Assessing parameter sensitivity in a university campus COVID-19 model with vaccinations

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Universities face unique COVID-19 challenges due to their residential and educational nature.
• Existing models often lack detailed representation of pharmaceutical interventions like vaccination.
• Time-varying vaccine immunity's impact on campus COVID-19 spread requires further investigation.

Purpose of the Study:

• To develop a compartment model for COVID-19 spread on college campuses.
• To incorporate time-varying vaccine efficacy and population subpopulations.
• To analyze the sensitivity of infection rates to vaccination and testing strategies.

Main Methods:

• Developed a compartment model simulating campus populations with vaccinated and unvaccinated individuals.
• Implemented time-varying vaccine efficacy within the model.
• Utilized Sobol' global sensitivity analysis to assess parameter influence on infection rates.

Main Results:

• Increased testing frequency for unvaccinated individuals correlates with decreased symptomatic infections.
• Symptomatic infections become insensitive to unvaccinated testing frequency when vaccination coverage reaches approximately 80%.
• Model sensitivity analysis identified key parameters influencing campus infection dynamics.

Conclusions:

• Campus outbreak management requires minimizing contact and maximizing early-semester vaccine protection.
• Reducing symptomatic cases and isolation burden is achievable through strategic interventions.
• High vaccination rates significantly reduce the impact of testing frequency on infection spread.