Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, United States
- Department of Genome Sciences, University of Washington, United States
- Howard Hughes Medical Institute, United States
Figures
Figure 1 with 3 supplements
Annual influenza A(H3N2) epidemics in the United States, 1997 – 2019.
(A) Weekly incidence of influenza A(H1N1) (blue), A(H3N2) (red), and B (green) averaged across 10 HHS regions (Region 1: Boston; Region 2: New York City; Region 3: Washington, DC; Region 4: Atlanta; …
Figure 1—figure supplement 1
Annual influenza A(H1N1) and influenza B epidemics in the United States, 1997 - 2019.
Intensity of weekly (A) influenza A(H1N1) and (B) influenza B incidence in 10 HHS regions. Incidences are the proportion of influenza-like illness (ILI) visits among all outpatient visits, …
Figure 1—figure supplement 2
Influenza test volume systematically increases in all HHS regions after the 2009 A(H1N1) pandemic.
Each point represents the total number of influenza tests in each HHS region in each season, as reported by the U.S. CDC WHO Collaborating Center for Surveillance, Epidemiology and Control of …
Figure 1—figure supplement 3
Pairwise correlations between seasonal influenza A(H3N2), A(H1N1), and B epidemic metrics.
Spearman’s rank correlations among indicators of A(H3N2) epidemic timing, including onset week, peak week, regional variation (s.d.) in onset and peak timing, the number of days from epidemic onset …
Figure 2 with 7 supplements
Antigenic and genetic evolution of seasonal influenza A(H3N2) viruses, 1997 – 2019.
(A–B) Temporal phylogenies of (A) hemagglutinin (H3) and (B) neuraminidase (N2) gene segments. Tip color denotes the Hamming distance from the root of the tree, based on the number of substitutions …
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Figure 2—source data 1
A/H3 sequence counts in five subsampled datasets.
We downloaded all H3 sequences and associated metadata from the GISAID EpiFlu database and focused our analysis on complete H3 sequences that were sampled between January 1, 1997, and October 1, 2019. To account for variation in sequence availability across global regions, we subsampled the selected sequences five times to representative sets of no more than 50 viruses per month, with preferential sampling for North America. Each month up to 25 viruses were selected from North America (when available) and up to 25 viruses were selected from nine other global regions (when available), with even sampling across the other global regions (China, Southeast Asia, West Asia, Japan and Korea, South Asia, Oceania, Europe, South America, and Africa).
- https://cdn.elifesciences.org/articles/91849/elife-91849-fig2-data1-v1.xlsx
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Figure 2—source data 2
A/N2 sequence counts in five subsampled datasets.
We downloaded all N2 sequences and associated metadata from the GISAID EpiFlu database and focused our analysis on complete N2 sequences that were sampled between January 1, 1997, and October 1, 2019. To account for variation in sequence availability across global regions, we subsampled the selected sequences five times to representative sets of no more than 50 viruses per month, with preferential sampling for North America. Each month up to 25 viruses were selected from North America (when available) and up to 25 viruses were selected from nine other global regions (when available), with even sampling across the other global regions (China, Southeast Asia, West Asia, Japan and Korea, South Asia, Oceania, Europe, South America, and Africa).
- https://cdn.elifesciences.org/articles/91849/elife-91849-fig2-data2-v1.xlsx
Figure 2—figure supplement 1
The number of A/H3 sequences in five subsampled datasets in each month and in each influenza season.
In each figure, the five subsampled datasets are plotted individually but individual time series are difficult to discern due to minor differences in sequence counts across the datasets. (A) The …
Figure 2—figure supplement 2
The number of A/N2 sequences in five subsampled datasets in each month and in each influenza season.
In each figure, the five subsampled datasets are plotted individually but individual time series are difficult to discern due to minor differences in sequence counts across the datasets. (A) The …
Figure 2—figure supplement 3
Comparison of seasonal antigenic drift measured by substitutions at H3 epitope sites and HI log2 titer measurements, from seasons 1997–1998 to 2018–2019.
Spearman’s rank correlations between H3 epitope distance and HI log2 titer distance at (A) one-season lags and (B) two-season lags. Correlation coefficients and associated p-values are shown in the …
Figure 2—figure supplement 4
Pairwise correlations between H3 and N2 evolutionary indicators (one-season lags).
Spearman’s rank correlations between seasonal measures of H3 and N2 evolution, including H3 RBS distance, H3 epitope distance, H3 non-epitope distance, H3 stalk footprint distance, HI log2 titer …
Figure 2—figure supplement 5
Pairwise correlations between H3 and N2 evolutionary indicators (two-season lags).
