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Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread

The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the...

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Detalles Bibliográficos
Autores principales: Ashouri, Mahsa, Phoa, Frederick Kin Hing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246234/
https://www.ncbi.nlm.nih.gov/pubmed/35771759
http://dx.doi.org/10.1371/journal.pone.0265477
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author Ashouri, Mahsa
Phoa, Frederick Kin Hing
author_facet Ashouri, Mahsa
Phoa, Frederick Kin Hing
author_sort Ashouri, Mahsa
collection PubMed
description The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results using the Ordinary Least Square (OLS) method. In this OLS model, we apply a model produced by the MOB tree to forecast all series in each cluster. Our user-friendly parametric forecasting method is computationally cheap. A web app based on R’s Shiny App makes it easier for practitioners to find clustering and forecasting results while choosing different parameters such as domain-relevant attributes. These results could help in determining the spread pattern and be utilized by medical researchers.
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spelling pubmed-92462342022-07-01 Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread Ashouri, Mahsa Phoa, Frederick Kin Hing PLoS One Research Article The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results using the Ordinary Least Square (OLS) method. In this OLS model, we apply a model produced by the MOB tree to forecast all series in each cluster. Our user-friendly parametric forecasting method is computationally cheap. A web app based on R’s Shiny App makes it easier for practitioners to find clustering and forecasting results while choosing different parameters such as domain-relevant attributes. These results could help in determining the spread pattern and be utilized by medical researchers. Public Library of Science 2022-06-30 /pmc/articles/PMC9246234/ /pubmed/35771759 http://dx.doi.org/10.1371/journal.pone.0265477 Text en © 2022 Ashouri, Kin Hing Phoa https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ashouri, Mahsa
Phoa, Frederick Kin Hing
Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title_full Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title_fullStr Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title_full_unstemmed Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title_short Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread
title_sort interactive tool for clustering and forecasting patterns of taiwan covid-19 spread
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246234/
https://www.ncbi.nlm.nih.gov/pubmed/35771759
http://dx.doi.org/10.1371/journal.pone.0265477
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