Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) 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 8 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 society, in better understanding and effective management of the disease.
【저자키워드】 VOC 202012/01, COVID-19 symptoms, COVID-19 demographics impacts, rule mining in COVID-19, global deaths in COVID-19, patterns analysis in COVID-19 data, 【초록키워드】 COVID-19, coronavirus disease, COVID-19 severity, Symptom, progression, outcome, COVID-19 infection, outbreak, management, Algorithm, female, variations, disease spread, concern, Smokers, association, Predictive, Support, complex, attributes, attribute, approach, effective, identify, performed, globe, the disease, turn, processed, 【제목키워드】 COVID-19, Spread, demographic characteristics, Analysing,