Evidence map›Paper›PMID 41305375›Full record

ArticlePathogens (Basel, Switzerland)2025

Estimating the Optimal COVID-19 Booster Timing Using Surrogate Correlates of Protection: A Longitudinal Antibody Study in Naïve and Previously Infected Individuals.

Yoshihiro Fujiya, Ryo Kobayashi, Makito Tanaka, Ema Suzuki, Shiro Hinotsu, Mami Nakae, Yuki Sato, Yuki Katayama, Masachika Saeki, Yuki Yakuwa and 4 more

Abstract read
In one paragraph

Article in Pathogens (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yoshihiro FujiyaDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.ORCID 0000-0002-5244-3992
Ryo KobayashiDivision of Laboratory Medicine, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.
Makito TanakaDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.
Ema SuzukiDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.
Shiro HinotsuDepartment of Biostatistics and Data Management, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.
Mami NakaeDivision of Infection Control, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.
Yuki SatoDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.ORCID 0000-0001-9539-074X
Yuki KatayamaDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.
Masachika SaekiDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.ORCID 0000-0001-6288-7696
Yuki YakuwaDivision of Laboratory Medicine, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.
Shinya NirasawaDivision of Laboratory Medicine, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.
Akemi EndohDivision of Laboratory Medicine, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.
Koji KuronumaDivision of Infection Control, Sapporo Medical University Hospital, Sapporo 060-8543, Japan.ORCID 0000-0002-2743-3266
Satoshi TakahashiDepartment of Infection Control and Laboratory Medicine, Sapporo Medical University School of Medicine, Sapporo 060-8556, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Standardized, one-size-fits-all COVID-19 booster schedules may be suboptimal due to individual variation in immune backgrounds, particularly prior infection, which induces robust hybrid immunity. This study estimated optimal booster timing by modeling antibody decay in relation to surrogate correlates of protection (CoP). In a prospective cohort of 177 Japanese healthcare workers, we longitudinally monitored anti-spike receptor-binding domain (S-RBD) antibody titers following BNT162b2 vaccination. Participants were stratified into SARS-CoV-2-naïve and previously infected groups. Mixed-effects models were developed to predict when antibody titers would decline below predefined CoP thresholds. The model estimated optimal booster timing after a two-dose primary series to be 3-5 months for naïve individuals and approximately one year for those with prior infection. Following a third dose, the estimated interval extended to 8-12 months for the naïve group and 1.5-2 years for the previously infected group. These substantial differences underscore the limitations of uniform booster schedules. Our findings provide a quantitative framework for personalized vaccination strategies based on individual antibody profiles and immune status, thereby optimizing protection.

Indexed as

Antibodies, ViralCOVID-19COVID-19 VaccinesImmunization, SecondarySARS-CoV-2AdultBNT162 VaccineFemaleHumansImmunization ScheduleLongitudinal StudiesMaleMiddle AgedProspective StudiesSpike Glycoprotein, CoronavirusTime FactorsAntibodies, ViralBNT162 VaccineCOVID-19 VaccinesSpike Glycoprotein, Coronavirusantibody titerbooster timingcorrelate of protectionCOVID-19mixed-effects modelmRNA vaccinesurrogate virus neutralization test

Identifiers

PMID41305375
PMCPMC12655711

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.