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

Abstract

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 cyclemore » to reduce the lead time required for strain selection.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3]
  1. Univ. of Texas, Austin, TX (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Fred Hutchinson Cancer Research Center, Seattle, WA (United States)
  3. Univ. of Texas, Austin, TX (United States); Santa Fe Inst. (SFI), Santa Fe, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
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)
OSTI Identifier:
1604006
Report Number(s):
LA-UR-19-29731
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
89233218CNA000001; U01 GM087719
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 16; Journal Issue: 2; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biological Science; Computer Science

Citation Formats

Castro, Lauren Ann, Bedford, Trevor, and Ancel Meyers, Lauren. Early prediction of antigenic transitions for influenza A/H3N2. United States: N. p., 2020. Web. doi:10.1371/journal.pcbi.1007683.
Castro, Lauren Ann, Bedford, Trevor, & Ancel Meyers, Lauren. Early prediction of antigenic transitions for influenza A/H3N2. United States. doi:https://doi.org/10.1371/journal.pcbi.1007683
Castro, Lauren Ann, Bedford, Trevor, and Ancel Meyers, Lauren. Tue . "Early prediction of antigenic transitions for influenza A/H3N2". United States. doi:https://doi.org/10.1371/journal.pcbi.1007683. https://www.osti.gov/servlets/purl/1604006.
@article{osti_1604006,
title = {Early prediction of antigenic transitions for influenza A/H3N2},
author = {Castro, Lauren Ann and Bedford, Trevor and Ancel Meyers, Lauren},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1007683},
journal = {PLoS Computational Biology (Online)},
number = 2,
volume = 16,
place = {United States},
year = {2020},
month = {2}
}

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