University of Oulu

J. Pita Costa et al., "Meaningful Big Data Integration for a Global COVID-19 Strategy," in IEEE Computational Intelligence Magazine, vol. 15, no. 4, pp. 51-61, Nov. 2020, doi: 10.1109/MCI.2020.3019898

Meaningful big data integration for a global COVID-19 strategy

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Author: Costa, Joao Pita1; Grobelnik, Marko1; Fuart, Flavio1;
Organizations: 1Quintelligence & Jozef Stefan Institute, Slovenia
2Ulster University, UK
3KU Leuven, Belgium
4Vicomtech & Biodonostia, Spain
5Public Health England, UK
6University of Oulu, Finland
7Analytics Engines, UK
8Open University, UK
9Dublin City University, Ireland
10IBM, Ireland
11BIOEF, Spain
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-12-14


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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Series: IEEE computational intelligence magazine
ISSN: 1556-603X
ISSN-E: 1556-6048
ISSN-L: 1556-603X
Volume: 15
Issue: 4
Pages: 51 - 61
DOI: 10.1109/MCI.2020.3019898
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This project was funded by the European Union research fund ‘Big Data Supporting Public Health Policies,’ under GA No. 727721.
EU Grant Number: (727721) MIDAS - Meaningful Integration of Data, Analytics and Services
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