Throughout the COVID-19 pandemic, government policy and healthcare implementation responses had been guided by reported positivity rates and vaccination rates in the community. The selection bias of these test data had questioned their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. Publicly available vaccination data had frequently been cited as a proxy for population immunity, but this metric had ignored the effects of naturally-acquired immunity. The health disparities concerning asymptomatic and symptomatic patients had not yet been studied.

This project had aimed to develop a valid metric to estimate the true viral incidence and naturally/vaccine-acquired immunity prevalence in the community, examine the health disparities and social inequality, and monitor the epidemic over time as an operational surveillance system. The approach had collected routine testing data on SARS-CoV-2 exposure and antibody seropositivity among patients in a hospital system and had performed statistical adjustments of sample representation using multilevel regression and poststratification (MRP), which adjusted for measured differences between the sample and population and also yielded stable small area estimates. The data collection and analysis procedure had provided information to entire communities with generalizability and a focus on burdens within specific demographics, with close attention to vulnerable populations and disparities across health outcomes, social determinants, and behaviors.

In particular, the research had yielded group-specific estimates of disparities with respect to asymptomatic and symptomatic patients and how these discrepancies may have impacted the socio-demographically dependent spread of disease and its subsequent treatment. The MRP adjustment had been made publicly accessible via a web interface and promoted broad investigations with integrated data sources toward a national study.