  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.
