SynthesisJMIR public health and surveillance2024
Predicting Efficacies of Fractional Doses of Vaccines by Using Neutralizing Antibody Levels: Systematic Review and Meta-Analysis.
Synthesis in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Clinical Significance of Neutralizing Antibodies in COVID-19: Implications for Disease Prognosis.Life (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Background: With the emergence of SARS-CoV-2 variants that have eluded immunity from vaccines and prior infections, vaccine shortages and vaccine effectiveness pose unprecedented challenges for governments in expanding booster vaccination programs. The fractionation of vaccine doses might be an effective strategy for helping society to face these challenges, as fractional doses may have efficacies comparable with those of the standard doses. Objective: This study aims to investigate the relationship between vaccine immunogenicity and protection and to project efficacies of fractional doses of vaccines for COVID-19 by using neutralizing antibody levels. Methods: In this study, we analyzed the relationship between in vitro neutralization levels and the observed efficacies against both asymptomatic infection and symptomatic infection, using data from 13 studies of 10 COVID-19 vaccines and from convalescent cohorts. We further projected efficacies for fractional doses, using neutralization as an intermediate variable, based on immunogenicity data from 51 studies included in our systematic review. Results: In comparisons with the convalescent level, vaccine efficacy against asymptomatic infection and symptomatic infection increased from 8.8% (95% CI 1.4%-16.1%) to 71.8% (95% CI 63%-80.7%) and from 33.6% (95% CI 23.6%-43.6%) to 98.6% (95% CI 97.6%-99.7%), respectively, as the mean neutralization level increased from 0.1 to 10 folds of the convalescent level. Additionally, mRNA vaccines provided the strongest protection, which decreased slowly for fractional dosing with dosages between 50% and 100% of the standard dose. We also observed that although vaccine efficacy increased with the mean neutralization level, the rate of this increase was slower for vaccine efficacy against asymptomatic infection than for vaccine efficacy against symptomatic infection. Conclusions: Our results are consistent with studies on immune protection from SARS-CoV-2 infection. Based on our study, we expect that fractional-dose vaccination could provide partial immunity against SARS-CoV-2 and its variants. Our findings provide a theoretical basis for the efficacy of fractional-dose vaccines, serving as reference evidence for implementing fractional dosing vaccine policies in areas facing vaccine shortages and thereby mitigating disease burden. Fractional-dose vaccination could be a viable vaccination strategy comparable to full-dose vaccination and deserves further exploration.
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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.