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Fractal dimension based geographical clustering of COVID-19 time series data

Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flande...

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Autores principales: Natalia, Yessika Adelwin, Faes, Christel, Neyens, Thomas, Chys, Pieter, Hammami, Naïma, Molenberghs, Geert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016183/
https://www.ncbi.nlm.nih.gov/pubmed/36922616
http://dx.doi.org/10.1038/s41598-023-30948-7
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author Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Chys, Pieter
Hammami, Naïma
Molenberghs, Geert
author_facet Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Chys, Pieter
Hammami, Naïma
Molenberghs, Geert
author_sort Natalia, Yessika Adelwin
collection PubMed
description Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak.
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spelling pubmed-100161832023-03-15 Fractal dimension based geographical clustering of COVID-19 time series data Natalia, Yessika Adelwin Faes, Christel Neyens, Thomas Chys, Pieter Hammami, Naïma Molenberghs, Geert Sci Rep Article Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak. Nature Publishing Group UK 2023-03-15 /pmc/articles/PMC10016183/ /pubmed/36922616 http://dx.doi.org/10.1038/s41598-023-30948-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Natalia, Yessika Adelwin
Faes, Christel
Neyens, Thomas
Chys, Pieter
Hammami, Naïma
Molenberghs, Geert
Fractal dimension based geographical clustering of COVID-19 time series data
title Fractal dimension based geographical clustering of COVID-19 time series data
title_full Fractal dimension based geographical clustering of COVID-19 time series data
title_fullStr Fractal dimension based geographical clustering of COVID-19 time series data
title_full_unstemmed Fractal dimension based geographical clustering of COVID-19 time series data
title_short Fractal dimension based geographical clustering of COVID-19 time series data
title_sort fractal dimension based geographical clustering of covid-19 time series data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016183/
https://www.ncbi.nlm.nih.gov/pubmed/36922616
http://dx.doi.org/10.1038/s41598-023-30948-7
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