Date Presented

Spring 5-15-2022

Document Type

Thesis

Degree Name

Bachelor of Science

Department

Mathematics

First Advisor

Dr. Leslie Hatfield

Second Advisor

Dr. Kevin Peterson

Third Advisor

Dr. Christine Terry

Abstract

This thesis seeks to analyze the spread of the original COVID-19 strain and develop a mathematical model to predict the chances of being infected by this disease using a number of variables. This model is based on the mathematical theory of cellular automata, otherwise known as the theory of spread. The research uses real world data of COVID-19 which includes infection rate, death rate, vaccination rate, use of masks, and transmission rates. By using cellular automata, we predict that the more preventative measures an individual puts in place for themselves, the less likely they are to be infected by the virus. Therefore, COVID-19 data will be used to calculate the likelihood of infection based on certain factors, such as if the person is masked and/or vaccinated, in a set environment, such as a classroom with a specified number of people around. The results from this research will predict the best way for a person to limit their chances of contracting the virus. It will also give us results on the optimal combination of factors that will be most effective at limiting the spread.

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