Optimizing age specific strategies of vaccination for prevention of cytomegalovirus infection in the US using agent-based simulation.


Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
09 2023
Historique:
received: 23 06 2022
revised: 03 06 2023
accepted: 12 06 2023
medline: 8 9 2023
pubmed: 25 6 2023
entrez: 24 6 2023
Statut: ppublish

Résumé

There is an urgent need to develop a cytomegalovirus (CMV) vaccine as it remains the leading cause of birth defects in the United States. While several CMV vaccine candidates are currently in late-stage clinical trials, the most effective vaccination program remains an open research question. To take into account the critical uncertainties when evaluating the vaccine impact on both vertical (congenital) and horizontal CMV transmissions, we developed a CMV agent-based model representative of the US population and contact network structures. We evaluated 648 vaccination scenarios under various assumptions of vaccination age, vaccine efficacy, protection duration, and vaccination coverage. The optimal age of vaccination under all scenarios is shown to be during early childhood. However, a relatively modest benefit was also seen with vaccination of females of reproduction age (around age of 25) assuming near universal coverage and long vaccine-mediated protection. This study highlights the important need for a pediatric vaccination program in mitigating CMV in the United States. Our model is poised to investigate further location-based vaccine effectiveness questions in future planning of both clinical trials as well as eventual program implementation.

Sections du résumé

BACKGROUND
There is an urgent need to develop a cytomegalovirus (CMV) vaccine as it remains the leading cause of birth defects in the United States. While several CMV vaccine candidates are currently in late-stage clinical trials, the most effective vaccination program remains an open research question.
METHODS
To take into account the critical uncertainties when evaluating the vaccine impact on both vertical (congenital) and horizontal CMV transmissions, we developed a CMV agent-based model representative of the US population and contact network structures.
RESULTS
We evaluated 648 vaccination scenarios under various assumptions of vaccination age, vaccine efficacy, protection duration, and vaccination coverage. The optimal age of vaccination under all scenarios is shown to be during early childhood. However, a relatively modest benefit was also seen with vaccination of females of reproduction age (around age of 25) assuming near universal coverage and long vaccine-mediated protection.
CONCLUSIONS
This study highlights the important need for a pediatric vaccination program in mitigating CMV in the United States. Our model is poised to investigate further location-based vaccine effectiveness questions in future planning of both clinical trials as well as eventual program implementation.

Identifiants

pubmed: 37354657
pii: S1755-4365(23)00034-8
doi: 10.1016/j.epidem.2023.100698
pii:
doi:

Substances chimiques

Cytomegalovirus Vaccines 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100698

Informations de copyright

Copyright © 2023 Merck Sharp & Dohme LLC., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Dawei Wang (D)

Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA. Electronic address: dawei.wang@merck.com.

Yao-Hsuan Chen (YH)

Health Economic and Decision Sciences, MSD, Kings Cross, London, UK.

Mario Ventresca (M)

School of Industrial Engineering, Purdue University, 315 Grant St, West Lafayette, IN 47906, USA.

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Classifications MeSH