Does Repeat Influenza Vaccination Constrain Influenza Immune Responses and Protection
Conditions: Influenza, Human, SARS-CoV-2 Infection
Sponsor: University of Melbourne

DOES REPEATED INFLUENZA 
VACCINATION CONSTRAIN INFLUENZA 
IMMUNE RESPONSES AND PROTECTION?
PROTOCOL
4 March 2021
Version 4.3
MH project number 2019.176
HREC reference: HREC/54245/MH-2019
Authors:
Sheena Sullivan, Annette Fox, Adam Kucharski, Vivian Leung
Sponsor: Melbourne Health
Funding: US National Institutes of Health (Award:1R01AI141534)
CONFIDENTIAL
This document is confidential and the property of WHO Collaborating Centre for 
Reference and Research on Influenza. No part of it may be transmitted, reproduced, 
published, or used without prior written authorization from the institution.
Statement of Compliance
This study will be conducted in compliance with all stipulation of this protocol, the 
conditions of the ethics committee approval, the NHMRC National Statement on ethical 
Conduct in Human Research (2007) and the Note for Guidance on Good Clinical Practice 
(CPMP/ICH-135/95).

1
TABLE OF CONTENTS
1
Table of Contents..................................................................................................................................................... i
2
Glossary of Abbreviations and Terms...........................................................................................................iv
3
Executive Summary............................................................................................................................................... v
4
Project Team........................................................................................................................................................... vi
4.1
Steering Committee...................................................................................................................viii
5
Study Sites................................................................................................................................................................ ix
6
Background............................................................................................................................................................... 1
6.1
Preliminary results and related prior work........................................................................3
7
Significance of the Proposed Research..........................................................................................................6
8
Aims and Objectives.............................................................................................................................................. 6
8.1
Aim 1: To study how the immunogenicity and effectiveness of influenza 
vaccination is influenced by prior vaccination experience........................................................................7
8.1.1
Objectives:.............................................................................................................................................. 7
8.1.2
Outcomes................................................................................................................................................ 7
8.2
Aim 2: To characterize immunological profiles following infection and 
vaccination
............................................................................................................................................................... 7
8.2.1
Objectives:.............................................................................................................................................. 7
8.2.2
Outcomes................................................................................................................................................ 7
8.3
Aim 3: To evaluate the impact of immunological profiles on vaccination 
effectiveness ............................................................................................................................................................... 8
8.3.1
Objectives:.............................................................................................................................................. 8
8.3.2
Outcomes................................................................................................................................................ 8
9
Study Design............................................................................................................................................................. 8
9.1
Overview of Study Design...........................................................................................................9
9.2
Study Schedule..............................................................................................................................11
10
Study Population............................................................................................................................................. 13
10.1
Eligibility......................................................................................................................................... 13
10.1.1
Inclusion criteria...............................................................................................................................13
10.1.2
Exclusion criteria..............................................................................................................................13
10.2
Cohort Recruitment....................................................................................................................13
10.2.1
Recruitment strategies...................................................................................................................13
10.2.2
Retention strategies........................................................................................................................14
10.2.3
Follow-up............................................................................................................................................. 15
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10.2.4
Informed consent.............................................................................................................................15
10.3
Data Collection.............................................................................................................................. 16
10.3.1
Overview of data collection..........................................................................................................16
10.3.2
Baseline questionnaire...................................................................................................................17
10.3.3
Social contacts questionnaires....................................................................................................17
10.3.4
Influenza vaccination documentation......................................................................................18
10.3.5
Blood collection.................................................................................................................................18
10.3.6
Active surveillance for acute illness..........................................................................................19
11
Laboratory Procedures................................................................................................................................. 20
11.1
Laboratory procedures relevant to Aim 1.........................................................................20
11.1.1
Sero-response to vaccination......................................................................................................20
11.1.2
Influenza testing and virus characterization.........................................................................20
11.1.3
Reporting of influenza RT-PCR results....................................................................................20
11.2
Laboratory procedures relevant to Aim 2.........................................................................21
11.2.1
Antibody landscapes.......................................................................................................................21
11.2.2
HA-reactive B cell response.........................................................................................................21
11.2.3
Antibody focussing..........................................................................................................................22
12
Data considerations....................................................................................................................................... 22
12.1
Sample Size....................................................................................................................................22
12.1.1
Primary objective.............................................................................................................................23
12.1.2
Secondary objectives......................................................................................................................23
12.2
Data Analysis.................................................................................................................................23
12.3
Data cleaning................................................................................................................................. 23
12.4
Missing data................................................................................................................................... 23
12.5
Analyses relevant to Aim 1......................................................................................................24
12.5.1
Baseline comparisons.....................................................................................................................24
12.5.2
Serological endpoints.....................................................................................................................24
12.5.3
Influenza attack rates and VE......................................................................................................24
12.5.4
Duration of illness............................................................................................................................24
12.6
Analyses relevant to Aims 2 and 3........................................................................................25
12.6.1
Interpreting the landscapes.........................................................................................................25
12.6.2
Using dynamic antibody landscapes to predict infection risks......................................27
12.6.3
Evaluating current influenza vaccine effectiveness............................................................27
12.6.4
Evaluating alternative influenza vaccine scenarios............................................................27
13
Storage of biospecimens.............................................................................................................................. 27
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13.1
Serum samples storage and shipping..................................................................................27
13.2
Peripheral Blood Mononucleocytes (PBMCs) storage and shipping.......................28
13.3
Respiratory swabs.......................................................................................................................28
13.4
Consent to biobanking of specimens...................................................................................28
14
Data Entry and Management......................................................................................................................28
14.1
Data security..................................................................................................................................28
14.1.1
Study database..................................................................................................................................28
14.1.2
Data entry............................................................................................................................................ 29
14.2
Data storage...................................................................................................................................29
14.3
Participant identification and confidentiality..................................................................29
15
Participant Safety and Withdrawal..........................................................................................................29
15.1
Risks to participants...................................................................................................................29
15.2
Risk management and safety..................................................................................................30
15.2.1
Definitions........................................................................................................................................... 30
15.2.2
Eliciting adverse event information..........................................................................................30
15.2.3
Assessment and documentation of adverse events............................................................30
15.2.4
Serious adverse event reporting................................................................................................31
15.3
Participant withdrawals...........................................................................................................31
15.3.1
Follow up of withdrawn participants.......................................................................................31
15.4
Replacements................................................................................................................................ 31
15.5
Incidental findings.......................................................................................................................31
15.6
Vulnerable subjects.....................................................................................................................31
