COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2.


Journal

Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440

Informations de publication

Date de publication:
08 Apr 2022
Historique:
entrez: 8 4 2022
pubmed: 9 4 2022
medline: 9 4 2022
Statut: ppublish

Résumé

In controlling transmission of coronavirus disease 2019 (COVID-19), the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large-scale lockdown policies. Here, we develop and apply an individual-based model of COVID-19 to simulate case importation from managed quarantine under various vaccination scenarios. We then use the output of the individual-based model as input to a branching process model to assess community transmission risk. For parameters corresponding to the Delta variant, our results demonstrate that vaccination effectively counteracts the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.

Identifiants

pubmed: 35394833
doi: 10.1126/sciadv.abm3624
pmc: PMC8993115
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eabm3624

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Auteurs

Cameron Zachreson (C)

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.

Freya M Shearer (FM)

Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.

David J Price (DJ)

Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.

Michael J Lydeamore (MJ)

Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia.

Jodie McVernon (J)

Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
Victorian Infectious Diseases Laboratory Epidemiology Unit, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.

James McCaw (J)

Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia.

Nicholas Geard (N)

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.
Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.

Classifications MeSH