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Richard C. Larson. Nigmatulina, Karima Robert Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2010-03-25T15:27:59Z 2010-03-25T15:27:59Z 2009 2009 http://hdl.handle.net/1721.1/53298 549268015 Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009. Cataloged from PDF version of thesis. Includes bibliographical references. After 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. by Karima Robert Nigmatulina. Ph.D. 265 p. eng Massachusetts Institute of Technology M.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. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Modeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventions Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7c9f9054-d575-47af-9847-6ac579531f02"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventions</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Nigmatulina, Karima Robert</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>After 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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>