A framework for data-driven solutions with covid-19 illustrations

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dc.contributor.authorMwitondi, Kassim S.
dc.contributor.authorSaid, Raed A.
dc.date.accessioned2021-12-05T16:58:38Z
dc.date.available2021-12-05T16:58:38Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Data Science Journal (2021), available online at: http://doi.org/10.5334/dsj-2021-036en_US
dc.description.abstractData–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research. © 2021, Ubiquity Press. All rights reserved.en_US
dc.description.sponsorshipCouncil for Scientific and Industrial Research, South Africa Council of Scientific and Industrial Research, Indiaen_US
dc.identifier.citationMwitondi, K. S., & Said, R. A. (2021). A framework for data-driven solutions with covid-19 illustrations. Data Science Journal, 20(1). http://doi.org/10.5334/dsj-2021-036en_US
dc.identifier.issn16831470
dc.identifier.urihttp://doi.org/10.5334/dsj-2021-036
dc.identifier.urihttp://hdl.handle.net/20.500.12519/473
dc.language.isoenen_US
dc.publisherUbiquity Pressen_US
dc.relationAuthors Affiliations : Mwitondi, K.S., Sheffield Hallam University, College of Business, Technology & Engineering, GB, United Kingdom; Said, R.A., Canadian University Dubai, Faculty of Management, United Arab Emirates
dc.relation.ispartofseriesData Science Journal;Volume 20, Issue 1
dc.rights.holderCopyright : © 2021, Ubiquity Press. All rights reserved.
dc.rights.uriCreative Common Attribution 4.0 International License (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subjectAnimationen_US
dc.subjectBig Dataen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectCOVID-19en_US
dc.subjectData Scienceen_US
dc.subjectSustainable Development Goals (SDG)en_US
dc.titleA framework for data-driven solutions with covid-19 illustrationsen_US
dc.typeArticleen_US
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