We measured Spearman’s rank correlations between seasonal measures of H3 and N2 evolution, including H3 RBS distance, H3 epitope distance, H3 non-epitope distance, H3 stalk footprint distance, HI …
Figure 2—figure supplement 6
Pairwise correlations between H3 and N2 evolutionary indicators (one- and two-season lags).
We measured Spearman’s rank correlations between seasonal measures of H3 and N2 evolution, including H3 RBS distance, H3 epitope distance, H3 non-epitope distance, H3 stalk footprint distance, HI …
Figure 2—figure supplement 7
Comparison of seasonal antigenic drift measured by substitutions at H3 and N2 epitope sites, from seasons 1997–1998 to 2018–2019.
Spearman’s rank correlations between H3 epitope distance and N2 epitope distance at (A) one-season lags and (B) two-season lags. Correlation coefficients and associated p-values are shown in the top …
Figure 3 with 4 supplements
Influenza A(H3N2) antigenic drift correlates with larger, more intense annual epidemics.
A(H3N2) epidemic size, peak incidence, transmissibility (effective reproduction number, ), and epidemic intensity increase with antigenic drift, measured by (A) hemagglutinin (H3) epitope …
Figure 3—figure supplement 1
Univariate correlations between influenza A(H3N2) evolutionary indictors and epidemic impact.
Mean Spearman’s rank correlation coefficients, 95% confidence intervals of correlation coefficients, and corresponding p-values of bootstrapped (N=1000) evolutionary indicators (rows) and epidemic …
Figure 3—figure supplement 2
Excess influenza A(H3N2) mortality increases with H3 and N2 epitope distance, but correlations are not statistically significant.
Relationships between seasonal excess influenza A(H3N2) mortality and epitope distance are organized by gene segment and age group: (A) H3 epitope distance and all age groups, (B) H3 epitope …
Figure 3—figure supplement 3
Low seasonal diversity in the clade growth rates of circulating A(H3N2) viruses, as measured by the standard deviation of local branching index values, correlates with higher transmissibility and greater epidemic intensity.
A(H3N2) effective and epidemic intensity negatively correlate with the seasonal diversity of local branching index (LBI) values among circulating A(H3N2) lineages in the current season, measured …
Figure 3—figure supplement 4
Low seasonal diversity in the clade growth rates of circulating A(H3N2) viruses, as measured by the Shannon diversity of local branching index values, correlates with higher transmissibility and greater epidemic intensity.
A(H3N2) effective and epidemic intensity negatively correlate with the seasonal diversity of local branching index (LBI) values among circulating A(H3N2) lineages in the current season, measured …
Figure 4 with 1 supplement
The proportion of influenza positive samples typed as A(H3N2) increases with antigenic drift.
(A-B) Seasonal A(H3N2) subtype dominance increases with (A) hemagglutinin (H3) and (B) neuraminidase (N2) epitope distance. Seasonal epitope distance is the mean epitope distance between viruses …
Figure 4—figure supplement 1
Regional patterns of influenza type and subtype circulation during seasons 1997–1998 to 2018–2019.
Pie charts represent the proportion of influenza positive samples that were typed as A(H3N2), A(H1N1) or A(H1N1)pdm09, and B in each HHS region. Data for Region 10 (purple) are not available for …
Figure 5 with 3 supplements
Influenza A(H3N2) seasonal duration increases with the diversity of hemagglutinin (H3) and neuraminidase (N2) clade growth rates in each season.
Seasonal diversity of clade growth rates is measured as the (A) Shannon diversity or (B) standard deviation (s.d.) of H3 and N2 local branching index (LBI) values of viruses circulating in each …
Figure 5—figure supplement 1
Univariate correlations between influenza A(H3N2) evolutionary indicators and epidemic timing.
Mean Spearman’s rank correlation coefficients, 95% confidence intervals of correlation coefficients, and corresponding p-values of bootstrapped (N=1000) evolutionary indicators (columns) and …
Figure 5—figure supplement 2
Epidemic speed increases with N2 antigenic drift.
N2 epitope distance significantly correlates with fewer days from epidemic onset to peak (A), while the relationship between H3 epitope distance and epidemic speed is weaker (B). Seasonal epitope …
Figure 5—figure supplement 3
Influenza A(H3N2) epidemic onsets and peaks are earlier in seasons with high antigenic novelty, but correlations are not statistically significant.
(A) Epidemic onsets are earlier in seasons with increased H3 epitope distance ( – 2), but the correlation is not statistically significant. (B) Epidemic peaks are earlier in seasons with increased …
Figure 6 with 1 supplement
The proportion of outpatient influenza-like illness (ILI) cases in adults increases with neuraminidase (N2) antigenic novelty.
