Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and business closures. This article demonstrates how evolutionary AI can be used to facilitate the next step, i.e., determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest creative ways in which restrictions can be implemented softly, e.g., by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

中文翻译：

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从预测到处方：COVID-19 大流行中非药物干预措施的进化优化


已经开发了几种模型来预测 COVID-19 大流行如何传播，以及如何通过非药物干预措施（例如社交距离限制以及学校和企业关闭）来遏制疫情。本文演示了如何使用进化人工智能来促进下一步，即自动确定最有效的干预策略。通过进化替代辅助处方，可以生成大量候选策略并用预测模型对其进行评估。原则上，可以针对不同国家和地区制定策略，平衡遏制疫情的需要和尽量减少其经济影响的需要。早期实验表明，工作场所和学校的限制是最重要的，需要仔细设计。他们还证明，解除限制的结果可能不可靠，并提出了可以温和地实施限制的创造性方法，例如随着时间的推移交替实施限制。随着更多数据的出现，该方法在应对 COVID-19 以及未来可能出现的流行病方面会越来越有用。