Spatially explicit models for exploring COVID-19 lockdown strategies
Spatially explicit models for exploring COVID-19 lockdown strategies
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DOI：
10.1111/tgis.12660
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发表时间：
2020-06-15
影响因子：
2.4
通讯作者：
Adams, Benjamin
中科院分区：
文献类型：
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作者：
O'Sullivan, David;Gahegan, Mark;Adams, Benjamin
本文描述了两个空间明确的模型，这些模型是为了对COVID-19大流行的不同社会反应进行实验而创建的。我们概述了迄今为止在空间显性传染病建模方面的工作，并表明仍有重要的空白需要填补。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是如何运作的，以及如何使用它们来探索传染的许多不同方面，包括：试验不同的封锁措施，地方之间的连接，疾病聚集的追踪，以及使用改进的接触者追踪和隔离。我们提供了综合结果，显示了这些模型在特定情景下的使用情况，并得出结论，明确的区域化缓解模型比“一刀切”的方法具有显著优势。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。
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.