Mathematical Models of COVID-19

5-2021

Thesis

Abstract

For more than a year, the COVID-19 pandemic has been a major public health issue, affecting the lives of most people around the world. With both people’s health and the economy at great risks, governments rushed to control the spread of the virus. Containment measures were heavily enforced worldwide until a vaccine was developed and distributed. Although researchers today know more about the characteristics of the virus, a lot of work still needs to be done in order to completely remove the disease from the population. However, this is true for most of the infectious diseases in existence, including Influenza, Dengue fever, Ebola, Malaria, and Zika virus. Understanding the transmission process of a disease is usually acquired through biological and chemical studies. In addition, mathematical models and computational simulations offer different approaches to predict the number of infectious cases and identify the transmission patterns of a disease. Information obtained helps provide effective vaccination interventions, quarantine and isolation strategies, and treatment plans to reduce disease transmissions and prevent potential outbreaks. The focus of this paper is to investigate the spread of COVID-19 and its effect on a population through mathematical models. Specifically, we use SEIR and SEIR with vaccine models to formulate the spread of COVID-19, where S, E, I, R, and V are susceptible, exposed, infected, recovered, and vaccinated compartments, respectively. With these two models we calculate a central quantity in epidemiology called the basic reproduction number, R0. This helps examine the dynamical behavior of the models and how vaccines can help prevent the spread of the virus.

Mathematics

Thesis Comittee

Dr. Matthew Salomone, Committee Member

Dr. John Pike, Committee Member