15.7
Potential benefits of the proposed research to participants and others...............32
16
Resource Sharing Plan.................................................................................................................................. 32
16.1
Data Sharing Plan........................................................................................................................32
16.2
Genomic Data Sharing................................................................................................................33
17
References......................................................................................................................................................... 34
18
Appendices........................................................................................................................................................ 37
Appendix A
Screening form..............................................................................................................................37
Appendix B
Advertising materials.................................................................................................................38
Appendix C
Baseline questionnaire..............................................................................................................39
Appendix D
Weekly symptom diary.............................................................................................................40
Appendix E
Instructions for collection of respiratory swabs.............................................................41
Appendix F
Template for antibody results for participants...............................................................42
Appendix G
Participant cards..........................................................................................................................43
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2
GLOSSARY OF ABBREVIATIONS AND TERMS
Abbreviation
Description (using lay language)
ARI
Acute respiratory illness. Defined as one or more of the 
following symptoms include fever ≥37.8°C, headache, body 
aches, cough, sore throat, runny nose, sputum.
FRA
Focus reduction assay. A laboratory assay that measures the 
ability of antibodies to neutralize virus infectivity.
HI assay
Haemagglutination inhibition assay. Laboratory test which 
measures anti-haemagglutinin antibodies. These antibodies 
inhibit attachment of the influenza virus to target cell 
membrane receptors on red blood cells.
HCW
Health care worker. Any personnel eligible for the free 
vaccination programme run at participating hospitals or 
health services.  Personnel may include staff, including 
administrative, research, clinical and support services, 
employed at the participating hospital. It may also include 
volunteers, students or honorary staff. 
GMT
Geometric mean titre. Arithmetic mean of the logarithms 
(base 2) of the last positive dilution of each serum.
MN assay
Microneutralization assay. A laboratory test which measures 
the ability of antibodies to neutralize virus infectivity.
PBMC
Peripheral blood mononuclear cell. A type of white blood cell 
that contains a single lobed nucleus.
RT-PCR
Real-time reverse transcriptase polymerase chain reaction. A 
laboratory test used to make many copies of a specific 
genetic sequence for analysis and can be used to diagnose 
disease.
Seropositive or seropositivity
Antibody titre of ≥40, as measured using a 
haemagglutination inhibition assay.
Sero-conversion
4-fold rise in antibody titre, as measured using a 
haemagglutination inhibition assay.
VE
Vaccine effectiveness. A measure of real-world benefit to 
patients for whom vaccine is recommended.
WHOCCRRI
World Health Organization Collaborating Centre for 
Reference and Research on Influenza.
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3
EXECUTIVE SUMMARY
Hospitals in a number of countries have introduced annual staff influenza vaccination policies, some 
with a mandatory requirement. This strategy aims to protect both staff and patients from infection and 
incurs a considerable cost. Staff may be vaccinated for 10 or more consecutive years. However, some 
evidence suggests that the antibody response to influenza vaccination subsides with repeated 
vaccination. This finding is corroborated by epidemiological studies which have indicated that the 
vaccine’s effectiveness (VE) decreases with repeated administration. The possibility of suboptimal 
protection with repeated vaccination presents a compelling need to evaluate healthcare worker (HCW) 
influenza vaccination policies. HCWs are an important group in which to study these effects because 
they differ from most other vaccine target groups in being healthy adults, the group for whom we 
expect the vaccine should work best.
Our overall goal is to understand the mechanisms underlying observations of reduced immunogenicity 
and VE among multiply vaccinated persons in the context of their adaptive immune responses to 
vaccination and how this impacts expected gains from vaccination programs. To achieve this goal, we 
will recruit a cohort of HCWs working in 6 Australian hospitals. HCWs will represent a range of 
vaccination experience from unvaccinated to frequently vaccinated. We will follow HCWs for 4 years, 
document confirmed influenza cases, calculate influenza attack rates, and assess correlates of 
protection. We will conduct detailed immunological assays among subgroups to evaluate the role of 
prior exposures and memory B cell responses to vaccination. We will use mathematical modelling to 
interpret dynamic antibody responses and produce models of the effectiveness of HCW vaccination 
programs. The rationale for the proposed research is that increasing our understanding of the 
immunological consequences of repeated vaccination will improve the evidence base for decision 
making about vaccination policy. 
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4
PROJECT TEAM
Table 1 lists each team member, their role in the project and responsibilities. 
Table 1. Roles and responsibilities
Name
Role
Responsibilities
A/Prof Sheena Sullivan
Project director
Principal investigator (Aim 1)
Epidemiologist
Contact PI responsible for the study. Lead Aim 1 and oversee participant recruitment at the 6 
recruitment sites. Supervise the post-doctoral statistician and oversee analyses for Aim 1. 
Lead publications relevant to Aim 1 and contribute to the interpretation and write of results for 
Aims 2 and 3.
Dr Annette Fox
Principal investigator (Aim 2)
Immunologist
Lead Aim 2, supervise the post-doctoral immunologist, oversee all immunological assays and 
lead the interpretation of all laboratory data. Lead any publications resulting from work done 
for Aim 2. Contribute to the interpretation and write-up of results for the other Aims.
A/Prof Adam Kucharski
Principal investigator (Aim 3)
Mathematical Biologist
Lead Aim 3, contribute to activities relevant to Aims 2-3 and oversee the research fellow 
based at LSHTM. Contribute to the interpretation and write-up of results for the project, 
especially Aim 3.
Prof Kanta Subbarao
Co-investigator
Virologist
Provide expertise and advice on immunological assays; contribute to interpretation of results 
and development of manuscripts.
Prof Helen Marshall
Co-investigator
PI for Adelaide site
Vaccinologist 
Contribute to activities relevant to Aim 1 and oversee participant recruitment at the Women’s 
and Children’s Hospital, Adelaide.
A/Prof Julia Clark
Co-investigator
PI for Brisbane site
Infectious Diseases Paediatrician
Contribute to activities relevant to Aim 1 and oversee participant recruitment at Queensland 
Children’s Hospital.
Prof Allen Cheng
Co-investigator
PI for Melbourne site
Infectious Diseases 
Physician/Epidemiologist
Contribute to activities relevant to Aim 1 and oversee participant recruitment at the Alfred 
Hospital. 
Prof Peter Wark
Co-investigator
PI for Newcastle site
Consultant Respiratory Physician
Contribute to activities relevant to Aim 1 and oversee participant recruitment at John Hunter 
Hospital. 
A/Prof Christopher Blyth
Co-investigator
PI for Perth site
Infectious Diseases Paediatrician
Contribute to activities relevant to Aim 1 and oversee participant recruitment at Perth 
Children’s Hospital.
Prof Kristine Macartney
Co-investigator
PI for Sydney site
Paediatrician / Vaccinologist
Contribute to activities relevant to Aim 1 and oversee participant recruitment the Children’s 
Hospital at Westmead. 
Vivian Leung
Project manager
Liaise with site managers, governance procedures, perform data management and routine 
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Epidemiologist
analysis and report writing. 
Louise Carolan
Research assistant
Immunologist
Assist with laboratory assays for Aims 1 and 2.
Leslie Dowson
Project assistant
Provide assistance to the project manager, particularly with regards to reporting 
requirements.
TBN
Postgraduate student (PhD)
Work on the immunological assays proposed in Aim 2. Attend meetings and contribute to the 
interpretation and write up of results generated for Aims 1 and 2. The student will be 
supervised by PI Fox and PI Sullivan and will commence in year 2 of the project.
Arseniy Khvorov
Statistician
Statistical analysis of data generated in Aim 1. Attend meetings and contribute to the 
interpretation and write-up of results for Aim 1 and may contribute to interpretation of Aims 2 
and 3.
Yeu-Yang Tseng
Post-doctoral fellow Immunologist
Perform immunological assays for Aims 1 and 2. Attend meetings and contribute to the 
interpretation and write-up of results for Aims 1 and 2.
TBN
Research Fellow
Mathematical Biologist
Lead the development and testing of new mathematical models and Bayesian inference 
algorithms during the early stages of the project
Kathryn Riley
Site manager, Adelaide site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
TBN
Nurse/phlebotomist, Adelaide site
Perform blood collection.
Natasha Doran
Site manager, Brisbane site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
TBN
Nurse/phlebotomist, Brisbane site
Perform blood collection.
Michelle Hagenauer
Site manager, Melbourne site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
TBN
Nurse/phlebotomist, Melbourne 
site
Perform blood collection.
Catherine Delahunty
Site manager, Newcastle site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
TBN
Nurse/phlebotomist, Newcastle 
site
Perform blood collection.
Marion Macnish
Site manager, Perth site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
TBN
Nurse/phlebotomist, Perth site
Perform blood collection.
Archana Koirala
Site manager, Sydney site
Manage governance procedures, develop site-specific protocols, coordinate all recruitment 
activities and data collection; manage sample collection and transport.
Rosemary Joyce
Nurse/phlebotomist, Sydney site
Perform blood collection.
Dr Craig Dalton
Consultant
Contribute to activities relevant to Aim 1 and provide data management support for data 
collected from the symptoms diaries.
TBN: to be named 
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4.1
Steering Committee 
Table 2 lists the members of the steering committee for the project, their current position and 
qualifications. The steering committee will be engaged by the project team to provide expertise and 
advice on the analysis strategies and interpretation of results. 
Table 2. Steering committee
Name
Position
Qualifications
A/Prof Sheena Sullivan
Senior epidemiologist, WHO Collaborating 
Centre for Reference and Research on 
Influenza at the Doherty Institute 
BSc(Hons), MPH, 
PhD
Dr Annette Fox
Senior scientist, WHO Collaborating 
Centre for Reference and Research on 
Influenza at the Doherty Institute
BSc(Hons), PhD, 
Grad Dip Int Health
A/Prof Adam Kucharski
Assistant Professor and Sir Henry Dale 
Fellow, Department of Infectious Disease 
Epidemiology, London School of Hygiene 
and Tropical Medicine
MMath, PhD
Prof Kanta Subbarao
Director, WHO Collaborating Centre for 
Reference and Research on Influenza; 
Professor, Department of Microbiology 
and Immunology, University of Melbourne
MBBS, MPH
Prof Benjamin Cowling
Head of the Division of Epidemiology and 
Biostatistics, Hong Kong University; co-
director of the WHO Collaborating Centre 
for Infectious Disease Epidemiology and 
Control
BSc, PhD
A/Prof Scott Hensley
Associate Professor, Department of 
Microbiology, University of Pennsylvania
BA, PhD
Dr Mark Thompson
Epidemiologist, Influenza Division, US 
Centers for Disease Control and 
Prevention
PhD
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5
STUDY SITES
A list of the study sites and site contact details are provided in Table 3.
Table 3. Study sites and contact details
Site
Administering 
Institution
Recruitment site / 
Department
Address
Contact Person(s)
Phone
Email
Doherty
University of 
Melbourne
WHO Collaborating Centre 
for Reference and 
Research for Influenza
792 Elizabeth Street, 
Melbourne VIC 3000
A/Prof Sheena Sullivan
Dr Annette Fox
+613 9342 9317
+613 9342 9313
sheena.sullivan@influenza
centre.org
annette.fox@influenzacent
re.org 
London
London School of 
Hygiene and 
Tropical Medicine
Department of Infectious 
Disease Epidemiology
Keppel St, Bloomsbury, 
London WC1E 7HT, UK
A/Prof Adam Kucharski
+44 020 7958 8273
adam.kucharski@lshtm.ac
.uk 
Adelaide
University of 
Adelaide
Women’s and Children’s 
Hospital
72 King William Rd, North 
Adelaide SA 5006
Prof Helen Marshall
+618 8161 8115
helen.marshall@adelaide.
edu.au 
Brisbane
University of 
Queensland
Queensland Children’s 
Hospital
501 Stanley St, South 
Brisbane QLD 4101
A/Prof Julia Clark
+617 30681898
j.clark7@uq.edu.au 
Melbourne
The Alfred Hospital
The Alfred Hospital
55 Commercial Rd, 
Melbourne VIC 3004
Prof Allen Cheng
+613 9076 8941
allen.cheng@monash.edu 
Newcastle
University of 
Newcastle
John Hunter Hospital
Lookout Rd, New Lambton 
Heights NSW 2305
Prof Peter Wark
+613 4042 0110
peter.wark@newcastle.ed
u.au 
Perth
University of 
Western Australia
 
