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Natasha Markuzon and Marta Gonzalez. Fast, Shannon M. (Shannon Marie) Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2014-11-04T21:34:23Z 2014-11-04T21:34:23Z 2014 2014 http://hdl.handle.net/1721.1/91406 893487304 Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. 85 Cataloged from PDF version of thesis. Includes bibliographical references (pages 99-104). Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviors from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response to disease spread. We couple the disease spread and panic spread processes and model them through local interactions between agents. The behavioral contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analyzing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City, the 2003 SARS and 2009 H1N1 outbreaks in Hong Kong and the 2012-2013 Boston influenza season, accurately predicting population-level behavior. The effect of interventions on the disease spread and social response is explored, and we implement an optimization study to determine the least cost intervention, taking into account the costs of the disease itself, the intervention and the social response. We show that the optimal strategy is dependent upon the relative costs assigned to infection with the disease, intervention and social response, as well as the perceived risk of infection. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. by Shannon M. Fast. S.M. 107 pages 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. Pandemic panic : a network-based approach to predicting social response during a disease outbreak Network-based approach to predicting social response during a disease outbreak Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="05de36f3-af8c-4980-b11b-f38d790f66b0"> <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>Pandemic panic : a network-based approach to predicting social response during a disease outbreak</Title> <Subtitle>Network-based approach to predicting social response during a disease outbreak</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <Authors> <Author> <DisplayName>Fast, Shannon M. (Shannon Marie)</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>Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviors from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response to disease spread. We couple the disease spread and panic spread processes and model them through local interactions between agents. The behavioral contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analyzing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City, the 2003 SARS and 2009 H1N1 outbreaks in Hong Kong and the 2012-2013 Boston influenza season, accurately predicting population-level behavior. The effect of interventions on the disease spread and social response is explored, and we implement an optimization study to determine the least cost intervention, taking into account the costs of the disease itself, the intervention and the social response. We show that the optimal strategy is dependent upon the relative costs assigned to infection with the disease, intervention and social response, as well as the perceived risk of infection. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>