标题： 基于代理的佛罗里达州新冠疫情建模 显示英文标题

标题： Agent-based modeling of the COVID-19 pandemic in Florida

摘要： 新冠疫情的爆发促使研究小组广泛而往往缺乏协调地开发了SARS-CoV-2的数学模型，以研究其传播并为控制措施提供信息。疫情初期迫切需要洞察力，这意味着早期的模型通常是简单的，或者是从现有的研究议程中重新利用的。我们小组主要使用基于代理的模型（ABMs）来研究细粒度的干预情景。这些高分辨率模型规模大、复杂，需要大量实证数据，并且通常比回答定性问题如“我们应该实施封锁吗？”所严格需要的更加详细。在极其传染病危机的早期阶段，特别是在明确的实证证据出现之前，更简单的模型更为合适。然而，随着更详细的实证证据的出现，以及政策决策变得更加细致和复杂，像我们这样的细粒度方法变得更有用。在本文件中，我们讨论了我们小组在建模疫情期间如何应对这一转变。模型者的角色通常包括几乎实时的分析，以及快速适应我们工具的巨大努力。我们经常在与大量证据的洪流赛跑的同时，努力同时进行学术研究和实时决策支持，在不利于这两者的条件下进行。通过反思我们应对疫情的经验以及从这些挑战中学到的东西，我们可以更好地为未来的需求做准备。 显示英文摘要

摘要： The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like "Should we lockdown?" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.