  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 the epidemic, such as data science, epidemiology, or 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 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, as well as their
potential application to future epidemics.
