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. 2014 Oct 16:3:e03883.
doi: 10.7554/eLife.03883.

Improving pandemic influenza risk assessment

Colin A Russell  1 , Peter M Kasson  2 , Ruben O Donis  3 , Steven Riley  4 , John Dunbar  5 , Andrew Rambaut  6 , Jason Asher  7 , Stephen Burke  8 , C Todd Davis  9 , Rebecca J Garten  10 , Sandrasegaram Gnanakaran  11 , Simon I Hay  12 , Sander Herfst  13 , Nicola S Lewis  14 , James O Lloyd-Smith  15 , Catherine A Macken  16 , Sebastian Maurer-Stroh  17 , Elizabeth Neuhaus  18 , Colin R Parrish  19 , Kim M Pepin  20 , Samuel S Shepard  21 , David L Smith  22 , David L Suarez  23 , Susan C Trock  24 , Marc-Alain Widdowson  25 , Dylan B George  26 , Marc Lipsitch  27 , Jesse D Bloom  28
Affiliations

Improving pandemic influenza risk assessment

Colin A Russell et al. Elife. .

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: emergence; evolutionary biology; genomics; human; infectious disease; influenza; microbiology; pandemic; viruses.

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Conflict of interest statement

SIH: Reviewing editor, eLife.

The other authors declare that no competing interests exist.

Figures

Figure 1.
Figure 1.. Evidence for concern and actions to mitigate influenza pandemics.
Types of evidence that have been, or could be, used to justify specific preparedness or mitigation actions prior to evidence of sustained human-to-human transmission, largely based on the authors' interpretation of national and international responses to H5N1, H7N9, and H3N2v outbreaks (Epperson et al., 2013, WHO, 2011). Red indicates largely sufficient, orange partly sufficient, yellow minimally sufficient, gray insufficient. * high pathogenicity phenotype as defined by the World Organization for Animal Health (OIE)(OIE, 2013). DOI: http://dx.doi.org/10.7554/eLife.03883.002
Figure 2.
Figure 2.. Schematic of potential relationships from virus genetic sequence to level of public health concern/pandemic risk.
Pandemic risk is a combination of the probability that a virus will cause a pandemic and the human morbidity and mortality that might result from that pandemic. Arrows represent possible relationships between levels and are not intended to summarize current knowledge. DOI: http://dx.doi.org/10.7554/eLife.03883.003
Figure 3.
Figure 3.. Geographic distribution of publicly available influenza virus genetic sequence data in comparison to poultry and swine populations.
(A) Proportions of worldwide animal population by country (data from the Food and Agriculture Organization of the United Nations). (B) Number of unique influenza viruses for which sequence data exists in public databases from poultry or swine by country. Numbers of influenza virus sequences are not representative of influenza virus surveillance activities. Information regarding surveillance activities is not readily available. Virological surveillance, even if robust, may result in negative findings and is not captured in these figures. Most countries do not sequence every influenza virus isolate and some countries conduct virological surveillance without sharing sequence data publicly. Sequences deposited in public databases can reflect uneven geographic distribution and interest regarding viruses of concern such as H5N1 and H9N2. DOI: http://dx.doi.org/10.7554/eLife.03883.004
See this image and copyright information in PMC

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