Researchers have developed a statistical model for projecting how many people will get sick from seasonal influenza based on analyses of flu viruses circulating that season. The research, conducted by scientists at the National Institutes of Health, appears today in the open-access publication PLoS Currents: Influenza.
Building on other research that has shown that severity of infections with the Influenza A virus is related to its novelty (i.e., how much the virus has changed, or mutated, from prior seasons), the study evaluated the correlation between virus novelty and the epidemiologic severity of influenza from the 1993/1994 flu season through the 2008/2009 season. Virus novelty was assessed through analysis of genetic data (sequences of hemagglutinin proteins from virus samples) and serological data (hemagglutinin inhibition results). The research focused on H3N2 influenza, the influenza subtype responsible for the most severe influenza seasons during inter-pandemic periods.
The results showed that more than 90% of the variation in influenza severity over the periods studied could be explained by the novelty of the virus’ hemagglutinin protein.
The researchers also assessed whether influenza sequence and serological data for viruses isolated in the Southern Hemisphere influenza season correlated with influenza severity that occurred in the later influenza season in the Northern Hemisphere. Results showed that the projections explained 66% of the variance in severity in the Northern Hemisphere.
The ability to accurately predict influenza severity suggests that with appropriate surveillance methods, scientists could make more informed decisions in planning for influenza, including the selection of vaccines. For example, in selecting a vaccine for the coming season, it would be helpful to know that one circulating virus in the current season was likely to produce much more severe influenza than the other circulating viruses.
Edward Holmes (The Pennsylvania State University), an expert on the evolution of flu viruses, and one of the Editors of PLoS Currents: Influenza commented: “this paper represents a major step forward in our ability to predict the behavior of influenza and simultaneously opens up a new field of study”.
Funding: This research was supported by the Intramural Research Program of the NCBI/NLM/NIH and FIC/NIH.
Competing Interests: David Lipman, corresponding author, is one of the Editors of PLoS Currents: Influenza
Citation: Wolf, Yuri I; Nikolskaya, Anastasia; Cherry, Joshua L.; Viboud, Cecile; Koonin, Eugene; Lipman, David J. Projection of seasonal influenza severity from sequence and serological data. PLoS Currents: Influenza. 2010 Dec 6th
Link to the freely available article, published today: http://knol.google.com/k/yuri-i-wolf/projection-of-seasonal-influenza/agr0htar1u6r/22
About PLoS Currents
PLoS Currents is an open-access publication for the extremely rapid communication of new research findings, which minimizes the delay between submission and publication. PLoS Currents is organized in sections that cover particular topics. The first section was launched in August 2009, and focused on influenza research. In 2010 the project was extended and now includes the following sections: Huntington Disease (produced with support from the CHDI Foundation); Evidence on Genomic Tests; Influenza; and Tree of Life
There are two key features that make PLoS Currents different, and much faster, than a conventional journal. First, each section of PLoS Currents is run by a group of expert researchers – led by two or three Editors — who determine as rapidly as possible if the conception, structure and presentation of a given submission indicates that it is a legitimate work of science which does not contain any obvious methodological, ethical or legal violations. As long as the work passes this test, it is published.
The second key difference from conventional journals is that submissions to PLoS Currents are written and published using a web-based tool, called google knol. Authors are in complete control over the content and appearance of their submission, and once it has passed the review process, articles are published immediately. Upon publication they are also archived at PubMed Central, where they are given a unique ID, so that the work can be cited.
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