Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching
This work addresses the need for better understanding of COVID-19 spread dynamics to support policy makers and medical specialists, but it is incremental as it applies existing methods to new demographic data.
The study tackled the problem of modeling complex associations between combined demographic attributes and global variations in COVID-19 infections, using class rule mining and pattern matching on data up to January 8, 2021, and found strong associations, such as with female smokers when combined with other attributes.
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.