期刊
ANNUAL REVIEWS IN CONTROL
卷 50, 期 -, 页码 345-360出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.arcontrol.2020.09.003
关键词
Epidemic processes; Network-dependent spread; COVID-19; Parameter estimation; Stability analysis; Networked control systems; Nonlinear systems
资金
- C3.ai Digital Transformation Institute - C3.ai Inc.
- Microsoft Corporation, United States of America
- Jump ARCHES endowment through the Health Care Engineering Systems Center of the University of Illinois at Urbana-Champaign, United States of America
- National Science Foundation, United States of America, grant NSF-CNS [2028738]
- National Science Foundation, United States of America, grant NSF-ECCS [2032258, 2032321]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [2028738] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [2032258] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [2032321] Funding Source: National Science Foundation
We present and discuss a variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes. We first present traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs. Then we consider models driven by similar principles, but involving non-trivial networks where spreading occurs between connected nodes. We present stability analysis results for selected models from both classes, as well as simple least squares approaches for estimating the spreading parameters of the virus from data for each basic networked model structure. We also provide some simulation models. The paper should serve as a succinct, accessible guide for systems and control research efforts toward understanding and combating COVID-19 and future pandemics.
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