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dc.contributor.authorMann, Peter Stephen
dc.contributor.authorSmith, V.A.
dc.contributor.authorMitchell, John B. O.
dc.contributor.authorDobson, Simon Andrew
dc.date.accessioned2022-07-27T11:30:07Z
dc.date.available2022-07-27T11:30:07Z
dc.date.issued2022-07-20
dc.identifier280189666
dc.identifierf61b92fe-2a81-4a81-86b4-48c2448e486d
dc.identifier000834659100005
dc.identifier85135997395
dc.identifier.citationMann , P S , Smith , V A , Mitchell , J B O & Dobson , S A 2022 , ' N-strain epidemic model using bond percolation ' , Physical Review. E, Statistical, nonlinear, and soft matter physics , vol. 106 , no. 1 , 014304 . https://doi.org/10.1103/PhysRevE.106.014304en
dc.identifier.issn1539-3755
dc.identifier.otherORCID: /0000-0002-0487-2469/work/116598110
dc.identifier.otherORCID: /0000-0002-0379-6097/work/116598334
dc.identifier.otherORCID: /0000-0001-9633-2103/work/116598389
dc.identifier.urihttps://hdl.handle.net/10023/25724
dc.descriptionFunding: This work was partially supported by the UK Engineering and Physical Sciences Research Council under grant number EP/N007565/1 (Science of Sensor Systems Software).en
dc.description.abstractIn this paper we examine the structure of random networks that have undergone bond percolation an arbitrary, but finite, number of times. We define two types of sequential branching processes: a competitive branching process - in which each iteration performs bond percolation on the residual graph (RG) resulting from previous generations; and, collaborative branching process - where percolation is performed on the giant connected component (GCC) instead. We investigate the behaviour of these models, including the expected size of the GCC for a given generation, the critical percolation probability and other topological properties of the resulting graph structures using the analytically exact method of generating functions. We explore this model for Erds-Renyi and scale free random graphs. This model can be interpreted as a seasonal n-strain model of disease spreading.
dc.format.extent20
dc.format.extent1225937
dc.language.isoeng
dc.relation.ispartofPhysical Review. E, Statistical, nonlinear, and soft matter physicsen
dc.subjectComplex networksen
dc.subjectEpidemic spreadingen
dc.subjectPercolationen
dc.subjectCo-infectionen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectT-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccQA75en
dc.subject.lccRA0421en
dc.titleN-strain epidemic model using bond percolationen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. St Andrews Bioinformatics Uniten
dc.contributor.institutionUniversity of St Andrews. Office of the Principalen
dc.contributor.institutionUniversity of St Andrews. St Andrews Centre for Exoplanet Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. EaSTCHEMen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.identifier.doi10.1103/PhysRevE.106.014304
dc.description.statusPeer revieweden
dc.identifier.grantnumberEP/N007565/1en


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