Meaningful Big Data Integration for a Global COVID-19 Strategy
Open accessAbstract
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.
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The unprecedented increase of data volumes associated with advances of analytic techniques empowered from AI has promoted big data analysis, searching, and mining activities for fighting COVID-19.
“Moreover, the unprecedented increase of data volumes associated with advances of analytic techniques empowered from AI has promoted big data analysis, big data searching and big data mining activities for fighting COVID-19 [166].”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [166]
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- Authors
- Pita Costa, J., Grobelnik, M., Fuart, F., Stopar, L., Epelde, G., Fischaber, S., Poliwoda, P., Rankin, D., Wallace, J., Black, M., Bond, R., Mulvenna, M., Weston, D., Carlin, P., Bilbao, R., Nikolic, G., Shi, X., De Moor, B., Pikkarainen, M., Paakkonen, J., Staines, A., Connolly, R., & Davis, P.
- Affiliations
- Jožef Stefan Institute (SI)
- Vicomtech (ES)
- Analytics Engines (United Kingdom) (GB)
- IBM (Ireland) (IE)
- University of Ulster (GB)
- Public Health England (GB)
- The Open University (GB)
- Bioef - Fundación Vasca de Innovación e Investigación Sanitarias (ES)
- KU Leuven (BE)
- University of Oulu (FI)
- Dublin City University (IE)
- ORCID
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2020-11
- Volume
- 15
- Issue
- 4
- Pages
- 51-61
- ISSN
- 1556-603X (ISSN-L)
- 1556-6048
- OpenAlex
- W3084396173
- Cited by
- 29
- References
- 21
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 8 · 2020: 1
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Computational Intelligence Magazine (publishedVersion)
- Ulster University Research Portal (Ulster University) (acceptedVersion, other-oa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- Dublin City University Open Access Institutional Repository (Dublin City University) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Funders
- European Commission (727721)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- COVID-19 pandemic, big data integration, heterogeneous data sources, open data, social data, data governance, ethical review, privacy, public health monitoring, pandemic preparedness, public health emergencies, data analytics
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