Evidence mapPaperPMID 35965469Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.

Paula Sanz-Leon, Lachlan H W Hamilton, Sebastian J Raison, Anna J X Pan, Nathan J Stevenson, Robyn M Stuart, Romesh G Abeysuriya, Cliff C Kerr, Stephen B Lambert, James A Roberts

Open access · hybridAbstract read
In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

9 citing papers in PubMed, 21 citations in OpenAlex.

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  7. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  8. Technical challenges of modelling real-life epidemics and examples of overcoming these.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  9. Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
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 5 institutions in 3 countries.

Paula Sanz-LeonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.ORCID 0000-0002-1545-6380
Lachlan H W HamiltonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
Sebastian J RaisonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
Anna J X PanBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
Nathan J StevensonBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
Robyn M StuartDepartment of Mathematical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark.
Romesh G AbeysuriyaBurnet Institute, Melbourne, VIC 3001, Australia.
Cliff C KerrInstitute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA 98109, USA.
Stephen B LambertNational Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, NSW 2145, Australia.
James A RobertsBrain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.ORCID 0000-0003-1626-6551
QIMR Berghofer Medical Research Institute · AUBurnet Institute · AUGates Foundation · USNational Centre for Immunisation Research & Surveillance · AUUniversity of Copenhagen · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Indexed as

COVID-19Immunity, HerdAustraliaHumansQueenslandSARS-CoV-2Vaccinationagent-based modellingAustraliaCOVID-19COVID-19 vaccinationherd immunity thresholdOmicron variant

Identifiers

PMID35965469
PMCPMC9376720
OpenAlexW4291448124

What Socratic 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 Socratic graph.