Modeling for COVID-19 college reopening decisions: Cornell, a case study

Area of Science:

• Epidemiology
• Public Health
• Mathematical Modeling

Background:

• Universities are vulnerable to COVID-19 outbreaks due to high population density and social mixing.
• Parameter uncertainty in epidemiological models persists due to evolving viral variants, vaccine efficacy, and policy changes.

Purpose of the Study:

• To develop and apply an epidemiological model for designing effective COVID-19 interventions in university settings.
• To inform Cornell University's decision to reopen for in-person instruction and design an asymptomatic screening program.

Main Methods:

• Utilized epidemiological modeling to simulate COVID-19 transmission dynamics within a university population.
• Incorporated university-specific characteristics such as a young demographic, high social contact rates, and intervention capabilities.
• Evaluated the impact of asymptomatic screening strategies on viral spread, considering parameter uncertainty.

Main Results:

• The model supported Cornell University's safe reopening in Fall 2020 and the implementation of a concurrent asymptomatic screening program.
• Risk mitigation was achieved despite parameter uncertainty, demonstrating the robustness of the modeling approach.
• Once-per-week asymptomatic screening of vaccinated undergraduates showed significant value against the Delta variant, even with high vaccination rates.

Conclusions:

• Epidemiological modeling provides a framework for managing COVID-19 risk in universities, adaptable to unique campus environments.
• Asymptomatic screening, particularly targeting highly social individuals, is an effective intervention for controlling viral spread in vaccinated student populations.
• The study demonstrates a generalizable approach for universities to design data-driven public health strategies amidst ongoing pandemic challenges.