Early mathematical models of COVID-19 vaccination in high-income countries: a systematic review.

COVID-19 Mathematical modelling SARS-CoV-2 Systematic review Vaccination

Journal

Public health
ISSN: 1476-5616
Titre abrégé: Public Health
Pays: Netherlands
ID NLM: 0376507

Informations de publication

Date de publication:
12 Sep 2024
Historique:
received: 01 02 2024
revised: 22 07 2024
accepted: 26 07 2024
medline: 14 9 2024
pubmed: 14 9 2024
entrez: 13 9 2024
Statut: aheadofprint

Résumé

Since COVID-19 first emerged in 2019, mathematical models have been developed to predict transmission and provide insight into disease control strategies. A key research need now is for models to inform long-term vaccination policy. We aimed to review the early modelling methods utilised during the pandemic period (2019-2023) in order to identify gaps in the literature and highlight areas for future model development. This study was a systematic review. We searched PubMed, Embase and Scopus from 1 January 2019 to 6 February 2023 for peer-reviewed, English-language articles describing age-structured, dynamic, mathematical models of COVID-19 transmission and vaccination in high-income countries that include waning immunity or reinfection. We extracted details of the structure, features and approach of each model and combined them in a narrative synthesis. Of the 1109 articles screened, 47 were included. Most studies used deterministic, compartmental models set in Europe or North America that simulated a time horizon of 3.5 years or less. Common outcomes included cases, hospital utilisation and deaths. Only nine models included long COVID, costs, life years or quality of life-related measures. Two studies explored the potential impact of new variants beyond Omicron. This review demonstrates a need for long-term models that focus on outcome measures such as quality-adjusted life years, the population-level effects of long COVID and the cost effectiveness of future policies - all of which are essential considerations in the planning of long-term vaccination strategies.

Identifiants

pubmed: 39270616
pii: S0033-3506(24)00341-X
doi: 10.1016/j.puhe.2024.07.029
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

207-215

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Auteurs

E Burch (E)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom. Electronic address: eleanor.burch@bristol.ac.uk.

S A Khan (SA)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

J Stone (J)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

A Asgharzadeh (A)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

J Dawe (J)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

Z Ward (Z)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

E Brooks-Pollock (E)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

H Christensen (H)

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

Classifications MeSH