Monitoring Sustainable Development Goals Amidst COVID-19 Through Big Data, Deep Learning and Interdisciplinarity

MWITONDI, Kassim (2020). Monitoring Sustainable Development Goals Amidst COVID-19 Through Big Data, Deep Learning and Interdisciplinarity. In: International Symposium on Data Science 2020 "Global Collaboration on Data beyond Disciplines", Online, 23 Sep 2020 - 25 Sep 2020. Joint Support Centre for Data Science Research (ROIS-DS).

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Abstract

As the coronavirus disease 2019 (COVID–19) ravaged across the globe, in the first half of 2020, the world was once again reminded of the huge gaps in our knowledge, despite our current scientific and technological capacities. The pandemic has had a severe impact on our ways of life, and despite its devastating impact, it has presented us with an opportunity for paying greater attention to the challenges we face. It is in that context that we associate the fight against COVID-19 with monitoring Sustainable Development Goals (SDG). Considering each SDG as a source of Big Data, we present a generic framework for combining Big Data, machine learning and interdisciplinarity to address global challenges. The work delivers descriptive and prescriptive findings, using data visualisation and animation techniques, on the one hand, and predictive results, based on convolutional neural networks, on the other. The former is based on structured data on cases and deaths from COVID–19 obtained from the European Centre for Disease Prevention and Control (ECDC) and data on the impact of the pandemic on various aspects of life, obtained from the UK Office of National Statistics. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from Github and Kaggle. The results from both sets are presented in the form that resonates with cross disciplinary discussions, opening novel paths for interdisciplinary research in tackling global challenges.

Item Type:	Conference or Workshop Item (Keynote)
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	12 Oct 2020 12:19
Last Modified:	17 Mar 2021 22:00
URI:	https://shura.shu.ac.uk/id/eprint/27380

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