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Modelling herd immunity requirements in Queensland

dc.contributor.authorSanz-Leon, Paulaen
dc.contributor.authorHamilton, Lachlan H.W.en
dc.contributor.authorRaison, Sebastian J.en
dc.contributor.authorPan, Anna J.X.en
dc.contributor.authorStevenson, Nathan J.en
dc.contributor.authorStuart, Robyn M.en
dc.contributor.authorAbeysuriya, Romesh G.en
dc.contributor.authorKerr, Cliff C.en
dc.contributor.authorLambert, Stephen B.en
dc.contributor.authorRoberts, James A.en
dc.date.accessioned2025-03-22T08:28:31Z
dc.date.available2025-03-22T08:28:31Z
dc.date.issued2022-10-03en
dc.description.abstractLong-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 0.005% 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'.en
dc.description.sponsorshipThis work was supported by QIMR Berghofer and Queensland Health’s Health Innovation, Investment and Research Office (HIIRO). This work was supported by QIMR Berghofer and Queensland Health's Health Innovation, Investment and Research Office (HIIRO)en
dc.description.statustrueen
dc.identifier.otherScopus:85134401309en
dc.identifier.otherPubMed:35965469en
dc.identifier.urihttps://dspace-test.anu.edu.au/handle/1885/733729128
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85134401309&partnerID=8YFLogxKen
dc.language.isoEnglishen
dc.rightsPublisher Copyright: © 2022 The Authors.en
dc.sourcePhilosophical transactions. Series A, Mathematical, physical, and engineering sciencesen
dc.subjectagent-based modellingen
dc.subjectAustraliaen
dc.subjectCOVID-19en
dc.subjectCOVID-19 vaccinationen
dc.subjectherd immunity thresholden
dc.subjectOmicron varianten
dc.titleModelling herd immunity requirements in Queenslanden
dc.typeArticleen
local.contributor.affiliationSanz-Leon, Paula; Queensland Institute of Medical Researchen
local.contributor.affiliationHamilton, Lachlan H.W.; Queensland Institute of Medical Researchen
local.contributor.affiliationRaison, Sebastian J.; Queensland Institute of Medical Researchen
local.contributor.affiliationPan, Anna J.X.; Queensland Institute of Medical Researchen
local.contributor.affiliationStevenson, Nathan J.; Queensland Institute of Medical Researchen
local.contributor.affiliationStuart, Robyn M.; University of Copenhagenen
local.contributor.affiliationAbeysuriya, Romesh G.; Burnet Instituteen
local.contributor.affiliationKerr, Cliff C.; Bill and Melinda Gates Foundationen
local.contributor.affiliationLambert, Stephen B.; National Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases, Australiaen
local.contributor.affiliationRoberts, James A.; Queensland Institute of Medical Researchen
local.identifier.citationvolume380en
local.identifier.doi10.1098/rsta.2021.0311en
local.identifier.pure13c8028f-7f51-4541-8619-08adf4cb9c91en
local.type.statusPublisheden

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