This study aimed to i) investigate the prevalence of unhealthy attributes, common diseases, and inequities in social determinants of health across a large and representative sample of adults in New York City; and ii) identify common key predictors of COVID-19 seropositivity by comparing various regression models using a hierarchical regression method among a sample of New York City adults. The study will use the New York City Community Health Survey (NYC CHS) 2020 dataset for this analysis. An exploratory approach is used to data to understand the social, environmental, and individual determinants of health in the New York City population at the peak of the pandemic and their effects on COVID-19 seropositivity. The study also emphasizes on using a predictive modeling approach to develop and select an optimal ML model that accurately predicts COVID-19 seropositivity from various ML algorithms. Hierarchical logistic regression was carried out on a sample of 928 participants. It was found that age group 65-75, Black and Hispanic race and being born in the US were statistically significant factors in model 1 of the hierarchical regression where only socioeconomic factors were considered. With the inclusion of health behaviors, tobacco smoking behaviors, and physical activity were statistically significant. In the full model, BMI, asthma prevalence, and suicidal thoughts were statistically significantly correlated with COVID-19 seropositivity. The findings are consistent with public health literature highlighting the importance of healthy behaviors and public health efforts in maintaining overall health and immunity.

COVID-19 continues to have a major impact on US health and society. Robust research on the epidemiology of acute and post-acute COVID-19 remains fundamentally important to informing policy makers, scientists, as well as the public. This dissertation reports on the development of a large, diverse, United States general population-based meta-cohort with standardized, prospective ascertainment of SARS-CoV-2 and COVID-19, integrated with comprehensive pre-pandemic phenotyping from 14 extant cohort studies. Meta-cohort data were used to investigate risk and resilience factors for incident severe (hospitalized or fatal) and non-severe COVID-19 and correlates of time-to-recovery from SARS-CoV-2 infection. Results support the major acute and post-acute public health impact of COVID-19 and the vital role of modifiable (e.g., obesity, diabetes, cardiovascular disease) and non-modifiable (e.g., age, sex) risk factors for adverse COVID-19 outcomes. Findings suggest that standard primary care interventions—including obesity and cardiometabolic disease prevention and treatment, depression care, and vaccination—remain fundamental to COVID-19 risk mitigation among US adults. Given its longitudinal design and comprehensive pre-pandemic and pandemic-era measurements, the meta-cohort is well suited to support ongoing work regarding the public health impact of SARS-CoV-2 infection, COVID-19, post-acute sequelae, and pandemic-related social and behavioral changes across multiple health domains.