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.