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Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Morris, Dylan H. and Gostic, Katelyn M. and Pompei, Simone and Bedford, Trevor and Łuksza, Marta and Neher, Richard A. and Grenfell, Bryan T. and Lässig, Michael and McCauley, John W.. (2017) Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends in Microbiology, 26 (2). p. 17.

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Official URL: http://edoc.unibas.ch/57583/

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Abstract

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Developed through data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Computational Modeling of Biological Processes (Neher)
UniBasel Contributors:Neher, Richard A
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:0966-842X
e-ISSN:1878-4380
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:18 Sep 2019 07:28
Deposited On:30 May 2018 06:43

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