ArticleChaos (Woodbury, N.Y.)2020
Cluster-based dual evolution for multivariate time series: Analyzing COVID-19.
Article in Chaos (Woodbury, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transmissibility and pathogenicity of the severe acute respiratory syndrome coronavirus 2: A systematic review and meta-analysis of secondary attack rate and asymptomatic infection.Journal of infection and public health · 2022Pooled it
- Clustering-based methodology for comparing multi-characteristic epidemiological dynamics with application to COVID-19 epidemiology in Europe.Royal Society open science · 2025Article
- Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID-19 patient analysis.Concurrency and computation : practice & experience · 2023Article
- Exploring COVID-19 transmission patterns and key factors during epidemics caused by three major strains in Asia.Journal of theoretical biology · 2023Article
- Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.Chaos, solitons, and fractals · 2022Article
- A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods.Expert systems with applications · 2022Article
- Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis.SN computer science · 2022Article
- COVID-19: average time from infection to death in Poland, USA, India and Germany.Quality & quantity · 2022Article
- Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020.BMJ open · 2021Article
- Trends in COVID-19 prevalence and mortality: A year in review.Physica D. Nonlinear phenomena · 2021Article
- Country transition index based on hierarchical clustering to predict next COVID-19 waves.Scientific reports · 2021Article
- Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19.Physica A · 2021Article
- Association between COVID-19 cases and international equity indices.Physica D. Nonlinear phenomena · 2021Article
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- Modeling the Global Dynamic Contagion of COVID-19.Frontiers in public health · 2021Article
- COVID-19 in the United States: Trajectories and second surge behavior.Chaos (Woodbury, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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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