Models to predict the public health impact of vaccine resistance: A systematic review.
Mathematical modeling
Vaccine resistance
Journal
Vaccine
ISSN: 1873-2518
Titre abrégé: Vaccine
Pays: Netherlands
ID NLM: 8406899
Informations de publication
Date de publication:
14 08 2019
14 08 2019
Historique:
received:
24
01
2019
revised:
12
05
2019
accepted:
02
07
2019
pubmed:
17
7
2019
medline:
7
10
2020
entrez:
17
7
2019
Statut:
ppublish
Résumé
Pathogen evolution is a potential threat to the long-term benefits provided by public health vaccination campaigns. Mathematical modeling can be a powerful tool to examine the forces responsible for the development of vaccine resistance and to predict its public health implications. We conducted a systematic review of existing literature to understand the construction and application of vaccine resistance models. We identified 26 studies that modeled the public health impact of vaccine resistance for 12 different pathogens. Most models predicted that vaccines would reduce overall disease burden in spite of evolution of vaccine resistance. Relatively few pathogens and populations for which vaccine resistance may be problematic were covered in the reviewed studies, with low- and middle-income countries particularly under-represented. We discuss the key components of model design, as well as patterns of model predictions.
Identifiants
pubmed: 31307874
pii: S0264-410X(19)30891-6
doi: 10.1016/j.vaccine.2019.07.013
pmc: PMC7094884
mid: NIHMS1535126
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
4886-4895Subventions
Organisme : NICHD NIH HHS
ID : P2C HD042828
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI108490
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM125440
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.
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