Date of Award
Summer 2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematical and Statistical Sciences
Program
Applied Statistics
First Advisor
Bansal, Naveen K.
Second Advisor
Rowe, Daniel
Third Advisor
Yu, Chenghan
Abstract
A novel coronavirus disease was first discovered in Wuhan, China, in December 2019. This new coronavirus named COVID-19 has rapidly spread and become a global threat affecting almost all the countries in the world. Therefore, it is important to know the trend of coronavirus disease to mitigate its effects. A good prediction model is crucial for the health care system to understand the trend of the COVID-19. This study aims to construct a good prediction model. Firstly, we detect change points of the time series data of daily confirmed cases and deaths of COVID-19 in the United States and Europe, and secondly, construct prediction models for daily confirmed cases and deaths of COVID-19 based on the data that was divided by the change points, and thirdly, select the best prediction model to forecast the future number of daily confirmed cases and deaths of COVID-19 in the United States and Europe. The data was collected from the official website of the Centers for Disease Control (CDC) and Our World in Data from August 1st, 2020 to January 23th, 2021, and we used daily confirmed cases and deaths of COVID-19 in the United States and Europe. An Auto-Regressive Integrated Moving Average (ARIMA) model was used to predict the daily new confirmed cases and deaths of COVID-19 from January 24th, 2021 to February 22th, 2021. This study finds that Change-Point ARIMA models that was divided the data by change points improve the forecasting trends of daily new confirmed cases and deaths of COVID-19 in the United States and Europe.