期刊
ANNUAL REVIEWS IN CONTROL
卷 52, 期 -, 页码 448-464出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.arcontrol.2021.05.003
关键词
Pandemic control; Epidemiological models; Machine learning; Forecasting; Surveillance systems; Epidemic control; Optimal control; Model predictive control
资金
- Agencia Estatal de Investigacion (AEI)-Spain [PID2019-106212RB-C41/AEI/10.13039/501100011033]
- US National Science Foundation [CAREER-ECCS-1651433, NSF-III-200884556]
- Strategic Grant MOSES at the University of Trento
This survey delves into the role of data-driven methodologies in pandemic modeling and control, presenting a roadmap from data access to epidemic control, analyzing the challenges and potential of data-driven strategies. The aim is to integrate various disciplines like data science, epidemiology, and systems-and-control theory for a comprehensive approach to epidemic analysis.
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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