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dc.contributor.authorZhang, Zhaoyang
dc.contributor.authorWang, Honggang
dc.contributor.authorWang, Chonggang
dc.contributor.authorFang, Hua (Julia)
dc.date2022-08-11T08:10:34.000
dc.date.accessioned2022-08-23T17:13:19Z
dc.date.available2022-08-23T17:13:19Z
dc.date.issued2015-09-01
dc.date.submitted2015-11-20
dc.identifier.citation<p>Zhaoyang Zhang; Honggang Wang; Chonggang Wang; Hua Fang, "Modeling Epidemics Spreading on Social Contact Networks," in Emerging Topics in Computing, IEEE Transactions on , vol.3, no.3, pp.410-419, Sept. 2015. doi: 10.1109/TETC.2015.2398353. <a href="http://dx.doi.org/10.1109/TETC.2015.2398353" target="_blank" title="http://dx.doi.org/10.1109/TETC.2015.2398353">Link to article on publisher's site</a></p>
dc.identifier.doi10.1109/TETC.2015.2398353
dc.identifier.pmid27722037
dc.identifier.urihttp://hdl.handle.net/20.500.14038/46698
dc.description.abstractSocial contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.
dc.language.isoen_US
dc.publisherIEEE
dc.relation<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/27722037" target="_blank">Link to article on PubMed</a></p>
dc.rightsCopyright 2015 IEEE. Open access. Publisher PDF posted as allowed by the publisher's author rights policy at http://www.ieee.org/publications_standards/publications/rights/oapa.pdf.
dc.subjectUMCCTS funding
dc.subjectEpidemic Control
dc.subjectEpidemic modeling
dc.subjectOptimal Strategies
dc.subjectSocial Contact Network
dc.subjectBiostatistics
dc.subjectComputer Sciences
dc.subjectMathematics
dc.subjectMedicine and Health
dc.subjectPublic Health
dc.subjectStatistical Models
dc.titleModeling Epidemics Spreading on Social Contact Networks
dc.typeJournal Article
dc.source.journaltitleIEEE Transactions on Emerging Topics in Computing
dc.source.volume3
dc.source.issue3
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2155&amp;context=qhs_pp&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/1155
dc.identifier.contextkey7865852
refterms.dateFOA2022-08-23T17:13:19Z
html.description.abstract<p>Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.</p>
dc.identifier.submissionpathqhs_pp/1155
dc.contributor.departmentDepartment of Quantitative Health Sciences, Division of Biostatistics and Health Services Research
dc.source.pages410-419


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