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Updated: Oct 10, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative
Stefana Cioban1,2, Codruta Mare1,2
1Babes-Bolyai University, Faculty of Economics and Business Administration, Department of Statistics-Forecasts-Mathematics, 58-60, Teodor Mihali str., 400591, Cluj-Napoca, Romania.
Abstract:
Spatial analyses related to Covid-19 have been so far conducted at county, regional or national level, without a thorough assessment at the continuous local level of administrative-territorial units like cities, towns, or communes. To address this gap, we employ daily data on the infection rate provided for Romanian administrative units from March to May 2021. Using the global and local Moran I spatial autocorrelation coefficients, we identify significant clustering processes in the Covid-19 infection rate. Additional analysis based on spatially smoothed rate maps and spatial regressions prove that this clustering pattern is influenced by the development level of localities, proxied by unemployment rate and Local Human Development Index. Results show the features of the 3rd wave in Romania, characterized by a quadratic trend.
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