Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

TL;DR

This paper investigates complex relationships between demographic factors and COVID-19 spread worldwide using class rule mining and pattern matching, revealing significant associations that can inform policy and health strategies.

Contribution

It introduces an intelligent approach combining class rule mining and pattern matching to model multi-dimensional demographic associations with COVID-19 variations globally.

Findings

Strong associations between demographic attributes and COVID-19 severity.

Identification of specific demographic groups linked to higher infection risks.

Enhanced understanding of disease dynamics for better policy formulation.

Abstract

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.