Big data for human security: The case of COVID-19

Cárdenas, Pedro; Ivrissimtzis, Ioannis; Obara, Boguslaw; Kureshi, Ibad; Theodoropoulos, Georgios

Abstract

The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities in several facets of society, most notably health, economy and public order. Measures to contain the pandemic by governments have occasionally met with increasing discontent from societies and have triggered social unrest, imposing serious threats to human security. Big Data Analytics can provide a powerful force multiplier to support policy and decision makers to contain the virus while at the same time dealing with such threats to human security. This paper presents the utilisation of a big data forecasting and analytics framework and its utilisation to deal with COVID-19 triggered social unrest. The paper is an extended version of paper Cárdenas et al. (2021) presented at the 2021 International Conference on Computational Science.

Citation

Cárdenas, P., Ivrissimtzis, I., Obara, B., Kureshi, I., & Theodoropoulos, G. (2022). Big data for human security: The case of COVID-19. Journal of Computational Science, 60, Article 101574. https://doi.org/10.1016/j.jocs.2022.101574

• Journal Article Type: Article
• Acceptance Date: Jan 21, 2022
• Online Publication Date: Feb 15, 2022
• Publication Date: 2022-04
• Deposit Date: Jun 30, 2022
• Publicly Available Date: Feb 15, 2023
• Journal: Journal of Computational Science
• Print ISSN: 1877-7503
• Electronic ISSN: 1877-7511
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 60
• Article Number: 101574
• DOI: https://doi.org/10.1016/j.jocs.2022.101574
• Public URL: https://durham-repository.worktribe.com/output/1202660