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This document analyzes the role of data-driven methodologies in Covid-19pandemic. We provide a SWOT analysis and a roadmap that goes from the access todata sources to the final decision-making step. We aim to review the availablemethodologies while anticipating the difficulties and challenges in thedevelopment of data-driven strategies to combat the Covid-19 pandemic. A3M-analysis is presented: Monitoring, Modelling and Making decisions. The focusis on the potential of well-known datadriven schemes to address differentchallenges raised by the pandemic: i) monitoring and forecasting the spread ofthe epidemic; (ii) assessing the effectiveness of government decisions; (iii)making timely decisions. Each step of the roadmap is detailed through a reviewof consolidated theoretical results and their potential application in theCovid-19 context. When possible, we provide examples of their applications onpast or present epidemics. We do not provide an exhaustive enumeration ofmethodologies, algorithms and applications. We do try to serve as a bridgebetween different disciplines required to provide a holistic approach to theepidemic: data science, epidemiology, controltheory, etc. That is, we highlighteffective data-driven methodologies that have been shown to be successful inother contexts and that have potential application in the different steps ofthe proposed roadmap. To make this document more functional and adapted to thespecifics of each discipline, we encourage researchers and practitioners toprovide feedback. We will update this document regularly.

Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory