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A decomposition of waves in time series of data related to Covid-19, applied to study the role of Alpha variant in the spread of infection

Authors: Sparavigna, Amelia Carolina;

A decomposition of waves in time series of data related to Covid-19, applied to study the role of Alpha variant in the spread of infection

Abstract

Here it is proposed a decomposition in components of the ���waves��� which appear in the time series of data related to Covid-19 pandemic. The decomposition is based on functions of ��-statistics; in particular the ��-Weibull is used. Fitted data are those of the ���waves��� ranging from August 2020 to April 2021 in the United Kingdom, from September 2020 to May 2021 in Ireland, and from September 2020 to June 2021 in Italy. For the United Kingdom, the time series of daily infection shows a wave composed by two peaks. Among the many factors involved in the spread of infection, it seems that, in driving the onset of the second peak, the main role was played by the emergence of Alpha variant of Sars-Cov-2. For what concerns data from Ireland, if we decompose the analysed ���wave��� in three components, we can observed that for one of them the role of the Alpha variant was relevant for sure. In the case of Italy, the effect is less evident, because it was spread over a larger time interval.

October 25, 2021, first layout ��� October 28, 2021, added discussion ��� October 29, 2021, added analysis of data concerning Ireland ��� October 30, 2021, added analysis of data concerning London

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Keywords

probability distribution function, Threshold, power-law tail, Variants of Sars-CoV-2, Gaussian pdf, heavy-tail, Weibull functions, Alpha Variant, Time-series Decomposition, kappa-Weibull functions, Covid-19, B.1.1.7

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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