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dc.contributor.authorDube, Michael
dc.contributor.authorHoughten, Sheridan
dc.contributor.authorAshlock, Daniel
dc.date.accessioned2022-11-11T14:24:30Z
dc.date.available2022-11-11T14:24:30Z
dc.date.issued2019-07
dc.identifier.citationM. Dubé, S. Houghten and D. Ashlock, "Pandemic: A Graph Evolution Story," 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2019, pp. 1-8, doi: 10.1109/CIBCB.2019.8791445.en_US
dc.identifier.urihttp://hdl.handle.net/10464/16915
dc.description.abstractThe Graph Evolution Tool (GET)was built to generate personal contact networks representing who can infect whom within a community. The tool is expanded in order to permit an infection scheme which divides the community into different districts, thus permitting within-district and between-district infections. The evolutionary algorithm comprising GET is expanded upon to simulate communities which include 512 individuals in up to eight districts, initially infecting one person in one district and spreading through a community. The overall goal is to generate communities that will maximize the length of an epidemic. The problem associated with adequately exploring the numerous parameters accompanying evolutionary algorithms is addressed using a point packing and insight from previous work. The Susceptible-Infected-Removed (SIR)model of infection was chosen as it provides a sufficient balance of simplicity and complexity for the problem.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights.urihttps://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
dc.source2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
dc.subjectEpidemic Modellingen_US
dc.subjectGraph Evolutionen_US
dc.titlePandemic: A Graph Evolution Storyen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/cibcb.2019.8791445
refterms.dateFOA2022-11-11T14:24:30Z


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