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

Spataru, Daiana
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University of Guelph

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.

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