Title: A Mathematical Model of COVID-19: Efficacy of Vaccination with Heterogeneous Populations
Abstract: Infections from the novel coronavirus disease 2019 (COVID-19) remain superfluous as it continues to spread profusely across the world. Currently, there is no available vaccine to protect against COVID-19. As scientists work to develop a vaccine, our goal is to explore scenarios for different levels of vaccine-effectiveness and varying proportions of vaccinated-populations in order to mitigate the spread of COVID-19. We develop a mathematical model to analyze the disease dynamics of COVID-19 in relation to vaccine-effectiveness. Furthermore, we performed a data fitting algorithm to estimate parameters within the model to best resemble current infection trends using data from the CDC. Our simulation results determine possible best-case scenarios at varying degrees of vaccine-effectiveness and proportions of vaccinated-populations. Moreover, to account for the disease's varying infection and mortality rates based on an individual's age, we further partition the population by age groups to determine which groups are most vital to vaccinate. Our simulation also identifies the minimal required vaccine-efficiency for a given proportion of vaccinated individuals.
Advisor: Vinodh Chellamuthu