Perth Children’s Hospital
15 Hospital Ave, Nedlands 
WA 6009
A/Prof Christopher Blyth
+618 9340 7061
christopher.blyth@uwa.ed
u.au
PathWest (non-recruitment 
site)
Locked Bag 2009, Nedlands 
WA 6909
A/Prof Christopher Blyth
+618 9340 7061
christopher.blyth@uwa.ed
u.au
Sydney
Sydney Children’s 
Hospital Network 
The Children’s Hospital at 
Westmead
Cnr Hawkesbury Rd &, 
Hainsworth St, Westmead 
NSW 2145
Prof Kristine Macartney
+612 9845 1433
kristine.macartney@sydne
y.edu.au 
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6
BACKGROUND
Influenza vaccines require annual re-administration both because circulating strains, especially 
influenza A(H3N2) viruses, undergo rapid antigenic drift demanding re-configuration of the vaccine, 
and because vaccine-induced immunity against homologous strains may wane. Annual seasonal 
influenza vaccination is currently recommended for healthcare workers (HCW) to protect themselves 
against infection, reduce absenteeism and minimize the risk of transmission to patients. Under such 
policies, HCW may include anyone who works at a hospital, irrespective of patient contact, including 
administrative, research, clinical and support services staff. Many North American hospitals require 
HCW who decline vaccine to wear face masks throughout the influenza season (1), and they report 
vaccination uptake of over 90% in their hospitals (2).  Australian hospitals generally do not have such 
HCW vaccination policies and vaccine uptake is around 60%-70%.
The effectiveness of influenza vaccines is at best moderate. A 2012 meta-analysis estimated pooled 
efficacy to be around 59% (95% CI 51–67%) and effectiveness around 50% in healthy non-elderly 
adults (3) (the demographic in which HCW would largely fall). A more recent review observed a pooled 
VE estimate of only 33% (95%CI: 26–39) for A(H3N2)(4), and low and negative estimates have been 
reported (5-8). Thus, the expected protection from vaccination afforded to HCW is questionable. 
The effects of repeated vaccination are unclear and may reduce effectiveness (9). This was first noted 
during a vaccine trial in an English boarding school in the 1970s (9). A subsequent study in Texas 
observed poorer serologic responses in repeat vaccinees in 4/7 seasons (10), and a 1999 review found 
that roughly half of published serological studies reported poorer post-vaccination antibody titres 
among vaccine-experienced compared with vaccine-naïve vaccinees (11). Responses may be even 
poorer when revaccinated with the same formulation if the circulating virus has drifted (12). We have 
conducted 2 small studies to examine immunological responses to vaccination; one at the Peter 
MacCallum Cancer Centre (13) and one at Royal Melbourne Hospital (RMH) in 2016. Both of these 
studies showed attenuated antibody responses to vaccination among repeatedly vaccinated HCW, 
especially towards A(H3N2) antigens (Figure 1).
Interest in this phenomenon was reinvigorated when a 2013 household study reported VE=-45% 
(95%CI:-226-35) among people vaccinated two years in a row compared with people vaccinated only in 
the prior season (14). Consequently, many groups have begun routinely reporting effectiveness 
estimates by prior year’s vaccination status, often reporting reduced VE among multiply vaccinated 
patients (8, 15-20). However, no study has yet reported on both vaccine effectiveness and 
immunological responses to repeated influenza vaccination. 
An explanation for this phenomenon, including inconsistencies among studies, has been attempted by 
mathematical modelling (21). The antigenic distance hypothesis posits that when two vaccine strains 
(V1 and V2) are antigenically similar, responses to epitopes in V1 dominate such that repeat 
vaccination impairs VE if the circulating strain (C) has changed from V2, but enhances VE when C has 
not changed. In contrast, if V1 and V2 are antigenically distant, repeat vaccination has little effect 
because responses to V2 are not compromised. In Australia during the 2017 season, V1=V2 but the V2-C 
distance was great and VE for A(H3N2) among repeat vaccinees was poor (3%; 95%CI: -29 to 27)(19). 
Similar findings were observed in Canada in 2014/15 (16). These effects are not expected each year 
because of annual differences in V1-V2 and V2-C antigenic distance. However, on average, negative 
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interference is seen more often for A(H3N2) viruses compared with other influenza types/subtypes 
(22), probably because of the higher rate of antigenic drift in A(H3N2) viruses (23).
Figure 1. Summary antibody landscapes for 26 
HCW
By collating the results of many antibody assays to historical 
influenza strains, it is possible to visualize the landscape of 
an individual’s responses to vaccination and infection. The 
plot shows preliminary antibody landscapes for 26 HCW 
followed in 2016 at the Royal Melbourne Hospital. 31 
antigens representing antigenic clusters that circulated from 
1968-2016 were assessed (x-axis). Lines are estimated 
using a loess curve; full landscape analyses uses a 
nonlinear model (24).  These plots suggest: (1) Post-
vaccination and post-season HI titres against the vaccine 
strain (A/Hong Kong/4801/2014e; red arrows) were higher 
for the vaccine-naïve group compared with highly 
vaccinated or infected HCW; (2) Post-vaccination titres are 
higher and better-sustained post-season in the vaccine-
naïve compared with the highly vaccinated group; (3) Post-
vaccination titres against egg-grown antigens (grey-dashed 
lines) were generally higher than their cell-grown 
counterparts (shown to their right); (4) Post-infection titres 
were high and showed better response to historical cell-
grown than egg-grown antigens. 
Concepts regarding the underlying mechanisms at play have evolved over many years. First is the 
concept of original antigenic sin, which suggests that a person’s initial influenza infection affects 
responses to subsequent strains by preferentially orienting antibodies towards priming epitopes that 
remain in subsequent strains, often as subdominant epitopes (25). Second is the concept of antigenic 
seniority, which suggests that prior infections have cumulative negative effects on responses to later 
strains, resulting in antibody titres that are higher to more ‘senior’ strains encountered earlier in life. As 
with original antigenic sin, it is suggested that immune boosting and interference may account for 
antigenic seniority, with successive influenza exposures boosting antibody responses to more senior 
strains that dominate over responses to new epitopes on the later strain. A similar concept, termed 
back-boosting, was conceived from studies that developed antibody landscapes to depict how infection 
and vaccination affect titres to prevailing and past strains in the context of antigenic distance (see 
Figure 1). Both infection and vaccination induce broad back-boosting of pre-exposure antibody 
landscapes, suggesting that memory responses are invoked (24, 26). Importantly, effects of vaccination 
are associated with antigenic distance, with better responses to an antigenically distinct and more 
advanced vaccine, suggesting that antigenic distance may be an important determinant of a vaccine’s 
ability to escape interference from prior immunity. Several groups have used molecular approaches to 
demonstrate that antibodies can indeed become preferentially focused on an epitope that is conserved 
among successively encountered strains (27, 28). Some evidence indicates that memory B cells drive 
this focused antibody response (28). These earlier studies focused on A(H1N1)pdm09-reactive 
antibodies so it is important to establish whether antibody focusing also occurs upon successive 
exposure to A(H3N2) viruses, in which case the range of conserved epitopes may be greater and more 
complex depending upon the range of strains an individual has encountered. While antibody focusing 
may not necessarily reduce vaccine titres or effectiveness, it could create a future opportunity cost if the 
conserved epitopes are subsequently altered in circulating strains (29). Thus, the occurrence and 
consequences of antibody focusing may be linked to antigenic drift and the antigenic distance 
hypothesis, in that a series of similar vaccines containing a shared epitope may promote antibody 
focusing that would provide little protection if the circulating strain drifts. 
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6.1
Preliminary results and related prior work 
Effects of prior vaccination among HCW: PI-Sullivan has run 2 serosurveys in Australian hospitals. In 
the first, 202 HCW were recruited during the 2015 influenza vaccination campaign, among whom post-
season follow-up was 90% (n=183). In 2016, 190 HCW were enrolled, with 157 (83%) included in the 
final analyses. Haemagglutination inhibition (HI) assays were used to compare post-vaccination serum 
antibody responses between frequent and infrequent vaccinees. Figure 1 shows the HI antibody 
landscape for a selection of participants. A blunted response was observed among frequent vaccines 
(panel B in in Figure 1), with lower post-vaccination geometric mean titres (GMT), lower rises in GMT 
and lower levels of seroprotection compared with infrequent vaccines (panel A in in Figure 1), both 
post-vaccination and post-season. In both groups, post-vaccination antibody responses to cell-grown 
antigens were weaker than to egg-grown antigens (grey-dashed lines in Figure 1), and responses were 
especially strong against the vaccine strain (red arrow). In contrast, infected HCW showed stronger 
responses to cell-grown antigens, suggesting that vaccination focuses the response on egg-grown 
antigens while infection induces broad antibody production against circulating viruses.
Some limitations of these studies warrant mention. First, our sample was too small for extensive 
subgroup analyses; thus we are proposing to recruit a much larger cohort to identify subgroups for Aim 
2. Second, few vaccine-naïve HCW were recruited because of high vaccination uptake among staff 
(>80%); thus, we have identified hospitals with lower vaccine uptake and will do purposive sampling of 
vaccine-naïve HCW. Third, the severity of influenza seasons is unpredictable. Our first study was 
conducted during a moderate-to-severe influenza season(30) and serological evidence of infection (4-
fold increases in HI titres post-season) was evident in 25 HCW.(13) Our second study was conducted 
during a mild season. We actively followed and tested HCW, of which 65 reported an ARI, 6 tested 
positive, all for A(H3N2), but only 2/6 reported fever and only 1/5 showed further 4-fold rises post-
season. To overcome seasonal variations, we are proposing to follow HCW for 4 years to capture a 
range of severity, and improve the possibility of comparing responses to the same and different vaccine 
formulations.(12) We will use a sensitive ARI definition that does not require fever, because we and 
others have shown febrile ARI may miss 50% of influenza-positive cases.(31) Although the role of mild 
infections in transmission remains unclear,(32, 33) they may be important in hospital settings.
Effect of prior infection on post-vaccination antibodies: PI-Fox has followed an established cohort 
of ~1000 individuals in Ha Nam, Vietnam for 10+ years. 100 adults from this cohort, whose 10-years-
prior infection status was known, received influenza vaccine for the first time in 2016. Pre-vaccination 
GMTs against the 2016 A(H3N2) vaccine component, A/Hong Kong/4801/2014, were higher if subjects 
had documented prior A(H3N2) infection (Error: Reference source not found), indicating that prior 
infections induced antibodies against epitopes that were retained in the vaccine strain. Post vaccination 
GMT and seroconversion rates were also 
higher amongst the 72 Ha Nam vaccinees 
who had documented prior A(H3N2) 
infection compared to 28 lacking recent 
prior A(H3N2) infection. HI titre rises 
were positively associated with multiple 
and more recent infection (Error: 
Reference source not found). Preservation 
of antigenic sites should be greatest 
between recently circulating strains and 
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3
Figure 2. Effect of prior 
A(H3N2) infection on pre- 
and post-vaccine HI titres 
against 
A/Hong-Kong/4801/2014. 
Symbols represent individual log2 
HI titres for 72 vaccinees with 
documented prior A(H3N2) 
infection and 28 without. P values 
are shown for the comparison of 
pre and post-vaccine GMTs in 
these groups. Red bars show 
GMT + 95% CI.  The dashed 
horizontal line represents the 
seropositivity threshold (HI≥40). 

