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Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic pr...

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Autores principales: Shih, Dong-Her, Shih, Pai-Ling, Wu, Ting-Wei, Li, Cheng-Jung, Shih, Ming-Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323689/
https://www.ncbi.nlm.nih.gov/pubmed/35885762
http://dx.doi.org/10.3390/healthcare10071235
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author Shih, Dong-Her
Shih, Pai-Ling
Wu, Ting-Wei
Li, Cheng-Jung
Shih, Ming-Hung
author_facet Shih, Dong-Her
Shih, Pai-Ling
Wu, Ting-Wei
Li, Cheng-Jung
Shih, Ming-Hung
author_sort Shih, Dong-Her
collection PubMed
description Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through “The COVID Tracking Project”, this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic’s severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.
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spelling pubmed-93236892022-07-27 Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution Shih, Dong-Her Shih, Pai-Ling Wu, Ting-Wei Li, Cheng-Jung Shih, Ming-Hung Healthcare (Basel) Article Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through “The COVID Tracking Project”, this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic’s severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection. MDPI 2022-07-02 /pmc/articles/PMC9323689/ /pubmed/35885762 http://dx.doi.org/10.3390/healthcare10071235 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shih, Dong-Her
Shih, Pai-Ling
Wu, Ting-Wei
Li, Cheng-Jung
Shih, Ming-Hung
Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title_full Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title_fullStr Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title_full_unstemmed Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title_short Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution
title_sort cluster analysis of us covid-19 infected states for vaccine distribution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323689/
https://www.ncbi.nlm.nih.gov/pubmed/35885762
http://dx.doi.org/10.3390/healthcare10071235
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