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Analysing the Impact of Global Demographic Characteristics over the COVID-19 Spread Using Class Rule Mining and Pattern Matching

Khan, W, Hussain, A, Khan, SA, Al-Jumailey, M, Nawaz, R and Liatsis, P (2021) Analysing the Impact of Global Demographic Characteristics over the COVID-19 Spread Using Class Rule Mining and Pattern Matching. Royal Society Open Science, 8 (1). ISSN 2054-5703

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Since the Coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 08 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts’ reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g., female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and the society, in better understanding and effective management of the disease.

Item Type: Article
Uncontrolled Keywords: VOC 202012/01; COVID-19 Demographics Impacts; COVID-19 Symptoms; COVID-19 variants; Rule Mining in COVID-19; Global Deaths in COVID-19; Patterns Analysis in COVID-19 data
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QR Microbiology > QR355 Virology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Computer Science & Mathematics
Publisher: The Royal Society
Date Deposited: 28 Jan 2021 11:22
Last Modified: 11 Mar 2022 17:15
DOI or ID number: 10.1098/rsos.201823
URI: https://researchonline.ljmu.ac.uk/id/eprint/14332
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