  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.
