We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and by considering human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA.

中文翻译：

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具有测量延迟的隔室流行病学模型的安全关键控制

我们引入了一种方法，通过将流行病学模型视为控制系统，并将人为干预（例如隔离或社会疏远）视为控制输入，来确保安全防止传染病传播。我们考虑了一个代表最流行的流行病学模型形式的广义分区模型，我们设计了安全关键控制器，在将某些感兴趣的人群保持在规定的安全限制下的情况下，这些控制器正式保证安全进化。此外，我们讨论了源自潜伏期的测量延迟和测试延迟如何影响安全性，以及如何通过预测器反馈来补偿延迟。我们通过综合限制感染数量的积极干预政策来证明我们的结果，