From Immune Response to Public Health: Estimating Influenza Vaccine Protection Across Scales

Proposal Defense Practice, IDIG

Savannah M. Hammerton

2025-03-13

Background

Influenza

Influenza

Enveloped negative-sense single-strand RNA virus
Segmented genome (8 segmenets)
HA facilitates viral entry
NA facilitates viral release

Influenza Classification

Types and Subtypes:
- A/H1N1
- A/H3N2
- A/H5N1
- B/Victoria
- ~~B/Yamagata~~
- C
- D

Strains (WHO 2024):
- A/Victoria/4897/2022 (H1N1)pdm09-like virus
- A/Thailand/8/2022 (H3N2)-like virus
- B/Austria/1359417/2021 (B/Victoria lineage)-like virus

Influenza Vaccines

Strains are chosen/recommended each year based on (CDC 2024b):

Which influenza viruses are making people sick prior to the upcoming flu season,
The extent to which those viruses are spreading prior to the upcoming flu season,
How well the previous season’s vaccines may protect against those influenza viruses, and
The ability of vaccine viruses to provide cross-protection against a range of related influenza viruses of the same type or subtype/lineage.

Influenza Vaccines

Influenza Vaccines

Influenza Vaccines

Influenza Vaccines

Influenza Vaccines

Problem/Significance

Why is VE so low? Many different theories (probably a combination):
- Original antigenic sin/imprinting
- Low immunogenicity
- Match/Mismatch between vaccine strain and circulating strain

Specific Aims

Identify and model correlates of protection against influenza in animal models.
Evaluate benefit of high-dose influenza vaccination by estimating and comparing Fluzone vaccine efficacies in age- and dose-based groups.
Quantify the impact of antigenic distance between vaccine and circulating influenza strains on vaccine effectiveness.

Aim 1. Identify and model correlates of protection against influenza in animal models.

Correlates of Protection (CoPs)

Biomarker associated with protection against infection or disease
Related terms: surrogate, mechanistic/nonmechanistic correlate, mediator of protection
Nomenclature often depends on the believed causality of the marker
Often used in vaccine design, development, and licensure

Influenza CoPs in Humans

Problem/Significance

Lack of research in correlates of protection in animal models
Vaccine studies in animals base endpoints on human 50%PT
Lack of evidence supporting that those 50%PTs are the same
Identifying true 50%PTs/CoP relationships in e.g. mouse, ferret studies can streamline vaccine development

[Planned] Data Description

Proposed Study

Analyze mouse and ferret (two most common influenza animal models) data separately
Bayesian mixed-effects models

Outcomes to assess:
- PCR confirmed infection
- Weight loss/anorexia
- Time to illness
- Viral load
Account for:
- Immunity source
- Inoculum dose
- Study site

Proposed Study

Analyze mouse and ferret (two most common influenza animal models) data separately
Bayesian mixed-effects models

Outcomes to assess:
- PCR confirmed infection
- Weight loss/anorexia
- Time to illness
- Viral load
Account for:
- Immunity source
- Inoculum dose
- Study site

Aim 2. Evaluate benefit of high-dose influenza vaccination by estimating and comparing Fluzone vaccine efficacies in age- and dose-based groups.

Immunogenicity Studies

Efficacy studies time and resource intensive
Immunogenicity studies typically more sustainable and done more regularly/for longer periods
Still useful to bring results back to actual protection

Problem/Significance

High-dose (HD) vaccines have evidence of reducing influenza morbidity and mortality in older adults
Because efficacy studies are so resource intensive, there has not been an extended view of how much the HD vaccine helps older adults in terms of actual protection
We can use immunogenicity studies to estimate vaccine efficacy and compare protection in different groups

Data Description

UGAFluVac
We’ve talked about this several times in this group, so let’s move on

Study Methods

Study

Study

Study

Aim 3. Quantify the impact of antigenic distance between vaccine and circulating influenza strains on vaccine effectiveness.

Antigenic Match and Mismatch

Is the chosen vaccine strain “the same” as the strain(s) infecting people?
How much does this matter?

Previous Literature

Tricco match/mismatch:

Influenza A strains from infected trial participants were matched with the strain in the vaccine if they belonged to the same A subtype (that is, H1N1 or H3N2) and were antigenically similar in the HI assay (that is, if they showed sufficient crossreaction in a HI chessboard table using ferret antisera; for example, with a HI typing quotient <fourfold titer). Influenza A viral strains were considered mismatched by antigenic drift if they were antigenically distinct from influenza A strains contained in the vaccine as per HI typing (for example, HI titer quotient ≥fourfold) or the characterization did not belong to a similar influenza A subtype contained in the vaccine (for example, H1N1 strains circulating but only H3N2 strains contained in the vaccine for bivalent vaccines with one H subtype).

Tenforde paper:

Among B/Victoria viruses, 926 (98%) of 945 sequenced viruses belonged to clade V1A.3, which contains a 3-amino acid deletion (162–164) in the HA protein and differs antigenically from the vaccine component (V1A.1), which has a 2-amino acid deletion (162–163) in the HA protein.

Skowronski paper:

To understand low VE despite reports of vaccine match, we conducted further epidemiologic and laboratory investigations in end-of-season analyses. With additional participants and contributing viruses, we estimated VE against circulating strains belonging to both influenza A subtypes and B lineages accompanied by their in-depth genotypic and phenotypic characterization in relation to vaccine components. Specifically, vaccine-virus relatedness was assessed genotypically by determining the amino acid (AA) sequence of established haemagglutinin (HA) antigenic sites and phenotypically through the haemagglutination inhibition (HI) assay. For H3N2, virus characterization was in relation to the A/Victoria/361/2011 prototype strain recommended by the WHO [10], as well as the egg-adapted high growth reassortant strain as per that actually used by manufacturers in vaccine production (hereafter ‘‘IVR-165’’) [11]. We show that suboptimal VE for the H3N2 component during the 2012–13 season was related to mutations in the egg-adapted IVR-165 vaccine strain, rather than antigenic drift in circulating viruses.

