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dc.contributor.authorDubé, Michael
dc.date.accessioned2021-01-15T14:22:03Z
dc.date.available2021-01-15T14:22:03Z
dc.identifier.urihttp://hdl.handle.net/10464/14995
dc.description.abstractA global pandemic remains a public health event that presents a unique and unpredictable challenge for those making health related decisions and the populations who experience the virus. Though a pandemic also provides the opportunity for researchers and health administrations around the world to mobilize in the fields of epidemiology, computer science, and mathematics to generate epidemic models, vaccines, and vaccination strategies to mitigate unfavourable outcomes. To this end, a generative representation to create personal contact networks, representing the social connections within a population, known as the Local THADS-N generative representation is introduced and expanded upon. This representation uses an evolutionary algorithm and is modified to include new local edge operations improving the performance of the system across several test problems. These problems include an epidemic's duration, spread through a population, and closeness to past epidemic behaviour. The system is further developed to represent sub-communities known as districts, better articulating epidemics spreading within and between neighbourhoods. In addition, the representation is used to simulate four competing vaccination strategies in preparation for iterative vaccine deployment amongst a population, an inevitability when considering the lag inherent to developing vaccines. Finally, the Susceptible-Infected-Removed (SIR) model of infection used by the system is expanded in preparation for adding an asymptomatic state of infection as seen within the COVID-19 pandemic.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectEvolutionary Computationen_US
dc.subjectEvolutionary Algorithmsen_US
dc.subjectVaccination Strategiesen_US
dc.subjectEpidemic Simulationen_US
dc.subjectPersonal Contact Networksen_US
dc.titleEpidemic Simulation and Mitigation via Evolutionary Computationen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-15T02:02:48Z


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