Scalable estimation of epidemic thresholds via node sampling

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dc.contributor.author Dasgupta, Anirban
dc.contributor.author Sengupta, Srijan
dc.coverage.spatial Singapore
dc.date.accessioned 2012-09-26T07:22:31Z
dc.date.available 2012-09-26T07:22:31Z
dc.date.issued 2021-06
dc.identifier.citation Dasgupta, Anirban and Sengupta, Srijan, "Scalable estimation of epidemic thresholds via node sampling", Sankhya A, DOI: 10.1007/s13171-021-00249-0, Jul. 2021. en_US
dc.identifier.issn 0972-7671
dc.identifier.uri https://doi.org/10.1007/s13171-021-00249-0
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/6642
dc.description.abstract 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.
dc.description.statementofresponsibility by Anirban Dasgupta and Srijan Sengupta
dc.language.iso en_US en_US
dc.publisher Springer Nature en_US
dc.subject Epidemic threshold en_US
dc.subject Networks en_US
dc.subject Sampling en_US
dc.subject Random walk en_US
dc.subject Configuration model en_US
dc.subject Epidemiology en_US
dc.title Scalable estimation of epidemic thresholds via node sampling en_US
dc.type Article en_US
dc.relation.journal Sankhya A


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