Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
Committee Member 1
Committee Member 2
Despite continuing medical intervention and control with over 13 billion vaccine doses distributed by February 17, 2023, the WHO recorded a wide global spread of the Coronavirus Disease 2019 (COVID-19) including over 756 million infected individuals, and over 6.8 million deaths. In this study, a theoretical discrete-time Markov chain model for the disease, that approximates a Markov jump process for the disease dynamics is investigated. The primary focus is to conduct statistical inferences in the model via applying the Maximum-likelihood (ML-) method ; and to derive the Expectation-Maximization (EM) algorithm for finding ML-estimates for parameters in the model. Disease control epidemiological parameters such as the basic reproduction number and the probability of no spread are derived for the model. Also, the impacts of vaccination, access to effective hospitalization and asymptomatic/ symptomatic transmissions are assessed via conducting sensitivity analysis on the model, numerically.
Collins, Ivy, "Statistical Inference in a COVID-19 Stochastic Model: Modeling, Analysis, Maximum Likelihood Estimation and EM-Algorithm" (2023). Electronic Theses and Dissertations. 2530.
Research Data and Supplementary Material
Available for download on Monday, March 27, 2028