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dc.contributor.authorNsoesie, Elaine O.en_US
dc.date.accessioned2014-03-14T21:10:29Z
dc.date.available2014-03-14T21:10:29Z
dc.date.issued2012-03-30en_US
dc.identifier.otheretd-04132012-144023en_US
dc.identifier.urihttp://hdl.handle.net/10919/37620
dc.description.abstractIn recent years, several methods have been proposed for real-time modeling and forecasting of the epidemic curve. These methods range from simple compartmental models to complex agent-based models. In this dissertation, we present a model-based reasoning approach to forecasting the epidemic curve and estimating underlying disease model parameters. The method is based on building an epidemic library consisting of past and simulated influenza outbreaks. During an influenza epidemic, we use a combination of statistical, optimization and modeling techniques to either assign the epidemic to one of the cases in the library or propose parameters for modeling the epidemic. The method is presented in four steps. First, we discuss a sensitivity analysis study evaluating how minute changes in the disease model parameters influence the dynamics of simulated epidemics. Next, we present a supervised classification method for predicting the epidemic curve. The epidemic curve is forecasted by matching the partial surveillance curve to at least one of the epidemics in the library. We expand on the classification approach by presenting a method which identifies epidemics similar or different from those in the library. Lastly, we discuss a simulation optimization method for estimating model parameters to forecast the epidemic curve of an epidemic distinct from those in the library.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartNsoesie_EO_D_2012.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectsensitivity analysisen_US
dc.subjectcomputational epidemiologyen_US
dc.subjectinfluenzaen_US
dc.subjectnetwork modelsen_US
dc.subjectepidemic forecastingen_US
dc.titleSensitivity Analysis and Forecasting in Network Epidemiology Modelsen_US
dc.typeDissertationen_US
dc.contributor.departmentGenetics, Bioinformatics, and Computational Biologyen_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.disciplineGenetics, Bioinformatics, and Computational Biologyen_US
dc.contributor.committeememberLeman, Scott C.en_US
dc.contributor.committeememberBassaganya-Riera, Josepen_US
dc.contributor.committeememberBevan, David R.en_US
dc.contributor.committeememberHoeschele, Inaen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04132012-144023/en_US
dc.contributor.committeecochairBeckman, Richard J.en_US
dc.contributor.committeecochairMarathe, Madhav V.en_US
dc.date.sdate2012-04-13en_US
dc.date.rdate2014-06-05
dc.date.adate2012-06-05en_US


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