Serosurvey of SARS-COV-2 at a large public university (preprint)

Arizona State University (ASU) is one the largest universities in the United States, with more than 79,000 students attending in-person classes. We conducted a seroprevalence study from September 13-17, 2021 to estimate the number of people in our community with SARS-CoV-2-specific antibodies due to previous exposure to SARS-CoV-2 and/or vaccination. Participants provided their age, gender, race, status (student or employee), and general COVID-19 health-related information like previous exposure and vaccination status. The seroprevalence of the anti-receptor binding domain (RBD) antibody was 90% by a lateral flow assay and 88% by a semi-quantitative chemiluminescent immunoassay. The seroprevalence for anti-nucleocapsid (NC) was 20%. In addition, individuals with previous natural COVID infection plus vaccination had higher anti-RBD antibody levels compared to those who had vaccination only or infection only. Individuals who had a breakthrough infection had the highest anti-RBD antibody levels. Accurate estimates of the cumulative incidence of SARS-CoV-2 infection can inform the development of university risk mitigation protocols such as encouraging booster shots, extending mask mandates, or reverting to online classes. It could help us to have clear guidance to take action at the first sign of the next surge as well, especially since there is a surge of COVID subvariant infections.

COVID-19 Healthcare Demand Projections: Arizona (preprint)

Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on the Arizona healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a bin initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios.