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dc.contributor.authorXia, Huadongen_US
dc.date.accessioned2015-04-08T08:00:08Z
dc.date.available2015-04-08T08:00:08Z
dc.date.issued2015-04-07en_US
dc.identifier.othervt_gsexam:2251en_US
dc.identifier.urihttp://hdl.handle.net/10919/51672
dc.description.abstractSocial contact networks represent proximity relationships between individual agents. Such networks are useful in diverse applications, including epidemiology, wireless networking and urban resilience. The vertices of a social contact network represent individual agents (e.g. people). Time varying edges represent time varying proximity relationship. The networks are relational -- node and edge labels represent important demographic, spatial and temporal attributes. Synthesizing social contact networks that span large urban regions is challenging for several reasons including: spatial, temporal and relational variety of data sources, noisy and incomplete data, and privacy and confidentiality requirements. Moreover, the synthesized networks differ due to the data and methods used to synthesize them. This dissertation undertakes a systematic study of synthesizing urban scale social contact networks within the specific application context of computational epidemiology. It is motivated by three important questions: (i) How does one construct a realistic social contact network that is adaptable to different levels of data availability? (ii) How does one compare different versions of the network for a given region, and what are appropriate metrics when comparing the relational networks? (iii) When does a network have adequate structural details for the specific application we have. We study these questions by synthesizing three social contact networks for Delhi, India. Our case study suggests that we can iteratively improve the quality of a network by adapting to the best data sources available within a framework. The networks differ by the data and the models used. We carry out detailed comparative analyses of the networks. The analysis has three components: (i) structure analysis that compares the structural properties of the networks, (ii) dynamics analysis that compares the epidemic dynamics on these networks and (iii) policy analysis that compares the efficacy of various interventions. We have proposed a framework to systematically analyze how details in networks impact epidemic dynamics over these networks. The results suggest that a combination of multi-level metrics instead of any individual one should be used to compare two networks. We further investigate the sensitivity of these models. The study reveals the details necessary for particular class of control policies. Our methods are entirely general and can be applied to other areas of network science.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectsocial contact networken_US
dc.subjectnetworked-epidemiologyen_US
dc.subjectnetwork analysisen_US
dc.subjectmodeling and simulationen_US
dc.subjectsocio-technique systemsen_US
dc.titleModeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studiesen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairMarathe, Madhav Vishnuen_US
dc.contributor.committeememberRamakrishnan, Narendranen_US
dc.contributor.committeememberBarrett, Christopher L.en_US
dc.contributor.committeememberMortveit, Henning S.en_US
dc.contributor.committeememberChen, Jiangzhuoen_US


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