Computational Prediction of Vaccine Strains for Human Influenza A (H3N2) Viruses

Abstract

Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. IMPORTANCEHuman influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.

Burden of medically attended influenza infection and cases averted by vaccination – United States, 2013/14 through 2015/16 influenza seasons

“…[17] This lead time requires public health officials to predict which candidate vaccine viruses will best match the viruses that will circulate in the upcoming season. [18] This can lead to antigenic differences between the vaccine viruses and circulating viruses, as was seen in the 2014/15 influenza season, when the vaccine lacked effectiveness against the predominant A(H3N2) virus circulating in the United States that season. [5] However, despite the lower degree of similarity to circulating viruses, this study suggests that the 2014/15 influenza vaccine was still beneficial, with a statistically significant number of cases estimated to have been averted by vaccination for age groups at highest risk of complications following influenza infection.…”

Selection of antigenically advanced variants of seasonal influenza viruses

“…Here, we provide a methodology that identified H1N1 and H3N2 antigenic clusters similar to those observed in nature. These data can be generated before the variants are detected in nature; in this regard, our approach is conceptually different from methods that predict which circulating variants may become dominant 32-35 . Our method may therefore improve the current WHO influenza vaccine selection process.…”

Vaccine escape in 2013–4 and the hydropathic evolution of glycoproteins of A/H3N2 viruses

“…One would expect that application of these methods to both HA and NA sequenced stages of vaccine formation from deactivated viruses might be useful both for improving vaccine protocols and monitoring vaccine quality on-line during production, but this topic lies outside the scope of this paper. [2], 2012 [3], 2013 [4], 2014 (Jan.) [5] and 2014 Nov. [6]. The [1] - [4] specimens were chosen using BLAST to identify strains evolving towards the escaped Alaska and New York/04/2014 strains [6].…”

“…[2], 2012 [3], 2013 [4], 2014 (Jan.) [5] and 2014 Nov. [6]. The [1] - [4] specimens were chosen using BLAST to identify strains evolving towards the escaped Alaska and New York/04/2014 strains [6]. Singapore H2013.910 was collected 11/28/2013, and submitted to the NIAID -NCBI data base 6/26/2014, which was too late for use as a target strain for the 2014.…”