1 research outputs found
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
This document analyzes the role of data-driven methodologies in Covid-19
pandemic. We provide a SWOT analysis and a roadmap that goes from the access to
data sources to the final decision-making step. We aim to review the available
methodologies while anticipating the difficulties and challenges in the
development of data-driven strategies to combat the Covid-19 pandemic. A
3M-analysis is presented: Monitoring, Modelling and Making decisions. The focus
is on the potential of well-known datadriven schemes to address different
challenges raised by the pandemic: i) monitoring and forecasting the spread of
the epidemic; (ii) assessing the effectiveness of government decisions; (iii)
making timely decisions. Each step of the roadmap is detailed through a review
of consolidated theoretical results and their potential application in the
Covid-19 context. When possible, we provide examples of their applications on
past or present epidemics. We do not provide an exhaustive enumeration of
methodologies, algorithms and applications. We do try to serve as a bridge
between different disciplines required to provide a holistic approach to the
epidemic: data science, epidemiology, controltheory, etc. That is, we highlight
effective data-driven methodologies that have been shown to be successful in
other contexts and that have potential application in the different steps of
the proposed roadmap. To make this document more functional and adapted to the
specifics of each discipline, we encourage researchers and practitioners to
provide feedback. We will update this document regularly