Belongia paper:

A virus was considered antigenically different from the corresponding vaccine vaccine strain if there was a >= 4 fold difference between the homologous HAI titer of the vaccine strain.

Previous Literature

Problem/Significance

Match/mismatch has been dichotomous and arbitrarily defined in many papers
Some studies have used antigenic distance over one or two seasons, or one specific flu type/subtype
Understanding of the impact of antigenic distance on vaccine effectiveness is still weak

[Planned] Data Description

Season	Location	Population	SubType	Proportiona	VaxStrain	Repeatb	CircStrain	AgDist1c	AgDist2c	AgDist3c	Nd	VEe
xx	xx	xx	xx	xx	xx	xx	xx	xx	xx	xx	xx	xx
aProportion of infections caused by the listed circulating strain.
bNumber of uninterrupted years this strain has been used in the vaccine, including the referenced season.
cAntigenic distance methods will be specified in these column names when final methods are decided.
dStudy sample size.
eVE with std. error and uncertainty intervals, as available.

Gather vaccine strain history from CDC and WHO recommendations
Find circulating strains from MMWRs, papers, reports, etc. for specific times and areas
Calculate antigenic distance multiple ways, including p-epitope, temporal, and Grantham

Analysis Plan

Meta-analysis of papers/reports on vaccine effectiveness (VE) over the past 2+ decades
Bayesian mixed-effects modeling predicting VE based on anitgenic distance
Account for age group, region, “repeat vaccination effect”

Analysis Plan

Primary modeling result: what is the association between VE and antigenic distance?

Timeline goes here :)

Acknowledgements

Committee

Handelgroup
IDIG
Everyone who’s given me moral, emotional, and/or methodological support

References

CDC. 2024a. “Types of Influenza Viruses.” Influenza (Flu). September 27, 2024. https://www.cdc.gov/flu/about/viruses-types.html.

———. 2024b. “Selecting Viruses for the Seasonal Influenza Vaccine.” Influenza (Flu). September 30, 2024. https://www.cdc.gov/flu/vaccine-process/vaccine-selection.html.

———. 2024c. “CDC Seasonal Flu Vaccine Effectiveness Studies.” Flu Vaccines Work. October 3, 2024. https://www.cdc.gov/flu-vaccines-work/php/effectiveness-studies/index.html.

———. 2025. “Preliminary Estimated Flu Disease Burden 2024-2025 Flu Season.” Flu Burden. February 13, 2025. https://www.cdc.gov/flu-burden/php/data-vis/2024-2025.html.

Coudeville, Laurent, Fabrice Bailleux, Benjamin Riche, Françoise Megas, Philippe Andre, and René Ecochard. 2010. “Relationship Between Haemagglutination-Inhibiting Antibody Titres and Clinical Protection Against Influenza: Development and Application of a Bayesian Random-Effects Model.” BMC Med Res Methodol 10: 18. https://doi.org/10.1186/1471-2288-10-18.

Cowling, Benjamin J, Wey Wen Lim, Ranawaka A P M Perera, Vicky J Fang, Gabriel M Leung, J S Malik Peiris, and Eric J Tchetgen Tchetgen. 2019. “Influenza Hemagglutination-inhibition Antibody Titer as a Mediator of Vaccine-induced Protection for Influenza B.” Clinical Infectious Diseases 68 (10): 1713–17. https://doi.org/10.1093/cid/ciy759.

Dudášová, Julie, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fišer, Justina Ivanauskaite, Frank Liu, and Jeffrey R. Sachs. 2021. “A Method to Estimate Probability of Disease and Vaccine Efficacy from Clinical Trial Immunogenicity Data.” Npj Vaccines 6 (1): 133. https://doi.org/10.1038/s41541-021-00377-6.

Hammerton, Savannah M, W Zane Billings, Hayley Hemme, Ted M Ross, Ye Shen, and Andreas Handel. 2024. “Estimating Standard-Dose and High-Dose Fluzone Vaccine Efficacies for Influenza A Based on Hemagglutination Inhibition Titers.” The Journal of Infectious Diseases, December, jiae615. https://doi.org/10.1093/infdis/jiae615.

Krammer, Florian, Gavin J. D. Smith, Ron A. M. Fouchier, Malik Peiris, Katherine Kedzierska, Peter C. Doherty, Peter Palese, et al. 2018. “Influenza.” Nat Rev Dis Primers 4 (1): 3. https://doi.org/10.1038/s41572-018-0002-y.

Lim, Wey Wen, Nancy H L Leung, Sheena G Sullivan, Eric J Tchetgen Tchetgen, and Benjamin J Cowling. 2020. “Distinguishing Causation from Correlation in the Use of Correlates of Protection to Evaluate and Develop Influenza Vaccines.” Am J Epidemiol 189 (3): 185–92. https://doi.org/10.1093/aje/kwz227.

Pan, Yidan, and Michael W. Deem. 2016. “Prediction of Influenza B Vaccine Effectiveness from Sequence Data.” Vaccine 34 (38): 4610–17. https://doi.org/10.1016/j.vaccine.2016.07.015.

WHO. 2024. “Recommended Composition of Influenza Virus Vaccines for Use in the 2024-2025 Northern Hemisphere Influenza Season.” 2024. https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2024-2025-northern-hemisphere-influenza-season.