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Fractal dimension based geographical clustering of COVID-19 time series data.
Sci Rep. 2023 Mar 15; 13(1):4322.SR

Abstract

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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Authors+Show Affiliations

Natalia YA
I-BioStat, Data Science Institute, Hasselt University, 3500, Hasselt, Belgium. yessikaadelwin.natalia@uhasselt.be.
Faes C
I-BioStat, Data Science Institute, Hasselt University, 3500, Hasselt, Belgium.
Neyens T
I-BioStat, Data Science Institute, Hasselt University, 3500, Hasselt, Belgium. I-BioStat, KU Leuven, 3000, Leuven, Belgium.
Chys P
Team Infection Prevention and Vaccination, Agency for Care and Health, 1030, Brussels, Belgium.
Hammami N
Team Infection Prevention and Vaccination, Agency for Care and Health, 1030, Brussels, Belgium.
Molenberghs G
I-BioStat, Data Science Institute, Hasselt University, 3500, Hasselt, Belgium. I-BioStat, KU Leuven, 3000, Leuven, Belgium.

MeSH

HumansFractalsTime FactorsCOVID-19GeographyBelgium

Pub Type(s)

Journal Article

Language

eng

PubMed ID

36922616

Citation

Natalia, Yessika Adelwin, et al. "Fractal Dimension Based Geographical Clustering of COVID-19 Time Series Data." Scientific Reports, vol. 13, no. 1, 2023, p. 4322.
Natalia YA, Faes C, Neyens T, et al. Fractal dimension based geographical clustering of COVID-19 time series data. Sci Rep. 2023;13(1):4322.
Natalia, Y. A., Faes, C., Neyens, T., Chys, P., Hammami, N., & Molenberghs, G. (2023). Fractal dimension based geographical clustering of COVID-19 time series data. Scientific Reports, 13(1), 4322. https://doi.org/10.1038/s41598-023-30948-7
Natalia YA, et al. Fractal Dimension Based Geographical Clustering of COVID-19 Time Series Data. Sci Rep. 2023 Mar 15;13(1):4322. PubMed PMID: 36922616.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Fractal dimension based geographical clustering of COVID-19 time series data. AU - Natalia,Yessika Adelwin, AU - Faes,Christel, AU - Neyens,Thomas, AU - Chys,Pieter, AU - Hammami,Naïma, AU - Molenberghs,Geert, Y1 - 2023/03/15/ PY - 2022/7/27/received PY - 2023/3/3/accepted PY - 2023/3/16/entrez PY - 2023/3/17/pubmed PY - 2023/3/21/medline SP - 4322 EP - 4322 JF - Scientific reports JO - Sci Rep VL - 13 IS - 1 N2 - 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. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/36922616/Fractal_dimension_based_geographical_clustering_of_COVID_19_time_series_data_ DB - PRIME DP - Unbound Medicine ER -