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.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:hut2024-1022 |
| Date | 01 January 2024 |
| Creators | Crigger, Aviel S |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Honors Undergraduate Theses |
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