ArticleRoyal Society open science2021
Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Evaluating fairness of machine learning prediction of prolonged wait times in Emergency Department with Interpretable eXtreme gradient boosting.PLOS digital health · 2025Article
- How National Culture Influences the Speed of COVID-19 Spread: Three Cross-Cultural Studies.Cross-cultural research : official journal of the Society for Cross-Cultural Research · 2023Article
- COVID-19 Vaccination and Mental Stress within Diverse Sociodemographic Groups.International journal of environmental research and public health · 2022Article
- Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Identifying homogeneous subgroups of patients and important features: a topological machine learning approach.BMC bioinformatics · 2021Article
- Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach.International journal of hygiene and environmental health · 2021Article
- The relationship between non-motor features and weight-loss in the premanifest stage of Huntington's disease.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.