N2 epitope distance, but not H3 epitope distance, significantly correlates with the age distribution of outpatient ILI cases. Seasonal epitope distance is the mean distance between viruses …
Figure 6—figure supplement 1
Univariate correlations between A(H3N2) antigenic change and the age distribution of outpatient influenza-like illness (ILI) cases.
Mean Spearman’s rank correlation coefficients, 95% confidence intervals of correlation coefficients, and corresponding p-values of bootstrapped (N=1000) evolutionary indicators (rows) and the …
Figure 7 with 3 supplements
The effects of influenza A(H1N1) and B epidemic size on A(H3N2) epidemic burden.
(A) Influenza A(H1N1) epidemic size negatively correlates with A(H3N2) epidemic size, peak incidence, transmissibility (effective reproduction number, ), and epidemic intensity. (B) Influenza B …
Figure 7—figure supplement 1
National excess influenza A(H3N2) mortality decreases with A(H1N1) epidemic size but not B epidemic size.
Relationships between seasonal excess influenza A(H3N2) mortality and the circulation of A(H1N1) or B viruses are organized by influenza type/subtype and age group: (A) A(H1N1) epidemic size and all …
Figure 7—figure supplement 2
The effect of influenza A(H1N1) epidemic size on A(H3N2) epidemic burden during the entire study period, pre-2009 seasons, and post-2009 seasons.
Influenza A(H1N1) epidemic size negatively correlates with A(H3N2) epidemic size, peak incidence, transmissibility (maximum effective reproduction number, ), and epidemic intensity during (A) the …
Figure 7—figure supplement 3
Wavelet analysis of influenza A(H3N2), A(H1N1), and B epidemic timing.
(A) A(H3N2) incidence precedes A(H1N1) incidence in most seasons. Although A(H1N1) incidence sometimes leads or is in phase with A(H3N2) incidence (negative or zero phase lags), the direction of …
Figure 8 with 1 supplement
Variable importance rankings from conditional inference random forest models predicting seasonal region-specific influenza A(H3N2) epidemic dynamics.
Ranking of variables in predicting regional A(H3N2) (A) epidemic size, (B) peak incidence, (C) transmissibility (maximum effective reproduction number, ), (D) epidemic intensity, and (E) subtype …
Figure 8—figure supplement 1
Variable importance rankings from LASSO regression models predicting seasonal region-specific influenza A(H3N2) epidemic dynamics.
Ranking of variables in predicting regional A(H3N2) (A) epidemic size, (B) peak incidence, (C) transmissibility (maximum effective reproduction number, ), (D) epidemic intensity, and (E) subtype …
Figure 9 with 2 supplements
Observed versus predicted values of seasonal region-specific influenza A(H3N2) epidemic metrics from conditional inference random forest models.
(A) Epidemic size, (B) peak incidence, (C) transmissibility (maximum effective reproduction number, ), (D) epidemic intensity, and (E) subtype dominance. Results are facetted by HHS region and …
Figure 9—figure supplement 1
Relationships between the predictive accuracy of random forest models and seasonal H3 epitope distance.
Root mean squared errors between observed and model-predicted values were averaged across regions for each season, and results are facetted according to epidemic metric. Point color corresponds to …
Figure 9—figure supplement 2
Relationships between the predictive accuracy of random forest models and seasonal N2 epitope distance.
Root mean squared errors between observed and model-predicted values were averaged across regions for each season, and results are facetted according to epidemic metric. Point color corresponds to …
Author response image 1
Adjustment for pre- and post-2009 pandemic only.
Author response image 2
Adjustment for pre- and post-2009 pandemic only.
Tables
Table 1
Evolutionary indicators of seasonal viral fitness.
Evolutionary indicators are labeled by the influenza gene for which data are available (hemagglutinin, HA or neuraminidase, NA), the type of data they are based on, and the component of influenza …
| Evolutionary indicator | Influenza gene | Data type | Fitness category | Citations |
|---|---|---|---|---|
| HI log2 titer distance from the prior season | HA | Hemagglutination inhibition measurements using ferret sera | Antigenic drift | Huddleston et al., 2020; Neher et al., 2016 |
| Epitope distance from the prior season | HA and NA | Sequences | Antigenic drift | Bhatt et al., 2011; Bush et al., 1999; Krammer, 2023; Webster and Laver, 1980; Wiley et al., 1981; Wilson and Cox, 1990; Wolf et al., 2010 |
| Receptor binding site distance from the prior season | HA | Sequences | Antigenic drift | Koel et al., 2013 |
| Mutational load (non-epitope distance from the prior season) | HA and NA | Sequences | Functional constraint | Luksza and Lässig, 2014 |
| Stalk ‘footprint‘ distance from the prior season | HA | Sequences | Negative control | Kirkpatrick et al., 2018 |
| Local branching index | HA and NA | Sequences | Rate of recent phylogenetic branching | Huddleston et al., 2020; Neher et al., 2014 |
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Table format is adapted from Huddleston et al., 2020.
