Modeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studies

Xia, Huadong

Modeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studies

dc.contributor.author	Xia, Huadong	en
dc.contributor.committeechair	Marathe, Madhav Vishnu	en
dc.contributor.committeemember	Ramakrishnan, Naren	en
dc.contributor.committeemember	Barrett, Christopher L.	en
dc.contributor.committeemember	Mortveit, Henning S.	en
dc.contributor.committeemember	Chen, Jiangzhuo	en
dc.contributor.department	Computer Science	en
dc.date.accessioned	2015-04-08T08:00:08Z	en
dc.date.available	2015-04-08T08:00:08Z	en
dc.date.issued	2015-04-07	en
dc.description.abstract	Social 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
dc.description.degree	Ph. D.	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:2251	en
dc.identifier.uri	http://hdl.handle.net/10919/51672	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	social contact network	en
dc.subject	networked-epidemiology	en
dc.subject	network analysis	en
dc.subject	modeling and simulation	en
dc.subject	socio-technique systems	en
dc.title	Modeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studies	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en