Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases.

Détails

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Etat: Public
Version: Final published version
Licence: CC BY-NC 4.0
ID Serval
serval:BIB_993526317AD7
Type
Article: article d'un périodique ou d'un magazine.
Sous-type
Synthèse (review): revue aussi complète que possible des connaissances sur un sujet, rédigée à partir de l'analyse exhaustive des travaux publiés.
Collection
Publications
Institution
Titre
Scoping future outbreaks: a scoping review on the outbreak prediction of the WHO Blueprint list of priority diseases.
Périodique
BMJ global health
Auteur⸱e⸱s
Jonkmans N., D'Acremont V., Flahault A.
ISSN
2059-7908 (Print)
ISSN-L
2059-7908
Statut éditorial
Publié
Date de publication
09/2021
Peer-reviewed
Oui
Volume
6
Numéro
9
Pages
e006623
Langue
anglais
Notes
Publication types: Journal Article ; Review
Publication Status: ppublish
Résumé
The WHO's Research and Development Blueprint priority list designates emerging diseases with the potential to generate public health emergencies for which insufficient preventive solutions exist. The list aims to reduce the time to the availability of resources that can avert public health crises. The current SARS-CoV-2 pandemic illustrates that an effective method of mitigating such crises is the pre-emptive prediction of outbreaks. This scoping review thus aimed to map and identify the evidence available to predict future outbreaks of the Blueprint diseases.
We conducted a scoping review of PubMed, Embase and Web of Science related to the evidence predicting future outbreaks of Ebola and Marburg virus, Zika virus, Lassa fever, Nipah and Henipaviral disease, Rift Valley fever, Crimean-Congo haemorrhagic fever, Severe acute respiratory syndrome, Middle East respiratory syndrome and Disease X. Prediction methods, outbreak features predicted and implementation of predictions were evaluated. We conducted a narrative and quantitative evidence synthesis to highlight prediction methods that could be further investigated for the prevention of Blueprint diseases and COVID-19 outbreaks.
Out of 3959 articles identified, we included 58 articles based on inclusion criteria. 5 major prediction methods emerged; the most frequent being spatio-temporal risk maps predicting outbreak risk periods and locations through vector and climate data. Stochastic models were predominant. Rift Valley fever was the most predicted disease. Diseases with complex sociocultural factors such as Ebola were often predicted through multifactorial risk-based estimations. 10% of models were implemented by health authorities. No article predicted Disease X outbreaks.
Spatiotemporal models for diseases with strong climatic and vectorial components, as in River Valley fever prediction, may currently best reduce the time to the availability of resources. A wide literature gap exists in the prediction of zoonoses with complex sociocultural and ecological dynamics such as Ebola, COVID-19 and especially Disease X.
Mots-clé
Animals, COVID-19, Disease Outbreaks/prevention & control, Hemorrhagic Fever, Ebola/epidemiology, Hemorrhagic Fever, Ebola/prevention & control, Humans, SARS-CoV-2, World Health Organization, Zika Virus, Zika Virus Infection, SARS, geographic information systems, mathematical modelling, systematic review, viral haemorrhagic fevers
Pubmed
Web of science
Open Access
Oui
Création de la notice
01/10/2021 17:55
Dernière modification de la notice
13/03/2022 8:11
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