Meaningful Big Data Integration for a Global COVID-19 Strategy

Joao Pita Costa (Corresponding Author), Marko Grobelnik, Flavio Fuart, Luka Stopar, Gorka Epelde, Scott Fischaber, Piotr Poliwoda, Debbie Rankin, Jonathan Wallace, Michaela Black, Raymond 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, Juha Pajula, Adil Umer

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)


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.
Original languageEnglish
Article number9225216
Pages (from-to)51-61
JournalIEEE Computational Intelligence Magazine
Issue number4
Publication statusPublished - 15 Nov 2020
MoE publication typeA1 Journal article-refereed


Supported by the GYDRA Tool. European Union research fund ‘Big Data Supporting Public Health Policies


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