Networked SIS Epidemics With Awareness
Networked SIS Epidemics With Awareness
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DOI：
10.1109/tcss.2017.2719585
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发表时间：
2017-09-01
影响因子：
5
通讯作者：
Shamma, Jeff S.
中科院分区：
文献类型：
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作者：
Paarporn, Keith;Eksin, Ceyhun;Shamma, Jeff S.
我们通过静态接触网络研究易感者-感染者-易感者的流行病过程，其中节点拥有有关流行病状态的部分信息。当他们认为这种流行病目前正在流行时，他们的反应是限制与邻居的互动。节点的认知度由其社交网络中受感染邻居的比例以及整个网络中受感染节点比例的全局广播来加权。基准（无意识）和意识模型的动态由离散时间马尔可夫链描述，从中导出平均场近似（MFA）。 MFA 的状态被解释为节点被感染的概率。我们展示了意识模型的“亚稳态”或地方性状态与基准模型的状态一致的充分条件。此外，我们使用耦合技术对两条链进行完全随机比较分析，作为 MFA 分析的概率模拟。特别是，我们表明，增加意识会降低对样本路径空间上任何流行病指标的期望，例如根除时间或总感染率。我们用耦合分布来描述期望的减少。在模拟中，我们评估了社交距离对不同随机图族（几何、Erdos-Renyi 和无标度随机网络）的接触网络的影响。
We study a susceptible-infected-susceptible epidemic process over a static contact network where the nodes have partial information about the epidemic state. They react by limiting their interactions with their neighbors when they believe the epidemic is currently prevalent. A node's awareness is weighted by the fraction of infected neighbors in their social network, and a global broadcast of the fraction of infected nodes in the entire network. The dynamics of the benchmark (no awareness) and awareness models are described by discrete-time Markov chains, from which mean-field approximations (MFAs) are derived. The states of the MFA are interpreted as the nodes' probabilities of being infected. We show a sufficient condition for the existence of a "metastable," or endemic, state of the awareness model coincides with that of the benchmark model. Furthermore, we use a coupling technique to give a full stochastic comparison analysis between the two chains, which serves as a probabilistic analog to the MFA analysis. In particular, we show that adding awareness reduces the expectation of any epidemic metric on the space of sample paths, e.g., eradication time or total infections. We characterize the reduction in expectations in terms of the coupling distribution. In simulations, we evaluate the effect social distancing has on contact networks from different random graph families (geometric, Erdos-Renyi, and scale-free random networks).