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Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination

Tools that can be used to estimate antibody waning following COVID-19 vaccinations can facilitate an understanding of the current immune status of the population. In this study, a two-compartment-based mathematical model is formulated to describe the dynamics of the anti-SARS-CoV-2 antibody in healt...

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Autores principales: Uwamino, Yoshifumi, Nagashima, Kengo, Yoshifuji, Ayumi, Suga, Shigeru, Nagao, Mizuho, Fujisawa, Takao, Ryuzaki, Munekazu, Takemoto, Yoshiaki, Namkoong, Ho, Wakui, Masatoshi, Matsushita, Hiromichi, Hasegawa, Naoki, Sato, Yasunori, Murata, Mitsuru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988198/
https://www.ncbi.nlm.nih.gov/pubmed/36878929
http://dx.doi.org/10.1038/s41541-023-00626-w
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author Uwamino, Yoshifumi
Nagashima, Kengo
Yoshifuji, Ayumi
Suga, Shigeru
Nagao, Mizuho
Fujisawa, Takao
Ryuzaki, Munekazu
Takemoto, Yoshiaki
Namkoong, Ho
Wakui, Masatoshi
Matsushita, Hiromichi
Hasegawa, Naoki
Sato, Yasunori
Murata, Mitsuru
author_facet Uwamino, Yoshifumi
Nagashima, Kengo
Yoshifuji, Ayumi
Suga, Shigeru
Nagao, Mizuho
Fujisawa, Takao
Ryuzaki, Munekazu
Takemoto, Yoshiaki
Namkoong, Ho
Wakui, Masatoshi
Matsushita, Hiromichi
Hasegawa, Naoki
Sato, Yasunori
Murata, Mitsuru
author_sort Uwamino, Yoshifumi
collection PubMed
description Tools that can be used to estimate antibody waning following COVID-19 vaccinations can facilitate an understanding of the current immune status of the population. In this study, a two-compartment-based mathematical model is formulated to describe the dynamics of the anti-SARS-CoV-2 antibody in healthy adults using serially measured waning antibody concentration data obtained in a prospective cohort study of 673 healthcare providers vaccinated with two doses of BNT162b2 vaccine. The datasets of 165 healthcare providers and 292 elderly patients with or without hemodialysis were used for external validation. Internal validation of the model demonstrated 97.0% accuracy, and external validation of the datasets of healthcare workers, hemodialysis patients, and nondialysis patients demonstrated 98.2%, 83.3%, and 83.8% accuracy, respectively. The internal and external validations demonstrated that this model also fits the data of various populations with or without underlying illnesses. Furthermore, using this model, we developed a smart device application that can rapidly calculate the timing of negative seroconversion.
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spelling pubmed-99881982023-03-07 Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination Uwamino, Yoshifumi Nagashima, Kengo Yoshifuji, Ayumi Suga, Shigeru Nagao, Mizuho Fujisawa, Takao Ryuzaki, Munekazu Takemoto, Yoshiaki Namkoong, Ho Wakui, Masatoshi Matsushita, Hiromichi Hasegawa, Naoki Sato, Yasunori Murata, Mitsuru NPJ Vaccines Article Tools that can be used to estimate antibody waning following COVID-19 vaccinations can facilitate an understanding of the current immune status of the population. In this study, a two-compartment-based mathematical model is formulated to describe the dynamics of the anti-SARS-CoV-2 antibody in healthy adults using serially measured waning antibody concentration data obtained in a prospective cohort study of 673 healthcare providers vaccinated with two doses of BNT162b2 vaccine. The datasets of 165 healthcare providers and 292 elderly patients with or without hemodialysis were used for external validation. Internal validation of the model demonstrated 97.0% accuracy, and external validation of the datasets of healthcare workers, hemodialysis patients, and nondialysis patients demonstrated 98.2%, 83.3%, and 83.8% accuracy, respectively. The internal and external validations demonstrated that this model also fits the data of various populations with or without underlying illnesses. Furthermore, using this model, we developed a smart device application that can rapidly calculate the timing of negative seroconversion. Nature Publishing Group UK 2023-03-06 /pmc/articles/PMC9988198/ /pubmed/36878929 http://dx.doi.org/10.1038/s41541-023-00626-w Text en © The Author(s) 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Uwamino, Yoshifumi
Nagashima, Kengo
Yoshifuji, Ayumi
Suga, Shigeru
Nagao, Mizuho
Fujisawa, Takao
Ryuzaki, Munekazu
Takemoto, Yoshiaki
Namkoong, Ho
Wakui, Masatoshi
Matsushita, Hiromichi
Hasegawa, Naoki
Sato, Yasunori
Murata, Mitsuru
Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title_full Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title_fullStr Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title_full_unstemmed Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title_short Estimating immunity with mathematical models for SARS-CoV-2 after COVID-19 vaccination
title_sort estimating immunity with mathematical models for sars-cov-2 after covid-19 vaccination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988198/
https://www.ncbi.nlm.nih.gov/pubmed/36878929
http://dx.doi.org/10.1038/s41541-023-00626-w
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