Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays

被引：0

作者：

Molnar, Tamas G. ^{[1
]}

Singletary, Andrew W. ^{[2
]}

Orosz, Gabor ^{[1
,3
]}

Ames, Aaron D. ^{[2
]}

机构：

[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA

[2] CALTECH, Dept Mech & Civil Engn, Pasadena, CA 91125 USA

[3] Univ Michigan, Dept Civil & Environm Engn, Ann Arbor, MI 48109 USA

来源：

2021 AMERICAN CONTROL CONFERENCE (ACC) | 2021年

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1052 / 1057

页数：6

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