  A number of theoretical models have been developed in recent years modelling
epidemic spread in educational settings such as universities to help inform
re-opening strategies during the Covid-19 pandemic. However, these studies have
had differing conclusions as to the most effective non-pharmaceutical
interventions. They also largely assumed permanent acquired immunity, meaning
we have less understanding of how disease dynamics will play out when immunity
wanes. Here we complement these studies by developing and analysing a
stochastic simulation model of disease spread on a university campus where we
allow immunity to wane, expoloring the effectiveness of different
interventions. We find that the two most effective interventions to limit the
severity of a disease outbreak are reducing extra-household mixing and
surveillance testing backed-up by a moderate isolation period. We find that
contact tracing only has a limited effect, while reducing class sizes only has
much effect if extra-household mixing is already low. We identify a range of
measures that can not only limit an outbreak but prevent it entirely, and also
comment on the variation in measures of severity that emerge from our
stochastic simulations. We hope that our model may help in designing effective
strategies for universities in future disease outbreaks.
