Improving pandemic influenza risk assessment

Authors

Colin A. Russell, University of CambridgeFollow
Peter M. Kasson, University of Virginia
Ruben O. Donis, Centers for Disease Control and PreventionFollow
Steven Riley, School of Public Health
John Dunbar, Los Alamos National Laboratory
Andrew Rambaut, University of Edinburgh
Jason Asher, Department of Health and Human Services
Stephen Burke, Centers for Disease Control and Prevention
C. Todd Davis, Centers for Disease Control and Prevention
Rebecca J. Garten, Centers for Disease Control and Prevention
Sandrasegaram Gnanakaran, Los Alamos National Laboratory
Simon I. Hay, University of Oxford
Sander Herfst, Postgraduate School of Molecular Medicine
Nicola S. Lewis, University of Cambridge
James O. Lloyd-Smith, University of California, Los Angeles
Catherine A. Macken, Los Alamos National Laboratory
Sebastian Maurer-Stroh, Nanyang Technological University
Elizabeth Neuhaus, Centers for Disease Control and Prevention
Colin R. Parrish, Cornell UniversityFollow
Kim M. Pepin, USDA/APHIS/WS National Wildlife Research CenterFollow
Samuel S. Shepard, Centers for Disease Control and Prevention
David L. Smith, University of Oxford
David L. Suarez, USDA
Susan C. Trock, Centers for Disease Control and Prevention
Marc-Alain Widdowson, Centers for Disease Control and Prevention
Dylan B. George, National Institutes of Health
Marc Lipsitch, Harvard School of Public Health
Jesse D. Bloom, Fred Hutchinson Cancer Research Center

Document Type

Article

Date of this Version

2014

Citation

Russell et al. eLife 2014;3:e03883. DOI: 10.7554/eLife.03883

Comments

Open Access.

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.