Early prediction of antigenic transitions for influenza A/H3N2

Castro, Lauren Ann; Bedford, Trevor; Ancel Meyers, Lauren

doi:10.1371/journal.pcbi.1007683

Title: Early prediction of antigenic transitions for influenza A/H3N2

Journal Article · 17 February 2020 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1007683 · OSTI ID:1604006

^[1]; Bedford, Trevor ^[2];

^[3]

Univ. of Texas, Austin, TX (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Fred Hutchinson Cancer Research Center, Seattle, WA (United States)
Univ. of Texas, Austin, TX (United States); Santa Fe Inst. (SFI), Santa Fe, NM (United States)

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 a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. 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. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Defense Science & Engineering Graduate Fellowship (NDSEG) Program; National Institutes of Health (NIH); National Institute of General Medical Sciences (NIGMS); National Institute of Allergy and Infectious Diseases (NIAID)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1604006

Report Number(s):: LA-UR--19-29731

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 2 Vol. 16; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Early prediction of antigenic transitions for influenza A/H3N2

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