Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach

Bowen Wang, Yanjing Sun, Trung Q Duong, Long D Nguyen, Lajos Hanzo

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
3 Downloads (Pure)

Abstract

The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases. [Abstract copyright: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.]
Original languageEnglish
Pages (from-to)115655-115661
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 19 Jun 2020

Keywords

  • COVID-19
  • Social Internet of Thing (SIoT)
  • graph theory
  • reinforcement learning

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