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dc.contributor.advisorRichard C. Larson.en_US
dc.contributor.authorNigmatulina, Karima Roberten_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2010-03-25T15:27:59Z
dc.date.available2010-03-25T15:27:59Z
dc.date.copyright2009en_US
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/53298
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractAfter reviewing prevalent approaches to the modeling pandemic influenza transmission, we present a simple distributional model that captures the most significant population attributes that alter the dynamics of the outbreak. We describe how diversities in activity, susceptibility and infectivity can drive or dampen the spread of infection. We expand the model to show infection spread between several linked heterogeneous communities; this multi-community model is based on analytical calculations and Monte Carlo simulations. Focusing on mitigation strategies for a global pandemic influenza, we use our mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. In addition, as we witnessed with the SARS outbreak in 2003, human behavior is likely to change during the course of a pandemic. We propose several different novel approaches to incorporating reactive social distancing and hygiene improvement and its impact on the epidemic curve. Our results indicate that while a flu pandemic could be devastating; there are non-pharmaceutical coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak. We conclude with a discussion of the implications of the modeling work in the context of university planning for a pandemic.en_US
dc.description.statementofresponsibilityby Karima Robert Nigmatulina.en_US
dc.format.extent265 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleModeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventionsen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center.en_US
dc.identifier.oclc549268015en_US


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