具有测量延迟的分区流行病学模型的安全关键控制
Safety-Critical Control of Compartmental Epidemiological Models With Measurement Delays.
作者信息
Molnar Tamas G, Singletary Andrew W, Orosz Gabor, Ames Aaron D
机构信息
Department of Mechanical EngineeringUniversity of Michigan Ann Arbor MI 48109 USA.
Department of Mechanical and Civil EngineeringCalifornia Institute of Technology Pasadena CA 91125 USA.
出版信息
IEEE Control Syst Lett. 2020 Nov 26;5(5):1537-1542. doi: 10.1109/LCSYS.2020.3040948. eCollection 2021 Nov.
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and 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.
我们引入一种方法，通过将流行病学模型视为控制系统，并将人类干预措施（如隔离或社交距离）视为控制输入，来确保预防传染病传播的安全性。我们考虑一个广义的 compartments 模型，它代表了最流行的流行病学模型的形式，并设计安全关键控制器，以正式保证在将某些感兴趣的人群保持在规定的安全限度内的情况下安全演变。此外，我们讨论了由潜伏期和检测延迟引起的测量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。我们通过合成主动干预策略来证明我们的结果，这些策略为捕捉 COVID-19 在美国传播情况的流行病学模型限制了感染、住院和死亡人数。
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