Improving pandemic influenza risk assessment

Colin A Russell; Peter M Kasson; Ruben O Donis; Steven Riley; John Dunbar; Andrew Rambaut; Jason Asher; Stephen Burke; C Todd Davis; Rebecca J Garten; Sandrasegaram Gnanakaran; Simon I Hay; Sander Herfst; Nicola S Lewis; James O Lloyd-Smith; Catherine A Macken; Sebastian Maurer-Stroh; Elizabeth Neuhaus; Colin R Parrish; Kim M Pepin; Samuel S Shepard; David L Smith; David L Suarez; Susan C Trock; Marc-Alain Widdowson; Dylan B George; Marc Lipsitch; Jesse D Bloom

doi:10.7554/eLife.03883

Improving pandemic influenza risk assessment

Colin A Russell, Peter M Kasson, Ruben O Donis, Steven Riley, John Dunbar, Andrew Rambaut, Jason Asher, Stephen Burke, C Todd Davis, Rebecca J Garten, Sandrasegaram Gnanakaran, Simon I Hay, Sander Herfst, Nicola S Lewis, James O Lloyd-Smith, Catherine A Macken, Sebastian Maurer-Stroh, Elizabeth Neuhaus, Colin R Parrish, Kim M PepinSamuel S Shepard, David L Smith, David L Suarez, Susan C Trock, Marc-Alain Widdowson, Dylan B George, Marc Lipsitch, Jesse D Bloom

Institute of Tropical Medicine

Research output: Contribution to journal › A1: Web of Science-article › peer-review

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.

Original language	English
Article number	e03883
Journal	eLIFE
Volume	3
ISSN	2050-084X
DOIs	https://doi.org/10.7554/eLife.03883
Publication status	Published - 2014

Keywords

Base Sequence
Biological Evolution
Epidemiological Monitoring
Geography
Humans
Influenza A virus/genetics
Influenza, Human/epidemiology
Models, Biological
Pandemics/prevention & control
Public Health
Risk Assessment/methods

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10.7554/eLife.03883

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Russell, C. A., Kasson, P. M., Donis, R. O., Riley, S., Dunbar, J., Rambaut, A., Asher, J., Burke, S., Davis, C. T., Garten, R. J., Gnanakaran, S., Hay, S. I., Herfst, S., Lewis, N. S., Lloyd-Smith, J. O., Macken, C. A., Maurer-Stroh, S., Neuhaus, E., Parrish, C. R., ... Bloom, J. D. (2014). Improving pandemic influenza risk assessment. eLIFE, 3, Article e03883. https://doi.org/10.7554/eLife.03883

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title = "Improving pandemic influenza risk assessment",

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. ",

keywords = "Base Sequence, Biological Evolution, Epidemiological Monitoring, Geography, Humans, Influenza A virus/genetics, Influenza, Human/epidemiology, Models, Biological, Pandemics/prevention & control, Public Health, Risk Assessment/methods",

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doi = "10.7554/eLife.03883",

language = "English",

volume = "3",

journal = "eLIFE",

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Russell, CA, Kasson, PM, Donis, RO, Riley, S, Dunbar, J, Rambaut, A, Asher, J, Burke, S, Davis, CT, Garten, RJ, Gnanakaran, S, Hay, SI, Herfst, S, Lewis, NS, Lloyd-Smith, JO, Macken, CA, Maurer-Stroh, S, Neuhaus, E, Parrish, CR, Pepin, KM, Shepard, SS, Smith, DL, Suarez, DL, Trock, SC, Widdowson, M-A, George, DB, Lipsitch, M & Bloom, JD 2014, 'Improving pandemic influenza risk assessment', eLIFE, vol. 3, e03883. https://doi.org/10.7554/eLife.03883

TY - JOUR

T1 - Improving pandemic influenza risk assessment

AU - Russell, Colin A

AU - Kasson, Peter M

AU - Donis, Ruben O

AU - Riley, Steven

AU - Dunbar, John

AU - Rambaut, Andrew

AU - Asher, Jason

AU - Burke, Stephen

AU - Davis, C Todd

AU - Garten, Rebecca J

AU - Gnanakaran, Sandrasegaram

AU - Hay, Simon I

AU - Herfst, Sander

AU - Lewis, Nicola S

AU - Lloyd-Smith, James O

AU - Macken, Catherine A

AU - Maurer-Stroh, Sebastian

AU - Neuhaus, Elizabeth

AU - Parrish, Colin R

AU - Pepin, Kim M

AU - Shepard, Samuel S

AU - Smith, David L

AU - Suarez, David L

AU - Trock, Susan C

AU - Widdowson, Marc-Alain

AU - George, Dylan B

AU - Lipsitch, Marc

AU - Bloom, Jesse D

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Base Sequence

KW - Biological Evolution

KW - Epidemiological Monitoring

KW - Geography

KW - Humans

KW - Influenza A virus/genetics

KW - Influenza, Human/epidemiology

KW - Models, Biological

KW - Pandemics/prevention & control

KW - Public Health

KW - Risk Assessment/methods

U2 - 10.7554/eLife.03883

DO - 10.7554/eLife.03883

M3 - A1: Web of Science-article

C2 - 25321142

SN - 2050-084X

VL - 3

JO - eLIFE

JF - eLIFE

M1 - e03883

ER -

Improving pandemic influenza risk assessment

Abstract

Keywords

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