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

do Espirito Santo, Jonatas Silva, da Conceição, Jackson Santos, da Costa, Lilia Carolina Carneiro, Fiaccone, Rosemeire Leovigildo, Barreto, Marcos ORCID: 0000-0002-7818-1855, Ichihara, Maria Yury and Ara, Anderson (2024) K-means DTW Barycenter Averaging: a clustering analysis of COVID-19 cases and deaths on the Brazilian federal units. International Journal of Data Science and Analytics. ISSN 2364-415X

Text (K-means DTW Barycenter Averaging A clustering analysis of COVID-19 cases and deaths on the Brazilian federal units) - Accepted Version Repository staff only until 14 April 2025. Download (2MB) | Request a copy

• Scopus publication
• Author

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.

Item Type:	Article
Official URL:	https://link.springer.com/journal/41060
Additional Information:	© 2024 The Author(s), under exclusive licence to Springer Nature Switzerland
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine H Social Sciences > HV Social pathology. Social and public welfare. Criminology
Date Deposited:	25 Apr 2024 15:21
Last Modified:	03 May 2024 09:15
URI:	http://eprints.lse.ac.uk/id/eprint/122799

Actions (login required)

View Item