  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.