the A(H3N2) vaccine strain. Therefore, higher vaccine HI titres in people with more recent prior 
infection are likely due to boosting of responses to preserved epitopes. An A(H3N2) epidemic 
commenced 9 months after vaccination, when A(H3N2) illness was detected in 4/28 vaccinees without 
recent prior infection versus 0/72 with recent prior infection (p = 0.006). A(H3N2) illness was also 
more common amongst vaccinees who did not seroconvert (3/17) compared to seroconverters (1/83, p 
= 0.013). Thus, recent prior A(H3N2) infection was associated with higher HI titres and seroconversion, 
and both prior infection and seroconversion were associated with protection after influenza 
vaccination.
It is notable that recent prior infection in 
the Ha Nam cohort was associated with 
enhanced vaccine antibody responses yet 
prior vaccination in the HCW cohort was 
associated with attenuated responses. We 
hypothesize that infection generates 
responses across a greater breadth of 
epitopes and antigenic sites compared to 
vaccination, such that even though 
baseline titres against that vaccine strain 
were relatively low in Ha Nam participants (Error: Reference source not found) compared to HCW 
(Figure 1) boosting across multiple epitopes could result in greater titre rises. Antibody focusing 
(described above (27)(28)) may occur because memory B cells and/or antibody monopolize antigen, 
interfering with the development of responses to altered epitopes in drifted strains. We propose that 
memory responses are more likely to monopolize antigen following vaccination, when antigen is 
limited, than following infection, and therefore vaccination is more likely to promote antibody focusing. 
Modeling influenza infection and immunity: Understanding these complex dynamics, across a range 
of antigens and vaccination histories, is difficult to parameterize using standard statistical methods. 
Thus, PI-Kucharski has developed several mathematical modeling tools to quantify the processes that 
shape influenza serological dynamics. It has been challenging to estimate influenza infection and 
vaccination history from observed HI titres because observed titres against specific influenza strains 
are the result of three main processes: (1) prior infection and vaccination history of that participant; (2) 
cross-reactive antibody responses against antigenically similar strains, which may vary over time; and 
(3) measurement variability in the assay itself. This leads to several sources of uncertainty; variation in 
the shape of antibody landscapes between two different participants may be the result of different 
exposure histories, differences in antibody responses, variability in the titre measurement, or a 
combination of all three.
Using a Bayesian modelling approach, it is possible to jointly estimate individual-level infection 
histories, temporal dynamics of antibody responses, and assay variability using serological data (Figure 
4). This approach works because certain features of the antibody response, such as boosting following 
infection, and subsequent waning, exhibit a degree of consistency between participants with identical 
exposure histories.(34) Across a study population, it is therefore possible to estimate how much of the 
variation in titres is explained by differences in individual exposure histories, dynamic antibody 
responses or assay variability. Recent work has shown that although cross-sectional serological data 
cannot reliably estimate the shape and magnitude of short- and long-term dynamic antibody responses, 
with longitudinal samples it is possible to distinguish a short-term antibody response that wanes from a 
longer-term persistent response.(35) There can be a substantial rise in observed titre post-infection or 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
4
Figure 3. Effect of prior 
A(H3N2) exposure level on 
HI titre increase after 
vaccination.
Log2 titre increments are plotted 
according to a score based on 
time and number of prior A(H3N2) 
infections: 
0 = no infections; 
1 = 1 infection 4-9 Y prior 
2 = 2 infections 4-9 Y prior
3 = 1 infection 1-3 Y prior
4 = 2 infections 1-3 Y prior.

