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K-means DTW Barycenter Averaging: a clustering analysis of COVID-19 cases and deaths on the Brazilian federal units

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Abstract

A challenge faced while monitoring the COVID-19 pandemic in Brazil is the identification of patterns of incidence and mortality, which can help prioritize interventions to avoid excessive disease transmission and associated deaths. This study aimed to identify epidemiological patterns concerning the evolution of the pandemic among Brazilian federal units (states). The proposed methodology is based on a combination of non-hierarchical k-means clustering and dynamic time warping (DTW), used to measure distances among time series, with the subsequent use of the DTW Barycenter Averaging (DBA) algorithm to calculate cluster centroids for time series of variable lengths. The dataset used is a time series consisting of the number of new cases and deaths per epidemiological week, and the number of cumulative cases and deaths until a given epidemiological week for each of the 27 Brazilian federal units. Six groups of Brazilian federation units were formed based on the similarities between the prevalence and incidence curves. The results demonstrate efficiency with respect to the characterization of both COVID-19 cases and rates of mortality.

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Acknowledgements

This article is the result of part of the project entitled “Avaliação dos efeitos das desigualdades sociais na pandemia COVID-19 em país de baixa e média renda,” (Assessment of the effects of social inequalities during the COVID-19 pandemic in low- and middle-income countries) carried out by the Center for Integration of Data and Knowledge for Health, linked to the Gonçalo Muniz Institute, Oswaldo Cruz Foundation (CIDACS/IGM-FIOCRUZ). This project was funded by Health Data Research United Kingdom (HDR-UK) and received support from the Bill and Melinda Gates Foundation.

Funding

This project was funded by Health Data Research United Kingdom (HDR-UK) and received support from the Bill and Melinda Gates Foundation.

Author information

Author notes

  1. Jackson Santos da Conceição, Lilia Carolina Carneiro da Costa, Rosemeire Leovigildo Fiaccone, Marcos Ennes Barreto, Maria Yury Ichihara and Anderson Ara have contributed equally to this work.

Authors and Affiliations

  1. Centre of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, 41745-715, Brazil

    Jonatas Silva do Espirito Santo, Jackson Santos da Conceição, Lilia Carolina Carneiro da Costa, Rosemeire Leovigildo Fiaccone, Marcos Ennes Barreto, Maria Yury Ichihara & Anderson Ara

  2. Superintendence of Economic and Social Studies of Bahia, Government of bahia, Salvador, Bahia, 41745-002, Brazil

    Jonatas Silva do Espirito Santo & Jackson Santos da Conceição

  3. Department of Statistics, Federal University of Bahia, Salvador, Bahia, 40170-110, Brazil

    Lilia Carolina Carneiro da Costa & Rosemeire Leovigildo Fiaccone

  4. Data Science Institute, London School of Economics and Political Science, London, Greater London, 610101, England

    Marcos Ennes Barreto

  5. Department of Statistics, Federal University of Paraná, Curitiba, Paraná, 81531-980, Brazil

    Anderson Ara

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  1. Jonatas Silva do Espirito Santo
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Contributions

All authors played essential roles throughout the development of this work, actively contributing from the initial planning phase of the study to the completion of the article’s writing. Jonatas Silva do Espirito Santo and Jackson Santos da Conceição were responsible for creating the figures, in collaboration with Lilia Carolina Carneiro da Costa, Rosemeire Leovigildo Fiaccone, and Anderson Ara, who drafted the manuscript. The critical review of the article and final approval were conducted by Maria Yury Ichihara, Marcos Ennes Barreto, and all other authors.

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Correspondence to Jonatas Silva do Espirito Santo.

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do Espirito Santo, J.S., da Conceição, J.S., da Costa, L.C.C. et al. K-means DTW Barycenter Averaging: a clustering analysis of COVID-19 cases and deaths on the Brazilian federal units. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00542-9

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