A modelling study investigating short and medium-term challenges for COVID-19 vaccination: From prioritisation to the relaxation of measures.

COVID-19 Comorbidities Prioritisation Relaxation of measures SARS-CoV-2 Vaccination

Journal

EClinicalMedicine
ISSN: 2589-5370
Titre abrégé: EClinicalMedicine
Pays: England
ID NLM: 101733727

Informations de publication

Date de publication:
Aug 2021
Historique:
received: 29 04 2021
revised: 10 06 2021
accepted: 11 06 2021
entrez: 19 7 2021
pubmed: 20 7 2021
medline: 20 7 2021
Statut: ppublish

Résumé

The roll-out of COVID-19 vaccines is a multi-faceted challenge whose performance depends on pace of vaccination, vaccine characteristics and heterogeneities in individual risks. We developed a mathematical model accounting for the risk of severe disease by age and comorbidity, and transmission dynamics. We compared vaccine prioritisation strategies in the early roll-out stage and quantified the extent to which measures could be relaxed as a function of the vaccine coverage achieved in France. Prioritizing at-risk individuals reduces morbi-mortality the most if vaccines only reduce severity, but is of less importance if vaccines also substantially reduce infectivity or susceptibility. Age is the most important factor to consider for prioritization; additionally accounting for comorbidities increases the performance of the campaign in a context of scarce resources. Vaccinating 90% of ≥65 y.o. and 70% of 18-64 y.o. before autumn 2021 with a vaccine that reduces severity by 90% and susceptibility by 80%, we find that control measures reducing transmission rates by 15-27% should be maintained to remain below 1000 daily hospital admissions in France with a highly transmissible variant (basic reproduction number Age and comorbidity-based vaccine prioritization strategies could reduce the burden of the disease. Very high vaccination coverage may be required to completely relax control measures. Vaccination of children, if possible, could lower coverage targets necessary to achieve this objective.

Sections du résumé

BACKGROUND BACKGROUND
The roll-out of COVID-19 vaccines is a multi-faceted challenge whose performance depends on pace of vaccination, vaccine characteristics and heterogeneities in individual risks.
METHODS METHODS
We developed a mathematical model accounting for the risk of severe disease by age and comorbidity, and transmission dynamics. We compared vaccine prioritisation strategies in the early roll-out stage and quantified the extent to which measures could be relaxed as a function of the vaccine coverage achieved in France.
FINDINGS RESULTS
Prioritizing at-risk individuals reduces morbi-mortality the most if vaccines only reduce severity, but is of less importance if vaccines also substantially reduce infectivity or susceptibility. Age is the most important factor to consider for prioritization; additionally accounting for comorbidities increases the performance of the campaign in a context of scarce resources. Vaccinating 90% of ≥65 y.o. and 70% of 18-64 y.o. before autumn 2021 with a vaccine that reduces severity by 90% and susceptibility by 80%, we find that control measures reducing transmission rates by 15-27% should be maintained to remain below 1000 daily hospital admissions in France with a highly transmissible variant (basic reproduction number
INTERPRETATION CONCLUSIONS
Age and comorbidity-based vaccine prioritization strategies could reduce the burden of the disease. Very high vaccination coverage may be required to completely relax control measures. Vaccination of children, if possible, could lower coverage targets necessary to achieve this objective.

Identifiants

pubmed: 34278284
doi: 10.1016/j.eclinm.2021.101001
pii: S2589-5370(21)00281-9
pmc: PMC8278244
doi:

Types de publication

Journal Article

Langues

eng

Pagination

101001

Informations de copyright

© 2021 The Author(s).

Déclaration de conflit d'intérêts

PC reports consulting fees from Sanofi Pasteur for projects outside of the submitted work and unrelated to COVID-19. The other authors declare no competing interests.

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Auteurs

Cécile Tran Kiem (C)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 25-28 rue du Dr Roux, 75015 Paris, France.
Sorbonne Université, Paris, France.

Clément R Massonnaud (CR)

Univ Rennes, EHESP, REPERES « Recherche en Pharmaco-Epidémiologie et Recours aux Soins », EA 7449 Rennes, France.
Centre Hospitalier Universitaire de Rouen, Département d'Informatique Médicale, D2IM, Rouen, France.

Daniel Levy-Bruhl (D)

Santé Publique France, Saint Maurice, France.

Chiara Poletto (C)

INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.

Vittoria Colizza (V)

INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.

Paolo Bosetti (P)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 25-28 rue du Dr Roux, 75015 Paris, France.

Arnaud Fontanet (A)

Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France.
PACRI Unit, Conservatoire National des Arts et Métiers, Paris, France.

Amélie Gabet (A)

Santé Publique France, Saint Maurice, France.

Valérie Olié (V)

Santé Publique France, Saint Maurice, France.

Laura Zanetti (L)

Haute Autorité de Santé, Saint-Denis La plaine Stade de France, France.

Pierre-Yves Boëlle (PY)

INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.

Pascal Crépey (P)

Univ Rennes, EHESP, REPERES « Recherche en Pharmaco-Epidémiologie et Recours aux Soins », EA 7449 Rennes, France.

Simon Cauchemez (S)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 25-28 rue du Dr Roux, 75015 Paris, France.

Classifications MeSH