vaccination as a result of transient boosting;(24) such models can adjust for this dynamic process, and 
hence provide a more reliable estimate of the true exposure history. 
Figure 4 Schematic of Bayesian model of 
serological dynamics
Each participant in the study population has a set of 
unknown prior influenza exposures; these feed into a 
mechanistic model of cross-reactive antibody dynamics, 
which includes flexible parameters such as boosting, 
waning, and cross-reactivity. With sufficient observed titre 
data, it is possible to infer the unobserved processes and 
hence obtain estimates of exposure history for each 
individual.
Because such a model separately estimates individual-specific exposures and accompanying antibody 
dynamics, once it has been fitted to available data, it can simulate the likely antibody responses 
generated by any hypothetical combination of infection/vaccination. This makes it possible to forecast 
the temporal evolution of individual-level responses: antibody landscapes immediately following 
infection/vaccination may have a very different shape to landscapes months or years later. The model is 
not limited to simulating the sequence of exposures that occurred in the study population, it can also 
show what immune responses would be generated if influenza exhibited different epidemiology (e.g. in 
another setting) or under a different vaccination schedule.
To understand how individual-level antibody responses influence infection risk, and hence the 
effectiveness of vaccination, it is necessary to combine estimates of immunity with models of exposure 
risk. Previous work has shown that prior immunity may interact with person-to-person contact 
patterns in a non-linear manner to shape transmission during an outbreak.(36) Transmission models 
stratified by key epidemiological factors, including social contact patterns and prior immunity, have 
previously been used to estimate the direct and indirect effects of vaccination in different groups.(37, 
38) Individual-based models have also been used to examine the indirect benefits of health care worker 
vaccination on patient morbidity in different health settings.(39, 40) To account for such heterogeneity 
in infection, we will combine all our data, including surveillance and serological data, to infer individual-
level infection risk for a given immunological profile and prior exposure history, and hence evaluate the 
potential impact of different vaccination strategies.
6.2
COVID-19 
Given the similarities between cases presenting with influenza and cases presenting with COVID-19, 
our funding agency has requested that we add on surveillance research activities to enhance our 
understanding of the COVID-19 pandemic.  Our cohort presents a number of opportunities for 
epidemiological, virological and immunological investigations of COVID-19. Other novel coronavirus 
outbreaks, including SARS-CoV and MERS-CoV, have been characterized by nosocomial transmission,1-3 
and while this does not appear to be the main driver of transmission for COVID-19,4 it is likely that our 
ARI surveillance will detect some COVID-19 cases in HCWs. We are already collecting sera around April 
and November, which will enable assessment of the asymptomatic infection rate. We intend to follow 
up HCW experiencing ARI to document the duration of symptoms and the illness outcome (e.g. GP visit, 
hospitalization, days absent from work). With the availability of COVID-19 vaccines (CoVax), studies 
comparing COVID-19 vaccines are needed and likely to be numerous. Studies that investigate the 
cellular and molecular basis for any differences in antibody responses against the CoVax brands may be 
rarer. In addition, there may be limited analysis of responses to CoVax and influenza vaccination, 
whether the sequence of vaccines matters, and if so why. Importantly, we already have the 
infrastructure and teams in place to conduct follow up of HCW experiencing an influenza illness, which 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
5

