Predictive efficacies of vaccine dose fractionation using neutralizing antibody levels

Abstract

With the emergence of SARS-CoV-2 variants that eluded immunity from vaccines and prior infections, vaccine shortages and their effectiveness pose unprecedented challenges for governments to expand booster vaccination programs. Fractionation of vaccine doses might be an effective strategy to help society to face these challenges, which may have comparable efficacies in contrast with the standard doses. In this study, we analyzed the relationship between in-vitro neutralization levels and the observed efficacies against asymptomatic and symptomatic infection of ten types of COVID-19 vaccines using data from 13 studies from vaccination and convalescent cohorts. We further projected efficacies for fractional doses based on 51 studies included in our systematic review. By comparing with the convalescent level, vaccine efficacy increases from 8.8% (95% CI: 1.4%, 16.1%) to 71.8% (95% CI: 63.0%, 80.7%) against asymptomatic infection, and from 33.6% (95% CI: 23.6%, 43.6%) to 98.6% (95% CI: 97.6%, 99.7%) against symptomatic infection, respectively, along with the mean neutralization level from 0.1 to 10 folds of convalescent level. And mRNA vaccines provide the strongest protection, and decrease slowly for fractional dosing between 50% and 100% dosage. Our results are consistent with studies for immune protection from COVID-19 infection. Based on our study, we expect that fractional dose vaccination could provide a partial immunity for SARS-CoV-2 virus. Fractional doses of vaccines could be a viable vaccination strategy compared to full-dose vaccination and deserves further exploration.

Competing Interest Statement

BJC reports honoraria from AstraZeneca, Fosun Pharma, GlaxoSmithKline, Moderna, Pfizer, Sanofi Pasteur, and Roche. The authors report no other potential conflicts of interest.

Funding Statement

We acknowledge the financial support from the AIR@InnoHK Programme from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.

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Data Availability

All data are collected from open source with a detailed description in the Methods section.

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