Viruses evolve genetically in immunopriviledged sites in order to escape immune recognition. To deal with these, the host’s immune system generates new memory cells that can recognise the mutated viral strains. The authors were interested in the observed speed of evolution and the incidence of infection. The authors observed a relationship between long-term cross-immunity, genetic diversity, speed of evolution and incidence. They developed a methodology that combines the standard epidemiological susceptible-infected-recovered model with modern virus evolution theory. The combined model includes the factors of strain selection due to immune memory cells, random genetic drift and clonal interference effects. Using this, the authors were able to predict that the distribution of memory serotypes in recovered patients is able to create a moving fitness landscape for circulating strains, in turn driving antigenic escape. They found that the fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity, R0, and inversely proportional to the cross-immunity distance, a. The latter is defined as the genetic distance of a virus strain from the previously infecting strain conferring 50% decrease in infection probability. The model suggests that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. When the authors fitted this new combined model to influenza A(H3N2) and A(H1N1), they were able to predict the annual infection incidence within a previously estimated range of 4-7%, and the antigenic mutation rate of 5−8 x 10^−4 per transmission event per genome. The predicted cross-immunity distance from the model of 14−15 amino acid substitutions also agrees with independent data for equine influenza.
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