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

From the beginning of the COVID-19 pandemic, universities have experienced unique challenges due to their dual nature as a place of education and residence. Current research has explored non-pharmaceutical approaches to combating COVID-19, including representing in models different categories such as age groups. One key area not currently well represented in models is the effect of pharmaceutical preventative measures, specifically vaccinations, on COVID-19 spread on college campuses. There remain key questions on the sensitivity of COVID-19 infection rates on college campuses to potentially time-varying vaccine immunity. Here we introduce a compartment model that decomposes a campus population into constituent subpopulations and implements vaccinations with time-varying efficacy. We use this model to represent a campus population with both vaccinated and unvaccinated individuals, and we analyze this model using two metrics of interest: maximum isolation population and symptomatic infection. We demonstrate a decrease in symptomatic infections occurs for vaccinated individuals when the frequency of testing for unvaccinated individuals is increased. We find that the number of symptomatic infections is insensitive to the frequency of testing of the unvaccinated subpopulation once about 80% or more of the population is vaccinated. Through a Sobol' global sensitivity analysis, we characterize the sensitivity of modeled infection rates to these uncertain parameters. We find that in order to manage symptomatic infections and the maximum isolation population campuses must minimize contact between infected and uninfected individuals, promote high vaccine protection at the beginning of the semester, and minimize the number of individuals developing symptoms.