ShareTrace: an iterative message passing algorithm for efficient and effective disease risk assessment on an interaction graph

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buir.contributor.authorAyday, Erman
dc.citation.epage6en_US
dc.citation.spage1en_US
dc.contributor.authorAyday, Erman
dc.contributor.authorYoo, Y.
dc.contributor.authorHalimi, A.
dc.coverage.spatialNew York, NY, United Statesen_US
dc.date.accessioned2022-02-09T10:00:28Z
dc.date.available2022-02-09T10:00:28Z
dc.date.issued2021-08-01
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference Name: BCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informaticsen_US
dc.descriptionDate of Conference: 1- 4 August 2021en_US
dc.description.abstractWe propose a novel privacy-preserving COVID-19 risk assessment algorithm that can make a fundamental contribution to the development of the next generation resilient public health and health care systems. The proposed algorithm, ShareTrace, uses a hyperlocal interaction graph to capture direct and indirect physical interactions among users. Combining user-reported symptoms that are propagated through the hyperlocal interaction graph via a novel message passing algorithm, ShareTrace is able to pick up early warning signals based on the combination of interactions with others and symptoms. The proposed algorithm is inspired by the belief propagation algorithm and iterative decoding of low-density parity-check codes over factor graphs. Our evaluation on synthetic data shows the efficiency and efficacy of the proposed solution.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-02-09T10:00:28Z No. of bitstreams: 1 ShareTrace_an_iterative_message_passing_algorithm_for_efficient_and_effective_disease_risk_assessment_on_an_interaction_graph.pdf: 1213839 bytes, checksum: c6db2fe925f49473766edf61a7e518d4 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-09T10:00:28Z (GMT). No. of bitstreams: 1 ShareTrace_an_iterative_message_passing_algorithm_for_efficient_and_effective_disease_risk_assessment_on_an_interaction_graph.pdf: 1213839 bytes, checksum: c6db2fe925f49473766edf61a7e518d4 (MD5) Previous issue date: 2021-08-01en
dc.identifier.isbn978-145038450-6
dc.identifier.urihttp://hdl.handle.net/11693/77160
dc.language.isoEnglishen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionof10.1145/3459930.3469553en_US
dc.source.titleBCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informaticsen_US
dc.subjectCOVID-19en_US
dc.subjectDigital contact tracingen_US
dc.subjectBelief-propagation algorithmen_US
dc.subjectPrivacyen_US
dc.subjectHyperlocal interaction graphen_US
dc.titleShareTrace: an iterative message passing algorithm for efficient and effective disease risk assessment on an interaction graphen_US
dc.typeConference Paperen_US

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