摘要翻译：
我们在一个简单的流行病传播模型中研究了风险感知的影响。我们假设被感染风险的感知取决于患病邻居的比例。这一因素的作用是降低传染性，因此成为模型的一个动态组成部分。我们在平均场近似下研究了该问题，并对正则、随机和无标度网络进行了数值模拟。我们表明，对于同质和随机网络，总有一个感知值阻止流行病。在“最坏情况”下，无标度网络具有发散的输入连接，线性感知无法阻止流行病；然而，我们表明，感知风险的非线性增加可能导致疾病的灭绝。这种转变是不连续的，并不是由平均场分析预测的。
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英文标题：
《Risk perception in epidemic modeling》
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作者：
Franco Bagnoli, Pietro Lio, Luca Sguanci
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  We investigate the effects of risk perception in a simple model of epidemic spreading. We assume that the perception of the risk of being infected depends on the fraction of neighbors that are ill. The effect of this factor is to decrease the infectivity, that therefore becomes a dynamical component of the model. We study the problem in the mean-field approximation and by numerical simulations for regular, random and scale-free networks.   We show that for homogeneous and random networks, there is always a value of perception that stops the epidemics. In the ``worst-case'' scenario of a scale-free network with diverging input connectivity, a linear perception cannot stop the epidemics; however we show that a non-linear increase of the perception risk may lead to the extinction of the disease. This transition is discontinuous, and is not predicted by the mean-field analysis.
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PDF链接：
https://arxiv.org/pdf/0705.1974