{"id":{"repo_id":"guelph","oai_identifier":"oai:atrium.lib.uoguelph.ca:10214/26379"},"canonical_url":"https://search.dev.ndltd.org/etd/guelph/oai:atrium.lib.uoguelph.ca:10214/26379","repository":{"repo_id":"guelph","name":"University of Guelph","base_url":"https://atrium.lib.uoguelph.ca/server/oai/request"},"display":{"title":"Using a Genetic Algorithm for Parameter Estimation in a Modified SEIR Model of COVID-19 Spread in Ontario","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Spataru, Daiana"],"institution":"University of Guelph","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Demers, Matthew"],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-08-21T16:45:07Z","subjects":["Optimization","COVID-19","modelling","genetic algorithm","SARS-CoV-2","Parameter estimation","compartmental model"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10214/26379","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://atrium.lib.uoguelph.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aatrium.lib.uoguelph.ca%3A10214%2F26379","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Demers, Matthew"]},{"key":"dc:creator","label":"Author","values":["Spataru, Daiana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-09-08T19:54:54Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-09-08T19:54:54Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Guelph"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Optimization","COVID-19","modelling","genetic algorithm","SARS-CoV-2","Parameter estimation","compartmental model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10214/26379"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Using a Genetic Algorithm for Parameter Estimation in a Modified SEIR Model of COVID-19 Spread in Ontario"]}]}],"canonical_facts":{"dc:contributor.advisor":["Demers, Matthew"],"dc:creator":["Spataru, Daiana"],"dc:date.accessioned":["2021-09-08T19:54:54Z"],"dc:date.available":["2021-09-08T19:54:54Z"],"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."],"dc:identifier.uri":["https://hdl.handle.net/10214/26379"],"dc:language.iso":["en"],"dc:publisher":["University of Guelph"],"dc:subject":["Optimization","COVID-19","modelling","genetic algorithm","SARS-CoV-2","Parameter estimation","compartmental model"],"dc:title":["Using a Genetic Algorithm for Parameter Estimation in a Modified SEIR Model of COVID-19 Spread in Ontario"],"dc:type":["Thesis"]},"updated_at":"2026-08-21T16:45:07Z"}