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

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.

Grantor dc:publisher

University of Guelph

Author dc:creator

• Spataru, Daiana

Advisor dc:contributor.advisor

• Demers, Matthew

• Optimization
• COVID-19
• modelling
• genetic algorithm
• SARS-CoV-2
• Parameter estimation
• compartmental model

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10214/26379

OAI record oai:identifier

https://atrium.lib.uoguelph.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aatrium.lib.uoguelph.ca%3A10214%2F26379