Pagel, Christina, Wilde, Harrison, Tomlinson, Christopher, Mateen, Bilal and Brown, Katherine (2023) A methodological framework for assessing the benefit of SARS-CoV-2 vaccination following previous infection : case study of five- to eleven-year-olds. Vaccines, 11 (5). 988. doi:10.3390/vaccines11050988 ISSN 2076-393X.
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Abstract
Vaccination rates against SARS-CoV-2 in children aged five to eleven years remain low in many countries. The current benefit of vaccination in this age group has been questioned given that the large majority of children have now experienced at least one SARS-CoV-2 infection. However, protection from infection, vaccination or both wanes over time. National decisions on offering vaccines to this age group have tended to be made without considering time since infection. There is an urgent need to evaluate the additional benefits of vaccination in previously infected children and under what circumstances those benefits accrue. We present a novel methodological framework for estimating the potential benefits of COVID-19 vaccination in previously infected children aged five to eleven, accounting for waning. We apply this framework to the UK context and for two adverse outcomes: hospitalisation related to SARS-CoV-2 infection and Long Covid. We show that the most important drivers of benefit are: the degree of protection provided by previous infection; the protection provided by vaccination; the time since previous infection; and future attack rates. Vaccination can be very beneficial for previously infected children if future attack rates are high and several months have elapsed since the previous major wave in this group. Benefits are generally larger for Long Covid than hospitalisation, because Long Covid is both more common than hospitalisation and previous infection offers less protection against it. Our framework provides a structure for policy makers to explore the additional benefit of vaccination across a range of adverse outcomes and different parameter assumptions. It can be easily updated as new evidence emerges.
| Item Type: | Journal Article |
|---|---|
| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RJ Pediatrics |
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics |
| SWORD Depositor: | Library Publications Router |
| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) , COVID-19 (Disease) -- Vaccination, Vaccination of children -- Mathematical models |
| Journal or Publication Title: | Vaccines |
| Publisher: | MDPI |
| ISSN: | 2076-393X |
| Official Date: | 16 May 2023 |
| Dates: | Date Event 16 May 2023 Published 11 May 2023 Accepted |
| Volume: | 11 |
| Number: | 5 |
| Article Number: | 988 |
| DOI: | 10.3390/vaccines11050988 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Open Access (Creative Commons open licence) |
| Date of first compliant deposit: | 25 July 2023 |
| Date of first compliant Open Access: | 26 July 2023 |
| RIOXX Funder/Project Grant: | Project/Grant ID RIOXX Funder Name Funder ID UNSPECIFIED Health Data Research UK UNSPECIFIED Feuer International Scholarship in Artificial Intelligence University of Warwick UNSPECIFIED University College London Hospitals NHS Foundation Trust |
| URI: | https://wrap.warwick.ac.uk/176021/ |
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