can be utilized for the follow up of HCW experiencing a COVID-19 illness or a HCW who has been 
vaccinated for COVID-19. 
7
SIGNIFICANCE OF THE PROPOSED RESEARCH 
The nature and impact of prior immunity is largely ignored during the implementation and evaluation 
of influenza vaccines, despite the requirement for frequent vaccine update and re-administration. 
Apparent attenuation of effectiveness with repeated administration calls this practice into question. 
Prior attempts to understand repeated vaccination effects have limitations. For example, the antigenic 
distance hypothesis (21) considered only the immediate prior vaccination and not prior infection or 
earlier vaccinations. Prior immunological studies, including our own, have been able to include only 
limited evaluation of the potential influence of prior exposures (e.g. through landscapes), and have not 
searched for evidence of antibody focusing that is enhanced among highly vaccinated persons (e.g. 
through quantifying the memory B-cell response). Earlier studies of the effects of repeated vaccination 
included obsolete vaccines (e.g. whole cell), were methodologically weak (10, 11, 22), or focused on 
adjuvanted A(H1N1)pdm09 vaccine (41). Finally, no previous mathematical model of the effectiveness 
of influenza vaccination for preventing infection in HCW and patients has taken into account prior 
vaccination status or individual serological responses to vaccination, and their associated cost-
effectiveness analyses may therefore overestimate the benefits of annual vaccination. Advances in 
comprehensive immunologic measurements and computing power mean it is now possible to better 
understand the vaccine’s mechanism of action with respect to key immunologic concepts, such as 
original antigenic sin (25), back-boosting (24) and antibody focusing.
Completion of the proposed research will define influenza exposures and cellular and molecular 
processes that contribute to attenuating vaccine antibody responses. This in turn will inform measures 
to improve VE, including, for example, the potential utility of adapting annual strain selection, annual 
recommendations for which vaccine formulations to use (e.g. inactivated vs. LAIV), vaccination 
schedules (e.g. annual or not), and investment decisions for novel types of influenza vaccines. The 
outputs of our cohort surveillance will be used to inform new mathematical models of vaccination and 
infection dynamics relevant to this frequently vaccinated population. HCW represent a target group for 
vaccination largely comprised of healthy adults (i.e. the group for whom we believe the vaccine should 
work best) and who may have higher influenza infection rates (42). The findings from our study in 
Australia will be relevant to HCW vaccination policy in all nations and can be used to optimize 
vaccination schedules and re-evaluate the cost-effectiveness of HCW vaccination programs. Improving 
the methodology for HCW studies lays a foundation for the future: established HCW cohorts, like the 
one proposed, can be leveraged for the evaluation of novel vaccines and vaccination strategies for 
which HCW will likely be a key target. Our findings will also be relevant to other highly vaccinated 
populations, such as the elderly, and to the evaluation of novel vaccines and novel applications of 
existing vaccines. 
8
AIMS AND OBJECTIVES 
We propose to establish a longitudinal cohort of healthcare workers (HCW) to understand why 
immunogenicity and effectiveness appear to attenuate with repeated administration of the influenza 
vaccine. To do this, we will focus on three specific aims:
1.
To study how the immunogenicity and effectiveness of influenza vaccination is influenced by 
prior vaccination experience. 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
6