Table 2
Seasonal metrics of A(H3N2) epidemic dynamics.
Epidemic metrics are defined and labeled by which outcome category they represent.
| Epidemic Outcome | Definition | Outcome category | Citations |
|---|---|---|---|
| Epidemic size | Cumulative weekly incidence | Burden | |
| Peak incidence | Maximum weekly incidence | Burden | |
| Maximum time-varying effective reproduction number, | The number of secondary cases arising from a symptomatic index case, assuming conditions remain the same | Transmissibility | Scott et al., 2021; Bhatt et al., 2023 |
| Epidemic intensity | Inverse Shannon entropy of the weekly incidence distribution (i.e. the spread of incidence across the season) | Sharpness of the epidemic curve | Dalziel et al., 2018 |
| Subtype dominance | The proportion of influenza positive samples typed as A(H3N2) | Viral activity | |
| Excess pneumonia and influenza mortality attributable to A(H3N2) virus | Mortality burden in excess of a seasonally adjusted baseline | Severity | Hansen et al., 2022; Simonsen and Viboud, 2012 |
| Onset week | Winter changepoint in incidence | Timing | Charu et al., 2017 |
| Peak week | First week of maximum incidence | Timing | |
| Spatiotemporal synchrony | Regional variation (s.d.) in onset or peak timing | Speed | Viboud et al., 2006 |
| Onset to peak | Number of days between onset week and peak week | Speed | |
| Seasonal duration | Number of weeks with non-zero incidence | Speed |
Table 3
Comparison of influenza A(H3N2) epidemic timing between A(H3N2) and A(H1N1) dominant seasons.
We used two-sided Wilcoxon rank-sum tests to compare the distributions of epidemic timing metrics between A(H3N2) and A(H1N1) dominant seasons. We categorized seasons as A(H3N2) or A(H1N1) dominant …
| A(H3N2) timing metric | DominantIAV subtype | Wilcoxon test | ||
|---|---|---|---|---|
| H3N2 | H1N1 | W | p-value | |
| Median onset week (from EW40) | 8 | 11 | 3590 | 2.95×10–7 |
| Median peak week (from EW40) | 17 | 20.5 | 5294.5 | 3.5×10–9 |
| Regional variation (s.d.) in onset timing | 9.6 | 16.3 | 4095 | 1.61×10–5 |
| Regional variation (s.d.) in peak timing | 12 | 22.6 | 6166 | 6.43×10–18 |
| Seasonal duration | 28 | 21.5 | 1977.5 | 6.25×10–6 |
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Abbreviations: IAV, influenza A virus; EW40, epidemic week 40 (the start of the influenza season); s.d., standard deviation.
Table 4
Predictors of seasonal A(H3N2) epidemic size, peak incidence, transmissibility, epidemic intensity, and subtype dominance.
Variables retained in the best fit model for each epidemic outcome were determined by BIC.
| Outcome | Best Minimal Model1 | R2 | Adj. R2 | RMSE |
|---|---|---|---|---|
| Epidemic Size | H3 epitope distance ( – 2) + H1 epidemic size + H3 epidemic size ( – 1) | 0.74 | 0.69 | 9.88 |
| Peak Incidence | H3 epitope distance ( – 2) + H1 epidemic size + Dominant IAV Subtype ( – 1) | 0.69 | 0.63 | 2.09 |
| Effective Rt | HI log2 titer distance ( – 2) + H1 epidemic size + N2 distance to vaccine strain | 0.69 | 0.63 | 0.11 |
| Epidemic Intensity | HI log2 titer distance ( – 2) + N2 distance to vaccine strain + vaccination coverage ( – 1) | 0.79 | 0.75 | 0.07 |
| Subtype Dominance | H3 epitope distance ( – 2) + N2 epitope distance ( – 1) + Dominant IAV Subtype ( – 1) | 0.56 | 0.48 | 0.2 |
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1Candidate models were limited to three independent variables and considered all combinations of the top 10 ranked predictors from conditional inference random forest models (Figure 8).
Additional files
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Supplementary file 1
GISAID accessions and metadata for influenza H3 and N2 sequences, including originating labs and submitting labs.
- https://cdn.elifesciences.org/articles/91849/elife-91849-supp1-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/91849/elife-91849-mdarchecklist1-v1.pdf
