Data-driven methods for present and future pandemics: Monitoring, modelling and managing. (2021)

• Record Type: Journal Article
• Authors: Alamo, Teodoro; G. Reina, Daniel; Millán Gata, Pablo; Preciado, Victor M.; Giordano, Giulia
• Is Part Of: Annual reviews in control. Volume 52(2021)
• Page Start: 448
• Page End: 464
• Publication Date: 2021
• Subjects: Pandemic control -- Epidemiological models -- Machine learning -- Forecasting -- Surveillance systems -- Epidemic control -- Optimal control -- Model predictive control; Automatic control -- Periodicals; Periodicals; 629.805
• DOI: 10.1016/j.arcontrol.2021.05.003
• Languages: English
• ISSNs: 1367-5788

Abstract

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.