Location

Schewel 232

Access Type

Campus Access Only

Presentation Type

Oral Presentation

Start Date

April 2022

Department

Mathematics

Abstract

This thesis seeks to analyze the spread of COVID-19 and come up with a mathematical model to predict the chances of being infected by this disease using a variety of variables. This model will be based on the mathematical theory of Cellular Automata, otherwise known as the theory of spread. The research focuses on overall data of COVID-19 which includes infection rate, death rate, vaccination rate, and the rate of which outside factors limit the possibility of being infected. The theories being used predict that the more preventative measures one puts in place for themselves, the less likely they are to be infected by the virus. The COVID-19 data collected will be used in a model that will calculate the likelihood of disease 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 best combination of factors that will have the least amount of spread.

Faculty Mentor(s)

Dr. Christine Terry Dr. Leslie Hatfield Dr. Kevin Peterson

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Apr 6th, 3:15 PM

Cellular Automata: The Mathematical Theory Behind the Spread of COVID-19 and Prediction of Future Spread

Schewel 232

This thesis seeks to analyze the spread of COVID-19 and come up with a mathematical model to predict the chances of being infected by this disease using a variety of variables. This model will be based on the mathematical theory of Cellular Automata, otherwise known as the theory of spread. The research focuses on overall data of COVID-19 which includes infection rate, death rate, vaccination rate, and the rate of which outside factors limit the possibility of being infected. The theories being used predict that the more preventative measures one puts in place for themselves, the less likely they are to be infected by the virus. The COVID-19 data collected will be used in a model that will calculate the likelihood of disease 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 best combination of factors that will have the least amount of spread.