The COVID-19 epidemic has highlighted a number of important challenges for infectious disease Epidemiologic research: 1) scaling causal-inference efforts across the human disease phenome; 2) understanding the long-term consequences of a novel disease without robust longitudinal data; and, 3) leveraging non-traditional data types for infectious disease research. Our dissertation provides three examples of advanced Epidemiologic methods that illustrate how researchers may address one or more of these challenges. Given the prevalence of multiple comorbidities, interrelated disease states may represent a more complete picture of COVID-19 infection severity risk compared to a disease-by-disease approach. We used a bias-adjusted, three-step Latent Class Analysis (LCA) method to identify patterns of comorbidities from 31 disease indicators; and, measured their relationship to severe COVID-19 infection among 176,894 participants in the UK Biobank cohort. We identified 5 distinct comorbidity patterns from 31 disease indicators, assessed using clinical diagnosis records from UK Biobank's comprehensive EHR data linkage between 2015-2019. Our results identified significantly increased risk for severe COVID-19 infection, with substantial heterogeneity in effect sizes, for each of our 4 comorbidity latent classes compared to our 'Healthy' latent class. We investigated the associations between genetic liability to severe COVID-19 infection, measured with Polygenic Risk Scores (PRS), and 31 comorbidity phenotypes derived from linked electronic health record (EHR) data in the past 20 years. PRS for very severe COVID-19 infection were associated with increased risk for uncomplicated diabetes, uncomplicated hypertension, obesity, and renal failure. Our research indicates that the same genetic composition that increases an individual's risk for COVID-19 may also influence their risk for other important comorbid diseases. Proximity to military bases may be an indicator of accessibility to military sexual partners; and, help identify important local HIV epidemics. We estimated the relationship between travel time to the nearest military base and HIV-status among 7,514 young women recruited at local venues. Our study found that adolescent girls and young women that meet or congregate near military bases were at a slightly elevated risk for HIV-infection in the combined sample, but only in 1 of our 4 military bases in stratified analysis.