Browsing by Author "Javan, Emily Morgan"

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Modeling heterogeneities in disease risk : mapping COVID-19 and identifying osteoarthritis
(2024-05) Javan, Emily Morgan ; Meyers, Lauren Ancel; Gaither, Kelly P.; Narasimhan, Vagheesh M.; Wilke, Claus O.
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.