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Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant(B.1.617.2) of SARS-CoV-2 (preprint)
medrxiv; 2021.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2021.10.12.21264840
ABSTRACT
Background From January to May 2021 the alpha variant (B.1.1.7) of SARS-CoV-2 was the most commonly detected variant in the UK, but since then the Delta variant (B.1.617.2), first detected in India, has become the predominant variant. The UK COVID-19 vaccination programme started on 8thDecember 2020. Most vaccine effectiveness studies to date have focused on the alpha variant. We therefore aimed to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1nCoV-19 (Oxford-AstraZeneca) vaccines in preventing infection with respect to the Delta variant in a UK setting. Methods We used anonymised public health record data linked to infection data (PCR) using the Combined Intelligence for Population Health Action resource. We then constructed an SIR epidemic model to explain SARS-CoV-2 infection data across the Cheshire and Merseyside region of the UK. Results We determined that the effectiveness of the Oxford-AstraZeneca vaccine in reducing susceptibility to infection is 39% (95% credible interval [34,43]) and 64% (95% credible interval [61,67]) for a single dose and a double dose respectively. For the Pfizer-BioNTech vaccine, the effectiveness is 20% (95% credible interval [10,28]) and 84% (95% credible interval [82,86]) for a single-dose and a double dose respectively. Conclusion Vaccine effectiveness for reducing susceptibility to SARS-CoV-2 infection shows noticeable improvement after receiving two doses of either vaccine. Findings also suggest that a full course of the Pfizer-BioNTech provides the optimal protection against infection with the Delta variant. This would advocate for completing the full course programme to maximise individual protection and reduce transmission.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
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