<p style='text-indent:20px;'>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.</p>

中文翻译：

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基于隔间和代理模型的计算和概念复杂性研究

<p style='text-indent:20px;'>持续的 COVID-19 大流行凸显了数学模型在了解病毒传播以及对各种缓解措施的影响进行可量化和基于科学的预测方面的重要作用。已采用多种类型的模型，并取得了不同程度的成功。这就引出了什么样的数学模型最适合给定情况的问题。我们考虑了两种广泛使用的模型类型：基于方程的模型（例如标准的分区流行病学模型）和基于代理的模型。我们通过对不同情景下 COVID-19 在夏威夷瓦胡岛上的传播进行建模来评估它们的性能。我们表明，当涉及到对决策至关重要的信息时，两种模型都会产生非常相似的结果。同时，当考虑到它们的计算和概念复杂性时，这两种类型的模型表现出非常不同的特征。因此，我们得出结论，选择模型应主要以可用的计算和人力资源为指导。</p>