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
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
207-215Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.