Improving pandemic influenza risk assessment

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Author
Russell, Colin A
Kasson, Peter M
Donis, Ruben O
Riley, Steven
Dunbar, John
Rambaut, Andrew
Asher, Jason
Burke, Stephen
Davis, C Todd
Garten, Rebecca J
Gnanakaran, Sandrasegaram
Hay, Simon I
Herfst, Sander
Lewis, Nicola S
Lloyd-Smith, James O
Macken, Catherine A
Maurer-Stroh, Sebastian
Neuhaus, Elizabeth
Parrish, Colin R
Pepin, Kim M
Shepard, Samuel S
Smith, David L
Suarez, David L
Trock, Susan C
Widdowson, Marc-Alain
George, Dylan B
Bloom, Jesse D
Note: Order does not necessarily reflect citation order of authors.
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https://doi.org/10.7554/eLife.03883Metadata
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Russell, C. A., P. M. Kasson, R. O. Donis, S. Riley, J. Dunbar, A. Rambaut, J. Asher, et al. 2014. “Improving pandemic influenza risk assessment.” eLife 3 (1): e03883. doi:10.7554/eLife.03883. http://dx.doi.org/10.7554/eLife.03883.Abstract
Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response. DOI: http://dx.doi.org/10.7554/eLife.03883.001Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199076/pdf/Terms of Use
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