Meaningful Big Data Integration For a Global COVID-19 Strategy

Joao Pita Costa, Marko Grobelnik, Flavio Fuart, Luka Stopar, Gorka Epelde, Scott Fischaber, Piotr Poliwoda, Debbie Rankin, J. G. Wallace, Michaela Black, RR Bond, Maurice Mulvenna, Dale Weston, Paul Carlin, Roberto Bilbao, Gorana Nikolic, Xi Shi, Bart De Moor, Minna Pikkarainen, Jarmo PääkkönenAnthony Staines, Regina Connolly, Paul Davis

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
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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 authorities 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.
Original languageEnglish
Article number9225216
Pages (from-to)51-61
Number of pages11
JournalIEEE Computational Intelligence Magazine
Issue number4
Early online date15 Oct 2020
Publication statusPublished (in print/issue) - 30 Nov 2020

Bibliographical note

Funding Information:
Supported by the GYDRA Tool

Funding Information:
This project was funded by the European Union research fund ‘Big Data Supporting Public Health Policies,’ under GA No. 727721.

Publisher Copyright:
© 2005-2012 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • COVID-19
  • Pandemics
  • Public healthcare
  • Big Data
  • Data analysis
  • Monitoring
  • Globalization


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