Data-driven methods for present and future pandemics
Monitoring, modelling and managing
Teodoro Alamo (Universidad de Sevilla, Escuela Técnica Superior de Ingeniería)
Daniel G. Reina (Universidad de Sevilla, Escuela Técnica Superior de Ingeniería)
Pablo Millán Gata (Loyola University Andalusia)
Victor M. Preciado (University of Pennsylvania)
G. Giordano (Università di Trento)
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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.
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