The unfolding coronavirus (COVID-19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID-19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard while allowing minimally impacted local communities to conduct “business as usual” and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, while keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons.

中文翻译：

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使用自组织地图预测和控制自然灾害和流行病

正在蔓延的冠状病毒 (COVID-19) 大流行凸显了全球对强大的预测和遏制工具和策略的需求。COVID-19 继续引起广泛的经济和社会动荡，虽然当前的重点是尽量减少疾病的传播和部署一系列疫苗来拯救生命，但注意力很快就会转向未来的验证。与此一致，本文提出了一种可用于流行病和自然灾害的预测和遏制模型。它结合了选择性封锁和保护警戒线，以迅速遏制危害，同时允许受影响最小的当地社区“照常营业”和/或为受影响严重的地区提供支持。基于自组织地图的灵活、易于使用的数据分析模型，旨在促进政府和组织轻松做出决策。使用英国 (GB) 的公开数据进行的比较测试表明，通过使用拟议的预测和遏制策略，可以降低峰值感染率，同时保持几个地区（高达 25% 的英国议会选区）在经济上在保护警戒线内活跃。