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Meaningful big data integration for a global COVID-19 strategy

Costa, Joao Pita orcid logoORCID: 0000-0001-5745-1302, Grobelnik, Marko orcid logoORCID: 0000-0002-5179-415X, Fuart, Flavio, Stopar, Luka, Epelde, Gorka orcid logoORCID: 0000-0002-5179-415X, Fischaber, Scott, Poliwoda, Piotr, Rankin, Debbie, Wallace, Jonathan orcid logoORCID: 0000-0002-8415-4001, Black, Michaela, Bond, Raymond R. orcid logoORCID: 0000-0002-1078-2232, Mulvenna, Maurice orcid logoORCID: 0000-0002-1554-0785, Weston, Dale, Carlin, Paul, Bilbao, Roberto orcid logoORCID: 0000-0002-1199-0420, Nikolic, Gorana, Shi, Xi, De Moor, Bart orcid logoORCID: 0000-0003-3908-7150, Pikkarainen, Minna, Pääkkönen, Jarmo orcid logoORCID: 0000-0002-7555-4134, Staines, Anthony orcid logoORCID: 0000-0001-9161-1357, Connolly, Regina orcid logoORCID: 0000-0003-3196-2889 and Davis, Paul (2020) Meaningful big data integration for a global COVID-19 strategy. IEEE Computational Intelligence Magazine, 15 (4). pp. 51-61. ISSN 1556-603X

Abstract
With the rapid spread of the COVID-19 pandemic, the novel Meaningful Integration of Data Analytics and Services (MIDAS) platform quickly demonstrates its value, relevance and transferability to this new global crisis. The MIDAS platform enables the connection of a large number of isolated heterogeneous data sources, and combines rich datasets including open and social data, ingesting and preparing these for the application of analytics, monitoring and research tools. These platforms will assist public health author ities in: (i) better understanding the disease and its impact; (ii) monitoring the different aspects of the evolution of the pandemic across a diverse range of groups; (iii) contributing to improved resilience against the impacts of this global crisis; and (iv) enhancing preparedness for future public health emergencies. The model of governance and ethical review, incorporated and defined within MIDAS, also addresses the complex privacy and ethical issues that the developing pandemic has highlighted, allowing oversight and scrutiny of more and richer data sources by users of the system.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:COVID-19; Pandemics; Public healthcare; Big Data; Data analysis; Monitoring
Subjects:Business > Innovation
Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Information retrieval
Computer Science > Information technology
Computer Science > Interactive computer systems
Social Sciences > Globalization
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
DCU Faculties and Schools > Faculty of Science and Health > School of Nursing, Psychotherapy & Community Health
Publisher:IEEE
Official URL:https://dx.doi.org/ 10.1109/MCI.2020.3019898
Copyright Information:© 2020 IEEE. Open Access.(CC-BY-4.0)
Funders:European Union research fund ‘Big Data Supporting Public Health Policies,’ under GA No. 727721.
ID Code:25931
Deposited On:31 May 2021 16:30 by Regina Connolly . Last Modified 05 Nov 2021 17:25
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