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Hindsight in 2020: Modeling Preventative Measures for Select Diseases in Ontario using Probabilistic Dynamic Programming and SEIR Compartmental Frameworks

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dc.contributor.advisor Cojocaru, Monica
dc.contributor.author Humphrey, Lia
dc.date.accessioned 2021-06-07T18:33:52Z
dc.date.available 2021-06-07T18:33:52Z
dc.date.copyright 2021-06
dc.date.created 2021-06-01
dc.date.issued 2020
dc.identifier.uri https://hdl.handle.net/10214/25901
dc.description.abstract In this work, we provide a “rear-view mirror” analysis for approaches to two different diseases that were relevant to Ontario in 2020. Using probabilistic dynamic programming, we simulate two versions of an optimized publicly funded shingles vaccination program for seniors; the first using existing vaccination data under the 2016-2019 program, and the second by building on a population with existing coverage and a new vaccine with improved efficacy. Then, using an SEIR compartmental model with ordinary differential equations, we use a derivative-free optimization method to model age-stratified case development of COVID-19 during the first wave of the pandemic, as well as determining the relative significance of specific NPI measures in slowing transmission. Lessons learned from the early handling of these diseases may inform better healthcare planning and prevent unnecessary future costs and suffering. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/ *
dc.subject dynamic programming en_US
dc.subject optimization en_US
dc.subject disease modeling en_US
dc.subject herpes zoster en_US
dc.subject COVID-19 en_US
dc.title Hindsight in 2020: Modeling Preventative Measures for Select Diseases in Ontario using Probabilistic Dynamic Programming and SEIR Compartmental Frameworks en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
dcterms.relation R. Fields, L. Humphrey, D. Flynn-Primrose, Z. Mohammadi, E. W. Thommes, and M. G. Cojocaru. Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave. Heliyon Mathematics en_US
dc.degree.grantor University of Guelph en_US


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