Early Recognition of Emerging Flu Strain Clusters

Minimizing time delays in manufacturing vaccines appropriate to rapidly mutating viruses is the key step for improving vaccine effectiveness. The vaccine for the H3N2 flu type has failed for the last two years (~ 15% effective). Here we summarize the state of the predictive art and report the most current results for H3N2 flu vaccine design. Using a 2006 model of dimensional reduction of viral mutational complexity, we show that this model can reduce vaccine time delays by a year or more in some cases. Viruses are hypermutated proteins, with nearly one mutation per replication [1]. This rapid mutation allows selection for viruses that succeed in evading antibody-mediated immunity. In a computational view the most successful strains tend to form clusters that are visible in a reduced, two-dimensional sequence space. This cluster of strains remains until sufficiently recognized by antibodies in the human population [2], at which point selection leads to a new antibody-evading strain, which initiates a new cluster. This process is a dramatic example of punctuated evolution [3-5], and it is modelled with cluster-specific immunity [6]. There are several types of flu viruses, and the current vaccine cocktail treats the most common three. The oldest type A/H1N1 was responsible for both the deadly 1918 and the comparatively harmless 2009 “swine flu” pandemics. The A/H1N1 type strains have stabilized, are milder, and are more effectively vaccinated against. More morbidity and mortality are now associated with rapidly mutating A/H3N2 strains, which are discussed here. There are two major problems facing the vaccination community. The first is the lengthy temporal delay between the arrival of new evasive viral strains, their reported sequencing, and determination of suitability of new strains as vaccine targets. The second is the effect of mutations in the currently circulating strains on the effectiveness of vaccines based on older strains, often including another