2.
To characterize immunological profiles following infection and vaccination
3.
To evaluate the impact of immunological profiles on vaccination effectiveness
8.1
Aim 1: To study how the immunogenicity and effectiveness of influenza vaccination 
is influenced by prior vaccination experience
8.1.1
Objectives:
1.
To compare immunological responses to vaccination by vaccination history 
2.
To compare influenza attack rates by vaccination history
8.1.2
Outcomes 
1.
Seropositivity post-vaccination; i.e. proportion of post-vaccination titres >40 (1)
2.
Seropositivity post-season; i.e. proportion of HCW with antibody titres >40 at the end of the 
season (~November each year) (1)
3.
Fold-rise in geometric mean antibody titre (GMT) per- to post-vaccination (1)
4.
Fold-change in geometric mean antibody titre (GMT) post-vaccination to post-season (1)
5.
Seroconversion fraction post-vaccination; i.e. proportion of samples with 4-fold increases in HI 
titre (1)
6.
Proportion of HCW PCR-positive for influenza at the end of each season (2)
Completion of this aim will result in immunogenicity data, as well as attack rates to potentially calculate 
VE, and confirm the existence of differences in immune responses by vaccination history and, as the 
study progresses, by infection history. 
Characterization of the cohort in this Aim will inform the selection of sera for further assessment in Aim 
2, and immunogenicity data and attack rates will inform Aim 3. 
8.2
Aim 2: To characterize immunological profiles following infection and vaccination
8.2.1
Objectives:
1.
To evaluate whether repeated vaccination leads to more focused or narrow antibody response 
profiles
2.
To evaluate whether the breadth of the antibody response is associated with influenza 
susceptibility in HCW
3.
To explore the cellular and molecular mechanisms that shape the antibody response
8.2.2
Outcomes
1.
HA antibody landscapes for vaccine-naïve and highly vaccinated HCWs (1)
2.
HA antibody landscapes for infected versus uninfected HCWs (2)
3.
Enumeration of influenza haemagglutinin (HA)-reactive B cells, and of subsets with phenotypic 
markers indicative of activation, and of memory versus naïve status, for vaccine-naïve, highly 
vaccinated and infected HCWs  (2,3)
4.
B cell receptor gene usage by influenza HA-reactive B cells recovered post-infection from 
selected vaccine-naïve, highly vaccinated and infected HCWs with distinct antibody response 
profiles. In depth characterization of HA antigenic sites recognized by serum antibodies from 
selected HCW including vaccine non-responders who lack seroprotection, and vaccine 
serological responders who fail to be protected (2,3)
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7

Qualitative insights from this Aim will inform the development of mechanistic mathematical models of 
antibody responses in Aim 3.
8.3
Aim 3: To evaluate the impact of immunological profiles on vaccination effectiveness
8.3.1
Objectives:
Quantify the impact of immune profiles on effectiveness of vaccination using mathematical models fit to 
data from Aims 1 and 2: 
1.
To estimate the key immunological parameters that shape observed antibody landscapes 
2.
To determine how antibody responses generated by prior exposures correlate with protective 
immunity
3.
To determine how different epidemiological scenarios, antigenic variation, vaccination 
schedules and vaccine compositions influence vaccination effectiveness
4.
Estimate the potential benefits of different HCW vaccination programs
8.3.2
Outcomes
1.
Quantify biological mechanisms that shape the antibody response
2.
Estimate protective titres 
3.
Estimate vaccine effectiveness 
4.
Optimal vaccination strategy for HCW under different vaccine availability
8.4
Aim 4: To increase our understanding of the epidemiological, virological and 
immunological characteristics of SARS-CoV-2 infections among HCWs
Note: activities relevant to this aim appear in the addendum to this protocol 
8.4.1
Objectives
1.
To estimate risk factors and correlates of protection for SARS-CoV-2 infection
2.
To characterise SARS-CoV-2 viruses infecting HCWs
3.
To characterise immunological profiles following infection by SARS-CoV-2
8.4.2
Outcomes
1.
Estimated attack rates among symptomatic and asymptomatic HCWs 
2.
Risk factors for asymptomatic, mild and severe infection
3.
Estimated antibody titre associated with protection 
4.
Estimated antibody kinetics over time
5.
Estimated duration of viral shedding and viral load over time
6.
Exploratory immunological findings to characterize the response to SARS-CoV-2 infection, 
including enumeration of SARS-CoV-2-reactive B  and T cells and identification of dominant 
epitopes
8.5
Aim 5: To increase our understanding of the immunological characteristics of SARS-
CoV-2 vaccination among HCWs
Note: activities relevant to this aim appear in the addendum to this protocol 
8.5.1
Objectives
1.
To measure and compare immunological responses to Adeno or RNA vaccines versus influenza 
protein vaccine
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8

2.
To characterise immunological profiles following vaccination for SARS-CoV-2
8.5.2
Outcomes
1.
Estimated post-vaccination serum antibody titres over time
2.
Enumeration of SARS-CoV-2-reactive B and T cells and identification of dominant epitopes
3.
Exploratory immunological findings to characterize the response to SARS-CoV-2 vaccination
9
STUDY DESIGN
9.1
Overview of Study Design
The proposed study is a longitudinal cohort study (Figure 5) that includes 2 nested studies (Figure 6 
and Figure 7). This study will collect data, blood and respiratory specimens from approximately 1500 
HCWs to understand responses to influenza vaccination. 
Figure 5. Study flowchart for recruitment of the primary cohort and main analyses proposed in Aim 1. 
We plan to recruit around 1500 HCWs, and expect that this will consist of at least 100 HCWs in each of the vaccination 
groups; the majority are likely to be frequent vaccinees. The expected proportion of each group is described in 12.1.
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9

 
Figure 6. Study flowchart for the nested study comparing highly vaccinated and vaccine-naïve HCWs
HCW Cohort Protocol. Version 4.3, 4 March 2021 
10

 
Figure 7. Study flowchart for the nested case-control study comparing A(H3N2)-infected  and 
uninfected HCWs
Note, unvaccinated HCW will also be eligible for post-infection follow and would not be vaccinated or have the post-
vaccination blood draw. 
9.2
Study Schedule
The study will run for 5 years (Figure 8). 
For Aim 1, recruitment of HCWs will begin in April 2020 to coincide with the usual timing of hospital 
influenza vaccination campaigns. Follow-up of HCW enrolled in year 1 will continue for 4 years (2020-
2023). The cohort will be open to recruitment of new HCW in years 2-4, with a preference for vaccine-
naïve HCW, to replace HCW lost to follow-up. Year 5 will be devoted to laboratory, statistical and 
mathematical analyses. Post-season blood samples will be collected by November or the end of the 
influenza season of each follow-up year. Samples collected in any study year will be analysed as 
described for Aim 1 at the end of each study year; i.e. starting around December (e.g. comparison of 
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11

post-vaccination HI titres; calculation of annual attack rates). We will allow 12 months in year 5 for final 
analyses and preparation of manuscripts reporting the final results for Aim 1.
 
