Abstract

Vaccines have reduced the burden of COVID-19 disease in the UK since their introduction in December 2020. At the time of their introduction, it was unclear the extent to which COVID-19 vaccines would be accepted and how spatial variations in uptake would emerge, driven by socio-demographic characteristics. In this study, data from a large-scale cross-sectional study of over 17,000 adults, surveyed in September and October 2020, was used to provide sub-national forecasts of COVID-19 vaccine uptake across the UK. Bayesian multilevel regression and poststratification was deployed to forecast COVID-19 vaccine acceptance before vaccine rollout across 174 regions of the UK. Although it was found that a majority of the UK adult population would likely take the vaccine, there were substantial heterogeneities in uptake intent across the UK. Large urban areas, including London and North West England, females, Black or Black British ethnicities, and Polish speakers were among the least likely to state an intent to vaccinate. These predicted spatial trends were validated by comparison to observed observed COVID-19 vaccine uptake in late 2021. The methodological approaches deployed in this validated forecasting study may be replicable for the prediction of routine childhood immunisation uptake. Given recent pandemic-induced disruptions to routine immunisation systems, reliable sub-national forecasts of vaccine uptake may provide policymakers and stakeholders early warning signals of potential vaccine confidence issues.

Details

Title
Forecasting sub-national trends in COVID-19 vaccine uptake in the UK before vaccine rollout
Author
de Figueiredo, A. 1 

 London School of Hygiene and Tropical Medicine, Department of Infectious Disease Epidemiology, London, UK (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X); Imperial College London, Department of Mathematics, London, UK (GRID:grid.7445.2) (ISNI:0000 0001 2113 8111) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2753902780
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.