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dc.contributor.authorMa, Yifeien_US
dc.date.accessioned2013-06-25T08:00:10Z
dc.date.available2013-06-25T08:00:10Z
dc.date.issued2013-06-24en_US
dc.identifier.othervt_gsexam:1227en_US
dc.identifier.urihttp://hdl.handle.net/10919/23264
dc.description.abstractPandemics can significantly impact public health and society, for instance, the 2009 H1N1
and the 2003 SARS. In addition to analyzing the historic epidemic data, computational simulation of epidemic propagation processes and disease control strategies can help us understand the spatio-temporal dynamics of epidemics in the laboratory. Consequently, the public can be better prepared and the government can control future epidemic outbreaks more effectively. Recently, epidemic propagation simulation systems, which use high performance computing technology, have been proposed and developed to understand disease propagation processes. However, run-time infection situation assessment and intervention adjustment, two important steps in modeling disease propagation, are not well supported in these simulation systems. In addition, these simulation systems are computationally efficient in their simulations, but most of them have
limited capabilities in terms of modeling interventions in realistic scenarios.
In this dissertation, we focus on building a modeling and simulation environment for epidemic propagation and propagation control strategy. The objective of this work is to
design such a modeling environment that both supports the previously missing functions,
meanwhile, performs well in terms of the expected features such as modeling fidelity,
computational efficiency, modeling capability, etc. Our proposed methodologies to build
such a modeling environment are: 1) decoupled and co-evolving models for disease propagation, situation assessment, and propagation control strategy, and 2) assessing situations and simulating control strategies using relational databases. Our motivation for exploring these methodologies is as follows: 1) a decoupled and co-evolving model allows us to design modules for each function separately and makes this complex modeling system design simpler, and 2) simulating propagation control strategies using relational databases improves the modeling capability and human productivity of using this modeling environment. To evaluate our proposed methodologies, we have designed and built a loosely coupled and database supported epidemic modeling and simulation environment. With detailed experimental results and realistic case studies, we demonstrate that our modeling environment provides the missing functions and greatly enhances many expected features, such as modeling capability, without significantly sacrificing computational efficiency and scalability.
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.subjectEpidemic simulationen_US
dc.subjectDatabase systemen_US
dc.subjectDistributed systemen_US
dc.titleA Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysisen_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.committeechairChen, Jiangzhuoen_US
dc.contributor.committeememberFox, Edward A.en_US
dc.contributor.committeememberBisset, Keith R.en_US
dc.contributor.committeememberEubank, Stephen G.en_US
dc.contributor.committeememberVullikanti, Anil Kumar S.en_US


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