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A bi-virus competing spreading model with generic infection rates
journal contribution
posted on 2018-01-01, 00:00 authored by Luxing YangLuxing Yang, X Yang, Y Yan TangIEEE Due to widespread applications, the multi-virus competing spreading dynamics has recently aroused considerable interests. To our knowledge, all previous competing spreading models assume infection rates that are each linear in the virus occupancy probabilities of the individuals in a population. As linear infection rates are overestimation of real infection rates, in some situations these models cannot accurately predict the spreading process of multiple competing viruses. This work takes the first step toward enhancing the accuracy of multi-virus competing spreading models. A continuous-time bilayer-network-based bi-virus competing spreading model with generic infection rates is proposed. Criteria for the extinction of both viruses and for the survival of only one virus are presented, respectively. Numerical examples show that (1) if the generic bi-virus spreading model with linear infection rates predicts that the fraction of nodes infected with some virus would approach zero, the prediction of the fraction is accurate, and (2) if the scenario-relevant generic infection rates could be estimated accurately, the resulting model would be able to accurately forecast the evolutionary process of a pair of competing viruses.
History
Journal
IEEE transactions on network science and engineeringVolume
5Issue
1Pagination
2 - 13Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
2327-4697Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
Competing virusesmultilayer networkvirus spreading modellinear infection rategeneric infection rateequilibriumglobal stabilityglobal attractivityScience & TechnologyTechnologyPhysical SciencesEngineering, MultidisciplinaryMathematics, Interdisciplinary ApplicationsEngineeringMathematicsEPIDEMIC MODELPROPAGATIONNETWORKSDYNAMICSIMPACTDistributed Computing
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