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Cluster-based dual evolution for multivariate time series: Analyzing COVID-19

This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries’ cluster...

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Detalles Bibliográficos
Autores principales: James, Nick, Menzies, Max
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328914/
https://www.ncbi.nlm.nih.gov/pubmed/32611104
http://dx.doi.org/10.1063/5.0013156
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author James, Nick
Menzies, Max
author_facet James, Nick
Menzies, Max
author_sort James, Nick
collection PubMed
description This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries’ cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country’s COVID-19 mortality rate.
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spelling pubmed-73289142020-07-03 Cluster-based dual evolution for multivariate time series: Analyzing COVID-19 James, Nick Menzies, Max Chaos Fast Track This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their cases and death counts. The total number of clusters and individual countries’ cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country’s COVID-19 mortality rate. AIP Publishing LLC 2020-06 2020-06-30 /pmc/articles/PMC7328914/ /pubmed/32611104 http://dx.doi.org/10.1063/5.0013156 Text en © 2020 Author(s). 1054-1500/2020/30(6)/061108/10 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Fast Track
James, Nick
Menzies, Max
Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title_full Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title_fullStr Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title_full_unstemmed Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title_short Cluster-based dual evolution for multivariate time series: Analyzing COVID-19
title_sort cluster-based dual evolution for multivariate time series: analyzing covid-19
topic Fast Track
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328914/
https://www.ncbi.nlm.nih.gov/pubmed/32611104
http://dx.doi.org/10.1063/5.0013156
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