Evidence map›Paper›PMID 38627637›Full record

ArticleBMC infectious diseases2024

Modelling the impact of hybrid immunity on future COVID-19 epidemic waves.

Thao P Le, Isobel Abell, Eamon Conway, Patricia T Campbell, Alexandra B Hogan, Michael J Lydeamore, Jodie McVernon, Ivo Mueller, Camelia R Walker, Christopher M Baker

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 19% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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

10 authors at 4 institutions in 2 countries.

Thao P LeSchool of Mathematics and Statistics, The University of Melbourne, Grattan Street, Melbourne, 3010, Victoria, Australia. tk.le@unimelb.edu.au.ORCID http://orcid.org/0000-0001-6309-1753
Isobel AbellSchool of Mathematics and Statistics, The University of Melbourne, Grattan Street, Melbourne, 3010, Victoria, Australia.ORCID http://orcid.org/0000-0003-0803-1648
Eamon ConwayPopulation Health & Immunity Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Melbourne, 3052, Victoria, Australia.ORCID http://orcid.org/0000-0002-8807-4877
Patricia T CampbellDepartment of Infectious Diseases at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth St, Melbourne, 3000, Victoria, Australia.ORCID http://orcid.org/0000-0003-4013-9835
Alexandra B HoganSchool of Population Health, University of New South Wales, Sydney, 2033, New South Wales, Australia.ORCID http://orcid.org/0000-0002-6271-9921
Michael J LydeamoreDepartment of Econometrics and Business Statistics, Monash University, Wellington Road, Melbourne, 3800, Victoria, Australia.ORCID http://orcid.org/0000-0001-6515-827X
Jodie McVernonDepartment of Infectious Diseases at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth St, Melbourne, 3000, Victoria, Australia.ORCID http://orcid.org/0000-0001-9774-1961
Ivo MuellerPopulation Health & Immunity Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Melbourne, 3052, Victoria, Australia.ORCID http://orcid.org/0000-0001-6554-6889
Camelia R WalkerSchool of Mathematics and Statistics, The University of Melbourne, Grattan Street, Melbourne, 3010, Victoria, Australia.ORCID http://orcid.org/0000-0003-1470-858X
Christopher M BakerSchool of Mathematics and Statistics, The University of Melbourne, Grattan Street, Melbourne, 3010, Victoria, Australia.ORCID http://orcid.org/0000-0001-9449-3632
The University of Melbourne · AUMonash University · AUUNSW Sydney · AUWalter and Eliza Hall Institute of Medical Research · AU

Funding

Australian National Health and Medical Research Council 2021/GNT2009278Australian National Health and Medical Research Council 2022/GNT2009278Australian National Health and Medical Research Council GNT1170960
6 · The paper itself

Abstract

backgroundSince the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves.

methodsTo investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region.

resultsWe find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants.

conclusionsEnhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

Indexed as

COVID-19EpidemicsVaccinesAdaptive ImmunityHumansSARS-CoV-2VaccinationVaccinesEpidemiologyMathematical modellingVaccinationVariants

Identifiers

PMID38627637
PMCPMC11020923
OpenAlexW4394849339

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.