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Elife
. 2024 Sep 25:13:RP91849.
doi: 10.7554/eLife.91849. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
Amanda C Perofsky 1 2 , John Huddleston 3 , Chelsea L Hansen 1 2 , John R Barnes 4 , Thomas Rowe 4 , Xiyan Xu 4 , Rebecca Kondor 4 , David E Wentworth 4 , Nicola Lewis 5 , Lynne Whittaker 5 , Burcu Ermetal 5 , Ruth Harvey 5 , Monica Galiano 5 , Rodney Stuart Daniels 5 , John W McCauley 5 , Seiichiro Fujisaki 6 , Kazuya Nakamura 6 , Noriko Kishida 6 , Shinji Watanabe 6 , Hideki Hasegawa 6 , Sheena G Sullivan 7 , Ian G Barr 7 , Kanta Subbarao 7 , Florian Krammer 8 9 , Trevor Bedford 2 3 10 11 , Cécile Viboud 1
Affiliations
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.
Keywords: H3N2; antigenic drift; epidemiology; global health; human; infectious disease; influenza virus; microbiology; virus.
. 2024 Sep 25:13:RP91849.
doi: 10.7554/eLife.91849. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
Amanda C Perofsky 1 2 , John Huddleston 3 , Chelsea L Hansen 1 2 , John R Barnes 4 , Thomas Rowe 4 , Xiyan Xu 4 , Rebecca Kondor 4 , David E Wentworth 4 , Nicola Lewis 5 , Lynne Whittaker 5 , Burcu Ermetal 5 , Ruth Harvey 5 , Monica Galiano 5 , Rodney Stuart Daniels 5 , John W McCauley 5 , Seiichiro Fujisaki 6 , Kazuya Nakamura 6 , Noriko Kishida 6 , Shinji Watanabe 6 , Hideki Hasegawa 6 , Sheena G Sullivan 7 , Ian G Barr 7 , Kanta Subbarao 7 , Florian Krammer 8 9 , Trevor Bedford 2 3 10 11 , Cécile Viboud 1
Affiliations
- PMID: 39319780
- PMCID: PMC11424097
- DOI: 10.7554/eLife.91849
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.
Keywords: H3N2; antigenic drift; epidemiology; global health; human; infectious disease; influenza virus; microbiology; virus.