome of an epidemic. Incorporating such aspects into classical epidemic models poses many challenges. First of all, there are many open questions about how information is generated, its availability locally and globally, its routes of dissemination and diminishing returns of “old” information. All these factors lead to a significantly extended state space with many more variables and parameters compared to standard epidemic models. Thus, apart from issues around measuring and quantifying risk perception and/or behaviour change driven by information, a major modelling challenge revolves around model complexity. More precisely, how to achieve an optimal balance between model accuracy and tractability. In thischapter, starting from a pairwise model that accounts for the concurrent spread of an epidemic and information, modelling complexity and results are discussed by (1) evaluating the effectiveness of various information generating and transmitting mechanisms followed by (2) the deconstruction of the pairwise model to a simpler variant and by (3) discussing concrete modelling alternatives (i.e., pairwise and effective degree models for dynamic networks) and potential future modelling trends in the area of coupled models of human behaviour and disease transmission.