Evidence map›Paper›PMID 41022482›Full record

ArticleBMJ global health2025

Health impact and cost-effectiveness of COVID-19 booster vaccination strategies in the early post-Omicron era: a dynamic modelling study.

Thao P Le, Eamon Conway, Edifofon Akpan, Isobel R Abell, Patrick Abraham, Christopher M Baker, Patricia T Campbell, Deborah Cromer, Michael J Lydeamore, Yasmine McDonough and 6 more

Abstract read
In one paragraph

Article in BMJ global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Thao P LeSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia tk.le@unimelb.edu.au.ORCID 0000-0001-6309-1753
Eamon ConwayPopulation Health & Immunity Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.ORCID 0000-0002-8807-4877
Edifofon AkpanMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0002-6646-206X
Isobel R AbellSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-0803-1648
Patrick AbrahamMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-1239-1363
Christopher M BakerSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0001-9449-3632
Patricia T CampbellMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-4013-9835
Deborah CromerKirby Institute, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0002-5276-5094
Michael J LydeamoreDepartment of Econometrics and Business Statistics, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0001-6515-827X
Yasmine McDonoughPopulation Health & Immunity Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.
Ivo MuellerPopulation Health & Immunity Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.ORCID 0000-0001-6554-6889
Gerard RyanMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-0183-7630
Camelia WalkerSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia.
Cyan Yingying WangMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0002-7657-1200
Natalie CarvalhoMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0002-5599-2209
Jodie McVernonDepartment of Infectious Diseases at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0001-9774-1961

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFollowing widespread exposure to Omicron variants, SARS-CoV-2 has transitioned to endemic circulation. Populations now have diverse infection and vaccination histories, resulting in heterogeneous immune landscapes. Careful consideration of the value of ongoing vaccination is required through the post-Omicron phase of COVID-19 management to minimise disease burden. We demonstrate the utility of a modelling approach to address this question, supporting recommendations for targeted vaccine use across different country settings.

methodsWe integrated immunological, transmission, clinical and cost-effectiveness models and simulated populations with different characteristics and immune landscapes over the early post-Omicron period. We calculated the expected number of infections, hospitalisations and deaths for different vaccine scenarios. Costs (from a healthcare perspective) were estimated for exemplar country income-level groupings in the Western Pacific Region using pandemic-era vaccine prices and healthcare-seeking behaviour assumptions. We assessed the impact and cost-effectiveness of targeted vaccination strategies. Results are reported as incremental costs and disability-adjusted life years averted compared with no additional vaccination. Parameter and stochastic uncertainty were captured through scenario and sensitivity analysis.

resultsAcross different population demographics and income levels, we consistently found that annual elder-targeted boosting strategies are most likely to be cost-effective or cost-saving (>75% probability of being cost-effective among older, high-income settings; >50% probability of being cost-effective in younger, middle-income settings), while paediatric programmes are unlikely to be cost-effective. Results remained broadly consistent while accounting for uncertainties in the epidemiological and economic models, although they were sensitive to the cost of home-based care and vaccination. Use of pandemic-era vaccine prices may underestimate current vaccine prices available in upper-middle-income and high-income settings, potentially overestimating the cost-effectiveness of boosting in these settings. Half-yearly boosting may only be cost-effective in higher income settings with older population demographics and higher cost-effectiveness thresholds.

conclusionCompeting health priorities and resource constraints mean COVID-19 vaccine allocation needs to be carefully considered in context. These results, reflecting modelling conducted on the early post-Omicron period, demonstrate the value of continued booster vaccinations to protect against severe COVID-19 disease outcomes across high-income and middle-income settings and show that the biggest health gains relative to vaccine costs are achieved by targeting older age groups.

Indexed as

COVID-19COVID-19 VaccinesHealth Impact AssessmentImmunization, SecondaryAdolescentAdultAgedChildChild, PreschoolCost-Benefit AnalysisDisability-Adjusted Life YearsFemaleHumansMiddle AgedSARS-CoV-2COVID-19 VaccinesCOVID-19Health economicsMathematical modelling

Identifiers

PMID41022482
PMCPMC12481342

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.