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EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization
The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we us...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692107/ https://www.ncbi.nlm.nih.gov/pubmed/38040776 http://dx.doi.org/10.1038/s41598-023-48484-9 |
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author | Mason, Lee Hicks, Blànaid Almeida, Jonas S. |
author_facet | Mason, Lee Hicks, Blànaid Almeida, Jonas S. |
author_sort | Mason, Lee |
collection | PubMed |
description | The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as ‘cluster embedding’ methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiVECS, a tool which allows the user to perform cluster embedding and explore the results using interactive visualization. EpiVECS is available as a privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs. |
format | Online Article Text |
id | pubmed-10692107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106921072023-12-03 EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization Mason, Lee Hicks, Blànaid Almeida, Jonas S. Sci Rep Article The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as ‘cluster embedding’ methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiVECS, a tool which allows the user to perform cluster embedding and explore the results using interactive visualization. EpiVECS is available as a privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs. Nature Publishing Group UK 2023-12-01 /pmc/articles/PMC10692107/ /pubmed/38040776 http://dx.doi.org/10.1038/s41598-023-48484-9 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mason, Lee Hicks, Blànaid Almeida, Jonas S. EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title | EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title_full | EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title_fullStr | EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title_full_unstemmed | EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title_short | EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
title_sort | epivecs: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692107/ https://www.ncbi.nlm.nih.gov/pubmed/38040776 http://dx.doi.org/10.1038/s41598-023-48484-9 |
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