Scalable Estimation of Epidemic Thresholds via Node Sampling.
Scalable Estimation of Epidemic Thresholds via Node Sampling.
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DOI：
10.1007/s13171-021-00249-0
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sengupta S
中科院分区：
文献类型：
--
作者：
Dasgupta A;Sengupta S
传染性疾病可以通过社交网络从一个人传播到另一个人。在当今相互关联的全球社会中，这种传染过程可能造成全球公共卫生危害，例如正在进行的Covid-19大流行。因此，从统计推断的角度研究传染病的网络传播具有重要的现实意义。网络上传染过程的一个重要且被广泛研究的边界条件是所谓的流行阈值。流行病阈值在确定引入社会接触网络的病原体是否会引起流行病或消亡方面起着关键作用。本文从统计网络推理的角度对流行病阈值进行了研究。我们确定了流行病阈值的高计算和采样复杂性引起的两个主要挑战。在Chung-Lu模型框架下，我们开发了两种统计准确和计算高效的近似技术来解决这些问题。第二种近似是基于随机游走抽样的，它的优势在于需要的数据在越来越少的节点上。我们为这两种方法建立了理论保证，并论证了它们的经验优越性。
Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today’s interconnected global society, such contagion processes can cause global public health hazards, as exemplified by the ongoing Covid-19 pandemic. It is therefore of great practical relevance to investigate the network transmission of contagious diseases from the perspective of statistical inference. An important and widely studied boundary condition for contagion processes over networks is the so-called epidemic threshold. The epidemic threshold plays a key role in determining whether a pathogen introduced into a social contact network will cause an epidemic or die out. In this paper, we investigate epidemic thresholds from the perspective of statistical network inference. We identify two major challenges that are caused by high computational and sampling complexity of the epidemic threshold. We develop two statistically accurate and computationally efficient approximation techniques to address these issues under the Chung-Lu modeling framework. The second approximation, which is based on random walk sampling, further enjoys the advantage of requiring data on a vanishingly small fraction of nodes. We establish theoretical guarantees for both methods and demonstrate their empirical superiority.
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