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• W4200236522 abstract "Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we analyse the world data of SARS-CoV-2 infection events, scrutinising two eight-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal universal patterns, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that then cluster according to similar shapes of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, cyclic trends are characteristic of the identified clusters, dominating large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic." @default.
• W4200236522 created "2021-12-31" @default.
• W4200236522 creator A5029273380 @default.
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• W4200236522 date "2021-12-21" @default.
• W4200236522 modified "2023-10-17" @default.
• W4200236522 title "Worldwide clustering and infection cycles as universal features of multiscale stochastic processes in the SARS-CoV-2 pandemic" @default.
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• W4200236522 doi "https://doi.org/10.1101/2021.12.20.21268095" @default.
• W4200236522 hasPublicationYear "2021" @default.
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