Modeling heterogeneities in disease risk : mapping COVID-19 and identifying osteoarthritis
| dc.contributor.advisor | Meyers, Lauren Ancel | |
| dc.contributor.committeeMember | Gaither, Kelly P. | |
| dc.contributor.committeeMember | Narasimhan, Vagheesh M. | |
| dc.contributor.committeeMember | Wilke, Claus O. | |
| dc.creator | Javan, Emily Morgan | |
| dc.creator.orcid | 0000-0002-6008-0271 | |
| dc.date.accessioned | 2024-07-25T01:22:07Z | |
| dc.date.available | 2024-07-25T01:22:07Z | |
| dc.date.created | 2024-05 | |
| dc.date.issued | 2024-05 | |
| dc.date.submitted | May 2024 | |
| dc.date.updated | 2024-07-25T01:22:08Z | |
| dc.description.abstract | Differences in disease risk and observed outcomes arise from heterogeneities in both environmental and genetic factors within a population. By identifying the drivers of risk, we can improve public awareness and design interventions to mitigate human suffering. Here I investigated the complexities of disease risk in human populations through three studies that utilize COVID-19 case data in the United States and hospitalization records from Austin, TX, and the United Kingdom. First, I modeled the risk of an undetected COVID-19 epidemic for each US county at the start of the pandemic. By March 16, 2020 once a single case was detected a county had a mean epidemic risk of 71% (95% CI: 52-83%), implying COVID-19 was already spreading widely by the first detected case. I reinforced public support for proactive measures given the virus' widespread presence before testing was widely available. Second, I explored the spatial and temporal disparities in COVID-19 deaths, hospitalizations, and infections. By comparing ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, we found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period, highlighting the excess COVID-19 burden suffered by vulnerable populations within Austin, TX. Lastly, a co-author employed deep learning on DXA scans of adult knees (mean age 64) from the UK Biobank and identified 1931 (178%) more cases of severe knee osteoarthritis than currently diagnosed in the health record. I performed a genome wide association study on the image derived quantitative measure of joint space width that identified these new cases and compared them to the standard binary case-control phenotype. The quantitative measure increased the number of genome-wide significant loci discovered from 6 to 18, despite the two phenotypes being highly genetically correlated (−0.92). My dissertation identifies latent predictors of disease risk for both infectious and degenerative diseases across spatiotemporal scales within diverse human populations. | |
| dc.description.department | Ecology, Evolution and Behavior | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.identifier.uri | https://hdl.handle.net/2152/126157 | |
| dc.identifier.uri | https://doi.org/10.26153/tsw/52694 | |
| dc.language.iso | English | |
| dc.subject | SARS-CoV-2 | |
| dc.subject | COVID-19 | |
| dc.subject | GWAS | |
| dc.subject | Osteoarthritis | |
| dc.subject | Equity | |
| dc.subject | Health | |
| dc.title | Modeling heterogeneities in disease risk : mapping COVID-19 and identifying osteoarthritis | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.college | Natural Sciences | |
| thesis.degree.department | Ecology, Evolution and Behavior | |
| thesis.degree.discipline | Evolution, Ecology, and Behavior | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.name | Doctor of Philosophy | |
| thesis.degree.program | Evolution, Ecology, and Behavior | |
| thesis.degree.school | University of Texas at Austin |
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