  The ongoing COVID-19 pandemic highlights the essential role of mathematical
models in understanding the spread of the virus along with a quantifiable and
science-based prediction of the impact of various mitigation measures. Numerous
types of models have been employed with various levels of success. This leads
to the question of what kind of a mathematical model is most appropriate for a
given situation. We consider two widely used types of models: equation-based
models (such as standard compartmental epidemiological models) and agent-based
models. We assess their performance by modeling the spread of COVID-19 on the
Hawaiian island of Oahu under different scenarios. We show that when it comes
to information crucial to decision making, both models produce very similar
results. At the same time, the two types of models exhibit very different
characteristics when considering their computational and conceptual complexity.
Consequently, we conclude that choosing the model should be mostly guided by
available computational and human resources.
