RT Journal Article
SR Electronic
T1 Early Prediction of Antigenic Transitions for Influenza A H3N2
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 558577
DO 10.1101/558577
A1 Lauren A Castro
A1 Trevor Bedford
A1 Lauren Ancel Meyers
YR 2019
UL http://biorxiv.org/content/early/2019/02/28/558577.abstract
AB Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using simulations from a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ∼80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. By focusing surveillance efforts on estimating population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions.