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Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium

INTRODUCTION: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In t...

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Autores principales: Natalia, Yessika Adelwin, Faes, Christel, Neyens, Thomas, Hammami, Naïma, Molenberghs, Geert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654974/
https://www.ncbi.nlm.nih.gov/pubmed/38026374
http://dx.doi.org/10.3389/fpubh.2023.1249141
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author Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Hammami, Naïma
Molenberghs, Geert
author_facet Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Hammami, Naïma
Molenberghs, Geert
author_sort Natalia, Yessika Adelwin
collection PubMed
description INTRODUCTION: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. METHODS: We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January – 31 December 2021 using canonical correlation analysis. RESULTS: Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. CONCLUSION: It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.
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spelling pubmed-106549742023-11-03 Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium Natalia, Yessika Adelwin Faes, Christel Neyens, Thomas Hammami, Naïma Molenberghs, Geert Front Public Health Public Health INTRODUCTION: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. METHODS: We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January – 31 December 2021 using canonical correlation analysis. RESULTS: Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. CONCLUSION: It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population. Frontiers Media S.A. 2023-11-03 /pmc/articles/PMC10654974/ /pubmed/38026374 http://dx.doi.org/10.3389/fpubh.2023.1249141 Text en Copyright © 2023 Natalia, Faes, Neyens, Hammami and Molenberghs. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Hammami, Naïma
Molenberghs, Geert
Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title_full Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title_fullStr Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title_full_unstemmed Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title_short Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
title_sort key risk factors associated with fractal dimension based geographical clustering of covid-19 data in the flemish and brussels region, belgium
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654974/
https://www.ncbi.nlm.nih.gov/pubmed/38026374
http://dx.doi.org/10.3389/fpubh.2023.1249141
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