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Predicting COVID-19 Vaccine Efficacy from Neutralizing Antibody Levels
Majid R. Abedi; Samuel Dixon; Timothy Guyon; Serene Hsu; Aviva R. Jacobs; Lakshmi Nair; Robert Terbrueggen.
Afiliação
  • Majid R. Abedi; DxTerity Diagnostics, Rancho Dominguez, CA
  • Samuel Dixon; DxTerity Diagnostics, Rancho Dominguez, CA
  • Timothy Guyon; DxTerity Diagnostics, Rancho Dominguez, CA
  • Serene Hsu; DxTerity Diagnostics, Rancho Dominguez, CA
  • Aviva R. Jacobs; DxTerity Diagnostics, Rancho Dominguez, CA
  • Lakshmi Nair; Thermo Fisher Scientific, South San Francisco, CA
  • Robert Terbrueggen; DxTerity Diagnostics, Rancho Dominguez, CA
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21264921
ABSTRACT
Recent studies using data accrued from global SARS-CoV-2 vaccination efforts have demonstrated that breakthrough infections are correlated with levels of neutralizing antibodies. The decrease in neutralizing antibody titers of vaccinated individuals over time, combined with the emergence of more infectious variants of concern has resulted in waning vaccine efficacy against infection and a rise in breakthrough infections. Here we use a combination of neutralizing antibody measurements determined by a high throughput surrogate viral neutralization test (sVNT) together with published data from vaccine clinical trials and comparative plaque reduction neutralization test (PRNT) between SARS-CoV-2 variants to develop a model for vaccine efficacy (VE) against symptomatic infection. Vaccine efficacy estimates using this model show good concordance with real world data from the US and Israel. Our work demonstrates that appropriately calibrated neutralizing antibody measurements determined by high throughput sVNT can be used to provide a semi-quantitative estimate of protection against infection. Given the highly variable antibody levels among the vaccinated population, this model may be of use in identification of individuals with an elevated risk of breakthrough infections.
Licença
cc_by_nc
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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