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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9246234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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