Modelling frameworks for vaccine protection are sorely needed to fight the Covid-19 pandemic with vaccines. We propose such a framework for the BNT162b2 and potentially other vaccines. It identifies correlates of protection based on live SARS-CoV-2 variants neutralising antibody titres from vaccinated individuals. We applied it to predict vaccine effectiveness in overall populations and age subgroups. It was validated by predicting effectiveness against the B.1.167.2 (Delta) variant. The predictions, of 51.7% (34%, 69%) after one and of 88.6% (76%, 97%) after two vaccine doses, were close to the corresponding means, 49% and 85.4%, of observations in real-life effectiveness studies. We illustrate its applications to inform decisions, such as about the doses and timing of vaccine boosters.
This paper presents predictions of the symptomatic effectiveness of the Pfizer-BioNTech BNT162b2 (Comirnaty) vaccine against Omicron B.1.1.529, the latest SARS-CoV-2 variant of concern. They were obtained assuming fold decreases in Omicron neutralisation by vaccine-induced antibodies versus neutralisation of the virus Wild Type. A 25-fold decrease was assumed based on Omicron pseudovirus neutralisation study by Pfizer and BioNTech; a 94-fold, based on live-Omicron neutralisation study in South Africa; and 40, 80 and 120 folds, hypothesised based on genetic information. The effectiveness of two vaccine doses was predicted as 66% (42, 86), 48% (25, 72) and 42% (20, 66) for up to five months starting 2-4 weeks after the second dose, for the 25, 80 and 120 folds, respectively. The effectiveness of booster vaccination was predicted under a highly conservative assumption that the third dose would increase neutralisation by only 3.3 folds compared to the second dose. The predictions of effectiveness for up to five months, starting 2-4 weeks after the third dose, were 81% (59, 95), 67% (43, 87) and 61% (37, 82) for the 25, 80 and 120 folds, respectively. Despite the large fold decreases considered, the vaccine could still provide substantial protection, particularly after a booster and against severe disease. The paper is accompanied by free software which can be used to predict the symptomatic effectiveness of Comirnaty against Omicron under different neutralisation folds, including those obtained experimentally.
Predictions of Covid vaccine effectiveness could support rapid and effective measures against the pandemic. Our modelling boosts the accuracy and applications of these predictions, especially to subgroups. We model the symptomatic effectiveness of Comirnaty or Vaxzevria with 50% neutralising antibody titres from a large UK immunogenicity study and with up to 68 effectiveness estimates from 23 vaccine studies. We predicted effectiveness in adult populations, age and disease subgroups, with 45% (95% CI: 27-63) predicted against Omicron BA.1 for Comirnaty boosters in haemodialysis patients. Prediction errors for two Comirnaty doses in adults were 1.9%, 2.6% and 0.4%, against the Alpha, Beta and Delta variants, versus 3.6%, 28% and 8.7% with a state-of-the-art alternative; and for Vaxzevria, 1.1% and 0.7% against Alpha and Delta, versus 18% and 20.4%. Identical titres implied between 18% (95% CI: 1-33) and 31% (95% CI: 13-50) lower Comirnaty effectiveness against Omicron BA.1 than Delta.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.