Keywords
Epidemiology; Disease Modeling; Compartmental Model
Abstract
Understanding the true burden of community transmission of communicable diseases like COVID-19 is crucial for effective public health response. Clinical cases, while important, only represent a fraction of the actual disease prevalence within a population. In this thesis, we investigate methods to estimate parameters that link clinical cases to the true disease prevalence using a modified compartmental model known as SICR (Susceptible, Infected, Cases, Recovered). We employ Bayesian inference and ensemble Markov chain Monte Carlo (MCMC) simulations to analyze clinical case data provided by the University of Central Florida Health Center from 2020 to 2022. Our goal is to estimate modeling parameters that shed light on the spread of COVID-19 spread on campus, which could help understand the spread of other respiratory diseases in communities like colleges.
Thesis Completion Year
2024
Thesis Completion Semester
Spring
Thesis Chair
Zhisheng, Shuai
College
College of Undergraduate Studies
Department
College of Sciences
Thesis Discipline
Mathematics
Language
English
Access Status
Open Access
Length of Campus Access
None
Campus Location
Orlando (Main) Campus
STARS Citation
Crigger, Aviel S., "Estimating Modeling Parameters for COVID-19 Spread on Campus" (2024). Honors Undergraduate Theses. 7.
https://stars.library.ucf.edu/hut2024/7
Included in
Ordinary Differential Equations and Applied Dynamics Commons, Other Applied Mathematics Commons