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Immunity of multiplex networks via acquaintance vaccination

  • Zhen Wang*
  • , Da-Wei Zhao
  • , Lin Wang
  • , Gui-Quan Sun
  • , Zhen Jin
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

How to find the effective approach of immunizing a population is one open question in the research of complex systems. Up to now, there have been a great number of works focusing on the efficiency of various immunization strategies. However, the majority of these existing achievements are limited to isolated networks, how immunization affects disease spreading in multiplex networks seems to need further exploration. In this letter, we explore the impact of the acquaintance immunization in multiplex networks, where two kinds of immunization strategies, multiplex node-based acquaintance immunization and layer node-based acquaintance immunization, are proposed. With the generating function method, our theoretical framework is able to accurately calculate the critical immunization threshold which is one of the most important indexes to predict the epidemic regime. Moreover, we further uncover that, with the increment of degree correlation between network layers, the immunization threshold declines for multiplex node-based acquaintance immunization, but slowly increases for layer node-based acquaintance immunization. Copyright (C) EPLA, 2015
Original languageEnglish
Article numberARTN 48002
JournalEPL
Volume112
Issue number4
DOIs
Publication statusPublished - Nov 2015

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Keywords

  • COMPLEX NETWORKS
  • DYNAMICS

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