2020/06/01 by Teodoro Álamo, Teodoro Alamo, D. G. Reina +5 · 18 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Medicine · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Artificial intelligence #Big data #Business #COVID-19 epidemiological studies #Computer science #Context (archaeology) #Control (management) #Coronavirus disease 2019 (COVID-19) #Data Analysis #Data mining #Data science #Data-Driven Disease Surveillance #Engineering #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Geography #Infectious disease (medical specialty) #Machine Learning (cs.LG) #Management science #Network Security and Intrusion Detection #Operations research #Pandemic #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Process management #Risk analysis (engineering) #SWOT analysis #Statistics and Probability (physics.data-an) #cs.LG #physics.data-an #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.48550/arxiv.2006.01731
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/06/01 · arxiv created 2020/06/10 · arxiv updated 2020/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.