使用分类规则挖掘和模式匹配分析全球人口统计特征对COVID-19传播的影响
Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching
摘要：
自2019年12月冠状病毒病（COVID-19）爆发以来，研究一直针对与COVID-19和关注变异体202012/01（VOC 202012/01）相关的多个方面，例如潜在症状和预测工具。然而，在对全球COVID-19感染的综合人口特征与不同性质之间的复杂关联进行建模方面，所做的工作有限。这项研究提出了一种智能方法来调查人口统计学属性与COVID-19全球变化之间的多维关联。我们从可靠来源收集多个人口统计属性和COVID-19感染数据（截至2021年1月8日），然后通过智能算法进行处理，以识别数据中的重要关联和模式。统计结果和专家报告显示，全球COVID-19严重程度与某些人口统计属性（例如女性吸烟者）结合其他属性时存在强烈关联。这些成果将有助于了解疾病传播及其进展的动态，从而支持决策者、医学专家和社会更好地了解和有效管理这一疾病。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
