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

© 2023. The Author(s)..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:8

Enthalten in:

NPJ vaccines - 8(2023), 1 vom: 06. März, Seite 33

Sprache:

Englisch

Beteiligte Personen:

Uwamino, Yoshifumi [VerfasserIn]
Nagashima, Kengo [VerfasserIn]
Yoshifuji, Ayumi [VerfasserIn]
Suga, Shigeru [VerfasserIn]
Nagao, Mizuho [VerfasserIn]
Fujisawa, Takao [VerfasserIn]
Ryuzaki, Munekazu [VerfasserIn]
Takemoto, Yoshiaki [VerfasserIn]
Namkoong, Ho [VerfasserIn]
Wakui, Masatoshi [VerfasserIn]
Matsushita, Hiromichi [VerfasserIn]
Hasegawa, Naoki [VerfasserIn]
Sato, Yasunori [VerfasserIn]
Murata, Mitsuru [VerfasserIn]

Links:

Volltext

Themen:

Journal Article

Anmerkungen:

Date Revised 08.03.2023

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1038/s41541-023-00626-w

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM353847488