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Identification and characterization of super-spreaders from voluminous epidemiology data

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dc.contributor.authorShah, Harshil, author
dc.contributor.authorPallickara, Shrideep, advisor
dc.contributor.authorPallickara, Sangmi, advisor
dc.contributor.authorBreidt, F. Jay, committee member
dc.date.accessioned2017-01-04T22:59:10Z
dc.date.available2017-01-04T22:59:10Z
dc.date.issued2016
dc.description.abstractPlanning for large-scale epidemiological outbreaks often involves executing compute-intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events such as super-spreading events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this thesis, we propose a distributed approach for analyzing voluminous epidemiology data to locate and classify the super-spreaders in a disease network. Our methodology constructs analytical models using features extracted from the epidemiology data. The analytical models are amenable to interpretation and disease planners can use them to inform identification of super-spreaders that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierShah_colostate_0053N_13910.pdf
dc.identifier.urihttp://hdl.handle.net/10217/178870
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleIdentification and characterization of super-spreaders from voluminous epidemiology data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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