This article describes two spatially explicit models created to allow experimentation with different societal responses to the COVID‐19 pandemic. We outline the work to date on modeling spatially explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters, and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalized models for mitigation provide significant advantages over a “one‐size‐fits‐all” approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.

中文翻译：

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用于探索 COVID-19 锁定策略的空间显式模型。

本文描述了创建的两个空间显式模型，以允许对 COVID-19 大流行的不同社会反应进行实验。我们概述了迄今为止在空间显式传染病建模方面的工作，并表明仍有一些空白需要填补。我们展示了地理区域而不是单一的国家方法如何可能为人口带来更好的结果。我们全面介绍了我们的模型如何运作，以及如何使用它们来探索传染病的许多不同方面，包括：尝试不同的封锁措施、地方之间的连通性、疾病集群的追踪以及使用改进的接触者追踪和隔离。我们提供了全面的结果，展示了这些模型在给定场景中的使用情况，并得出结论，明确区域化的缓解模式比“一刀切”的方法具有显着优势。我们已经公开了我们的模型及其数据，供其他人在他们自己的地区使用，希望为地理学家提供在这个困难时期发表意见所需的工具。