  This study introduces a stochastic model of COVID-19 transmission tailored to
the Colorado School of Mines campus and evaluates surveillance testing
strategies within a university context. Enhancing the conventional SEIR
framework with stochastic transitions, our model accounts for the unique
characteristics of disease spread in a residential college, including specific
states for testing, quarantine, and isolation. Employing an approximate
Bayesian computation (ABC) method for parameter estimation, we navigate the
complexities inherent in stochastic models, enabling an accurate fit of the
model with the campus case data. We then studied our model under the estimated
parameters to evaluate the efficacy of different testing policies that could be
implemented on a university campus. This framework not only advances
understanding of COVID-19 dynamics on the Mines campus but serves as a
blueprint for comparable settings, providing insights for informed strategies
against infectious diseases.
