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Using a Genetic Algorithm for Parameter Estimation in a Modified SEIR Model of COVID-19 Spread in Ontario

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dc.contributor.advisor Demers, Matthew Spataru, Daiana 2021-09-08T19:54:54Z 2021-09-08T19:54:54Z 2021-09 2021-09-01
dc.description.abstract In December 2019, the WHO in China reported cases of pneumonia of unknown etiology which was soon identified as a novel coronavirus: SARS-CoV-2 and its corresponding disease, COVID-19. By January 2020, the virus had spread to 16 countries around the world infecting almost 10,000 individuals. In this thesis, we analyze a compartmental model of the spread of COVID-19 for the case of China and develop a genetic algorithm that can successfully extract model parameters to provide insights into the dynamics of the virus. We develop a new deterministic compartmental model of COVID-19 spread in Ontario to capture the multiple waves of the pandemic as well as the effects of undetected individuals in the population. We use a genetic algorithm to extract a set of parameters that produces solutions to the system of ordinary differential equations that best describe the cumulative number of cases and deaths in Ontario. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.subject Optimization en_US
dc.subject COVID-19 en_US
dc.subject modelling en_US
dc.subject genetic algorithm en_US
dc.subject SARS-CoV-2 en_US
dc.subject Parameter estimation en_US
dc.subject compartmental model en_US
dc.title Using a Genetic Algorithm for Parameter Estimation in a Modified SEIR Model of COVID-19 Spread in Ontario en_US
dc.type Thesis en_US Mathematics and Statistics en_US Master of Science en_US Department of Mathematics and Statistics en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated. University of Guelph en_US

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