Fractal dimension based geographical clustering of COVID-19 time series data.
Sci Rep
; 13(1): 4322, 2023 03 15.
Article
in English
| MEDLINE | ID: covidwho-2273763
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Fractals
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
Limits:
Humans
Country/Region as subject:
Europa
Language:
English
Journal:
Sci Rep
Year:
2023
Document Type:
Article
Affiliation country:
S41598-023-30948-7
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