Towards a characterization of behavior-disease models.
Towards a characterization of behavior-disease models.
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DOI：
10.1371/journal.pone.0023084
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Vespignani A
中科院分区：
文献类型：
--
作者：
Perra N;Balcan D;Gonçalves B;Vespignani A
在过去十年中，出现了越来越现实的流行病模型，这些模型利用了非常详细的人口普查和人口流动数据。数据驱动模型的目标是细化到家庭或单个个人的水平。然而，相对较少的系统性工作已经完成，以提供耦合的行为-疾病模型，能够关闭的反馈回路之间的行为变化引发的人口由个人的感知疾病传播和实际的疾病传播本身。虽然缺乏这种耦合的模型在温和的流行病中可能非常成功，但在人们对疾病的了解导致社会混乱或行为改变的情况下，它们显然用处有限。在这里，我们提出了一个表征的一组原型机制，自我发起的社会距离诱导的本地和非本地的流行为基础的信息提供给个人的人口。我们的特点，这些机制的影响，在一个房室方案，扩大了基本的SIR模型的框架内考虑单独的行为类的人口。个体进入/退出行为类别的转变与疾病的传播相结合，并提供了具有多个流行高峰和临界点的丰富相空间。这里提出的模型类可以用于数据驱动的计算方法的情况下，分析社会适应和行为变化的场景。
The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.
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DOI：
10.1073/pnas.0906910106
发表时间：
2009-12-22
影响因子：
11.1
作者：
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
4.3
作者：
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通讯作者：
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DOI：
10.1073/pnas.0810762106
发表时间：
2009-04-21
影响因子：
11.1
作者：
Funk, Sebastian;Gilad, Erez;Jansen, Vincent A. A.
通讯作者：
Jansen, Vincent A. A.
影响因子：
4.3
作者：
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Codeço CT
DOI：
10.1088/1742-5468/2006/08/p08008
发表时间：
2006-08-01
影响因子：
2.4
作者：
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通讯作者：
Torcini, A.