  Operations research (OR) approaches have been increasingly applied to model
the resilience of a system to surprise events. In order to model a surprise
event, one must have an understanding of its characteristics, which then become
parameters, decisions, and/or constraints in the resulting model. This means
that these models cannot (directly) handle fundamental surprise events, which
are events that could not be defined before they happen. However, OR models may
be adapted, improvised, or created during a fundamental surprise event, such as
the COVID-19 pandemic, to help respond to it. We provide a framework for how OR
models were applied by a university in response to the pandemic, thus helping
to understand the role of OR models during fundamental surprise events. Our
framework includes the following adaptations: adapting data, adding
constraints, model switching, pulling from the modeling toolkit, and creating a
new model. Each of these adaptations is formally presented, with supporting
evidence gathered through interviews with modelers and users involved in the
university response to the pandemic. We discuss the implications of this
framework for both OR and resilience.
