Projection of seasonal influenza severity from sequence and serological data

[tetano]

Plos Currents


Severity of seasonal influenza A epidemics is related to the antigenic novelty of the predominant viral strains circulating each year. Support for a strong correlation between epidemic severity and antigenic drift comes from infectious challenge experiments on vaccinated animals and human volunteers, field studies of vaccine efficacy, prospective studies of subjects with laboratory-confirmed prior infections, and analysis of the connection between drift and severity from surveillance data. We show that, given data on the antigenic and sequence novelty of the hemagglutinin protein of clinical isolates of H3N2 virus from a season along with the corresponding data from prior seasons, we can accurately predict the influenza severity for that season. This model therefore provides a framework for making projections of the severity of the upcoming season using assumptions based on viral isolates collected in the current season. Our results based on two independent data sets from the US and Hong Kong suggest that seasonal severity is largely determined by the novelty of the hemagglutinin protein although other factors, including mutations in other influenza genes, co-circulating pathogens and weather conditions, might also play a role. These results should be helpful for the control of seasonal influenza and have implications for improvement of influenza surveillance.


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[tetano]

Re: Projection of seasonal influenza severity from sequence and serological data

Researchers develop statistical model to predict severity of seasonal influenza
9. December 2010 04:45
Model could be useful in planning process for seasonal influenza

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".

http://www.news-medical.net/news/20101209/Researchers-develop-statistical-model-to-predict-severity-of-seasonal-influenza.aspx