Modeling the Spread of COVID-19 in University Communities

Abstract

Mathematical and simulation models are often used to predict the spread of a disease and estimate the impact of public health interventions, and many such models have been developed and used during the COVID-19 pandemic. This paper describes a study that systematically compared models for a university community, which has a much smaller but more connected population than a state or nation. We developed a stochastic agent-based model, a deterministic compartment model, and a model based on ordinary differential equations. All three models represented the disease progression with the same susceptible-exposed-infectious-recovered (SEIR) model. We created a baseline scenario for a population of 14,000 students and faculty and eleven other scenarios for combinations of interventions such as regular testing, contact tracing, quarantine, isolation, moving courses online, mask wearing, improving ventilation, and vaccination. We used parameter values from other epidemiological studies and incorporated data about COVID-19 testing in College Park, Maryland, but the study was designed to compare modeling approaches to each other using a synthetic population. For each scenario we used the models to estimate the number of persons who become infected over a semester of 119 days. We evaluated the models by comparing their predictions and evaluating their parsimony and computational effort. The agent-based model (ABM) and the deterministic compartment model (DCM) had similar results with cyclic flow of persons to and from quarantine, but the model based on ordinary differential equations failed to capture these dynamics. The ABM's computation time was much greater than the other two models' computation time. The DCM captured some of the dynamics that were present in the ABM's predictions and, like those from the ABM, clearly showed the importance of testing and moving classes on-line.