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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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Title: Hindsight in 2020: Modeling Preventative Measures for Select Diseases in Ontario using Probabilistic Dynamic Programming and SEIR Compartmental Frameworks
Author: Humphrey, Lia
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Cojocaru, Monica
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.
URI: https://hdl.handle.net/10214/25901
Date: 2021-06
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
Related Publications: 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


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Attribution-NonCommercial-ShareAlike 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International