Identifying the Superspreader in Proactive Backward Contact Tracing by Deep Learning

Siya Chen, Pei Duo Yu, Chee Wei Tan, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The goal of proactive contact tracing is to diminish the spread of an epidemic by means of contact tracing mobile apps and big data analysis. Finding superspreaders as has been used in Japan and Australia during the early days of the COVID-19 pandemic has proven effective as backward contact tracing can pick up infections that might otherwise be missed. In this paper, we formulate a proactive contact tracing problem to identify the superspreaders using maximum-likelihood estimation, graph traversal and deep learning algorithms. This problem is challenging due to its sheer combinatorial complexity, problem scale and the fact that the underlying infection network topology is rarely known. We propose a deep learning-based framework using Graph Neural Networks to iteratively refine the supervised learning of proactive contact tracing networks using smaller infection networks and to identify the superspreader. By optimizing the graph traversal and topological features for deep learning, proactive contact tracing strategies can be developed to contain superspreading in an epidemic outbreak.

Original languageEnglish (US)
Title of host publication2022 56th Annual Conference on Information Sciences and Systems, CISS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-48
Number of pages6
ISBN (Electronic)9781665417969
DOIs
StatePublished - 2022
Externally publishedYes
Event56th Annual Conference on Information Sciences and Systems, CISS 2022 - Princeton, United States
Duration: Mar 9 2022Mar 11 2022

Publication series

Name2022 56th Annual Conference on Information Sciences and Systems, CISS 2022

Conference

Conference56th Annual Conference on Information Sciences and Systems, CISS 2022
Country/TerritoryUnited States
CityPrinceton
Period3/9/223/11/22

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

Keywords

  • Deep learning
  • Digital contact tracing
  • Graph algorithms
  • Graph neural networks
  • Superspreader identification

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