Evidence map›Paper›PMID 28884277›Full record

ArticleJournal of mathematical biology2018

Global stability for epidemic models on multiplex networks.

Yu-Jhe Huang, Jonq Juang, Yu-Hao Liang, Hsin-Yu Wang

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Article in Journal of mathematical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yu-Jhe HuangDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu, Taiwan.
Jonq JuangDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu, Taiwan. jjuang@math.nctu.edu.tw.ORCID 0000-0001-9563-8143
Yu-Hao LiangDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu, Taiwan.
Hsin-Yu WangDepartment of Applied Mathematics, National Chiao Tung University, Hsinchu, Taiwan.

Funding

Ministry of Science and Technology, Taiwan 105-2115-M-009 -002 -MY2
6 · The paper itself

Abstract

In this work, we consider an epidemic model in a two-layer network in which the dynamics of susceptible-infected-susceptible process in the physical layer coexists with that of a cyclic process of unaware-aware-unaware in the virtual layer. For such multiplex network, we shall define the basic reproduction number [Formula: see text] in the virtual layer, which is similar to the basic reproduction number [Formula: see text] defined in the physical layer. We show analytically that if [Formula: see text] and [Formula: see text], then the disease and information free equilibrium is globally stable and if [Formula: see text] and [Formula: see text], then the disease free and information saturated equilibrium is globally stable for all initial conditions except at the origin. In the case of [Formula: see text], whether the disease dies out or not depends on the competition between how well the information is transmitted in the virtual layer and how contagious the disease is in the physical layer. In particular, it is numerically demonstrated that if the difference in [Formula: see text] and [Formula: see text] is greater than the product of [Formula: see text], the deviation of [Formula: see text] from 1 and the relative infection rate for an aware susceptible individual, then the disease dies out. Otherwise, the disease breaks out.

Indexed as

Models, BiologicalBasic Reproduction NumberCommunicable DiseasesComputational BiologyComputer SimulationDisease SusceptibilityEpidemicsHumansMarkov ChainsMathematical ConceptsAwarenessEpidemic modelsGlobal stabilityMultiplex networksThe basic reproduction number

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.