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.

Data-driven methods for present and future pandemics: Monitoring, modelling and managing / Alamo, Teodoro; Reina, Daniel G.; Millán Gata, Pablo; Preciado, Victor M.; Giordano, Giulia. - In: ANNUAL REVIEWS IN CONTROL. - ISSN 1367-5788. - 52:(2021), pp. 448-464. [10.1016/j.arcontrol.2021.05.003]

Data-driven methods for present and future pandemics: Monitoring, modelling and managing

Giordano, Giulia
2021-01-01

Abstract

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.
2021
Alamo, Teodoro; Reina, Daniel G.; Millán Gata, Pablo; Preciado, Victor M.; Giordano, Giulia
Data-driven methods for present and future pandemics: Monitoring, modelling and managing / Alamo, Teodoro; Reina, Daniel G.; Millán Gata, Pablo; Preciado, Victor M.; Giordano, Giulia. - In: ANNUAL REVIEWS IN CONTROL. - ISSN 1367-5788. - 52:(2021), pp. 448-464. [10.1016/j.arcontrol.2021.05.003]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/315603
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