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Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic
INTRODUCTION: Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone ge...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646469/ https://www.ncbi.nlm.nih.gov/pubmed/36406186 http://dx.doi.org/10.1007/s12553-022-00712-4 |
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author | Oyewola, David Opeoluwa Dada, Emmanuel Gbenga Misra, Sanjay |
author_facet | Oyewola, David Opeoluwa Dada, Emmanuel Gbenga Misra, Sanjay |
author_sort | Oyewola, David Opeoluwa |
collection | PubMed |
description | INTRODUCTION: Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors. MATERIALS AND METHODS: Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES). RESULTS: Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively. CONCLUSION: This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them. |
format | Online Article Text |
id | pubmed-9646469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-96464692022-11-14 Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic Oyewola, David Opeoluwa Dada, Emmanuel Gbenga Misra, Sanjay Health Technol (Berl) Original Paper INTRODUCTION: Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors. MATERIALS AND METHODS: Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES). RESULTS: Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively. CONCLUSION: This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them. Springer Berlin Heidelberg 2022-11-10 2022 /pmc/articles/PMC9646469/ /pubmed/36406186 http://dx.doi.org/10.1007/s12553-022-00712-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Original Paper Oyewola, David Opeoluwa Dada, Emmanuel Gbenga Misra, Sanjay Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title | Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title_full | Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title_fullStr | Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title_full_unstemmed | Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title_short | Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic |
title_sort | machine learning for optimizing daily covid-19 vaccine dissemination to combat the pandemic |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646469/ https://www.ncbi.nlm.nih.gov/pubmed/36406186 http://dx.doi.org/10.1007/s12553-022-00712-4 |
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