Year
19
2020
2021
2022
2023
2024
 
Quarter
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
 
Preparation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Aim 1
Recruitment
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Baseline data & blood collection + 
vaccination
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Scheduled post-vaccination blood 
collections
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Monitoring and testing of influenza 
infection
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Post-season blood collection
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Interim statistical analyses for Aim 1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Laboratory analyses for Aim 1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Statistical analyses
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Aim 2
Develop and trouble-shoot panel for 
landscapes
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Development and trouble-shooting 
for B cell assays
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Landscapes comparing high/naïve
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B cell assays comparing high/naïve
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Assemble all data comparing 
high/naïve
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Landscapes comparing 
infected/uninfected 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B cell assays comparing 
infected/uninfected
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Assemble all data comparing 
infected/uninfected
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Aim 3
Development of mathematical 
modelling tools
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Apply models to landscape 
analyses
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Refinement of models, linking 
landscapes with infection risk
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Apply transmission dynamic models 
for vaccine effectiveness
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8. Study timeline, by year and quarter
For Aim 2, development and trouble-shooting of the panel to be used for landscape analyses will 
commence in year 1. Comparison of the immune responses for the nested study comparing highly 
vaccinated and vaccine-naïve HCWs will commence in year 2 when all sera from year 1 are available. 
This will include landscapes and memory B cell assays. Assays comparing infected and uninfected 
HCWs will be conducted in year 5 once the full set of infected HCWs is known. If, however, sufficient 
A(H3N2)-infected HCWs are identified sooner, these assays can commence sooner.
For Aim 3, preliminary development of the mathematical models will commence in year 1. Models 
comparing the antibody landscapes can commence in year 2 once early data are available, and can be 
updated as new data become available. Models for vaccine effectiveness will be worked up during the 
life of the project, with final models developed in year 5.
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12

10 STUDY POPULATION
10.1 Eligibility
10.1.1 Inclusion criteria
Eligible participants will be recruited from 1 of 6 participating hospitals (Table 5) and will meet the 
following criteria: 

Personnel (including staff, honorary staff, students and volunteers) located at a participating 
hospital or healthcare service at the time of recruitment who would be eligible for the hospital’s 
free vaccination programme

Be aged ≥18 years old and ≤60 years old;

Have a mobile phone that can receive and send SMS messages;

Willing and able to provide blood samples;

Available for follow-up over the next 7 months;

Able and willing to complete the informed consent process.
There are no restrictions on the type of HCW that can be recruited into the study in terms of their job 
role. HCW will be any hospital staff, including clinical, research, administrative and support staff. 
Table 4. Participating hospitals
Site
Hospital
Approx. 
number 
of staff
Approx. 
vaccination 
uptake in 2017
Site PI
Adelaide
Women’s and Children’s Hospital
3,000
~53%
Marshall
Brisbane
Queensland Children’s Hospital
4,000
~56%
Clark
Melbourne
Alfred Hospital
8,000
~81%
Cheng
Newcastle
John Hunter Hospital 
4,300
~51%
Wark
Perth
Perth Children’s Hospital
2,500
~60%
Blyth
Sydney
Children’s Hospital at Westmead
3,700
~50%
Macartney
10.1.2 Exclusion criteria

Immunosuppressive treatment (including systemic corticosteroids) within the past 6 months;

Personnel for whom vaccination is contraindicated at the time of recruitment 
10.2 Cohort Recruitment
10.2.1 Recruitment strategies
Participants will primarily be recruited from the staff vaccination clinics, which are temporarily set up 
around April each year for the influenza vaccination campaign. As per our previous HCW studies (see 
6.1), a member of the study team (e.g. site manager, phlebotomist) will wait outside the clinic to ask 
approaching staff members whether they are interested in participating in the study. If the staff 
members indicate their interest, the study team member will go through the eligibility criteria 
(Appendix A) and informed consent procedures. Baseline data collection and blood draws will take 
place in a private location located in or adjacent to the clinic. To facilitate recruitment, the baseline 
blood draw may occur up to two weeks prior to vaccination.
The study will be promoted via the staff newsletters, the staff influenza vaccination campaign webpage, 
the study website, email circulation to Heads of Department and with flyers, as permitted according to 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
13

each hospital’s policy. A copy of the advertising materials can be found in Appendix B.  HCWs will be 
able to contact the site manager or site PI to make an appointment for their baseline bleed. 
Participants who are interested in participating in the study may register their name and contact 
information on the study website for follow up from the site manager.
We expect that a high proportion of frequently and infrequently vaccinated HCWs will be willing to 
participate in the study and there is unlikely to be any risk associated with recruiting that target sample 
through the staff vaccination clinics. 
For vaccine-naïve and unvaccinated HCWs we will employ active, targeted recruitment strategies to 
ensure we meet our target sample size. This is expected to be the most difficult group to recruit. There 
is a possibility that people who are unwilling to be vaccinated have a dislike for needles which would 
also make them reluctant to participate in a study which involves blood draws. This is also a possibility 
for the other vaccination groups. HCWs will not be pressured into participation.  
Recruitment will be open, with new HCW recruited in years 2-4. Although we will make efforts to retain 
HCWs throughout the 4 years of active recruitment and follow-up, we expect some attrition and there is 
a need to recruit new vaccine-naïve and unvaccinated HCW to ensure there is a comparison group for 
annual analyses.
Participants will be reimbursed for their time, with a $10 gift voucher at each visit that involves a blood 
draw: 

Participants in the main study will receive a gift voucher at the enrolment, 1 post-vaccination 
and 1 post-season visits. 

Participants in the nested cohort will receive a gift voucher at the enrolment, 2 post-vaccination, 
and 1 post-season visits. 

Participants testing positive for influenza will receive a gift voucher at the 2 post infection visits.

Participants testing positive for SARS-CoV-2 will receive a gift voucher for each post-infection 
visit (up to 5, but it is not expected all will be able to be followed). 
10.2.2 Retention strategies
HCW will be enrolled for up to 4 years. We do not expect high attrition as we achieved 90% retention 
without any incentives in our pilot study at the Peter MacCallum Cancer Centre in 2015 and 83% 
retention in our study at the Royal Melbourne Hospital (RMH). The latter is a larger hospital with higher 
staff turn-over and thus for the proposed study we have tried to select hospitals with a smaller staff 
populations.
To retain HCWs we will use the following strategies: 
1.
Weekly reminders through SMS or email to report ARI symptoms during the influenza 
season (however disruptions to usual seasonality might mean that a different period is more 
appropriate.). Frequent reminders worked well in our study at RMH to improve retention 
and has been associated with reduced attrition in cohort studies; 
2.
MS Outlook calendar invites and SMS reminders for scheduled follow-up appointments, 
which worked well in our previous HCW studies; 
3.
Gift vouchers (value $10) at each follow-up appointment involving a blood draw;
4.
Monitor completion of weekly ARI surveys to identify participants who are not responding 
and identify communication issues; 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
14

5.
Provide prompt feedback about the results of influenza testing;
6.
Provide annual feedback on antibody responses to vaccination (e.g. HI titre responses to the 
4 vaccine components);
7.
Potentially reward HCWs for completing weekly symptoms diaries, such as holding 
competitions for high completion, raffles (budget permitting);
8.
Potentially reward HCW who complete all follow-up visits with bonus reimbursements 
(budget permitting);
9.
Study website with copies of publications or presentations arising from the study.  
10.2.3 Follow-up
At enrolment, participants will be requested to provide a mobile phone number and email address. 
Follow-up appointment reminders will be sent via email and/or SMS (Appendix D).  
In addition, participants will be given an appointment card at enrolment (Appendix G). The dates of 
visits will be completed as each visit happens. This is so that participants will have at hand their 
participant ID, the timeline of visits that they will have to do (specific to them) and contact numbers for 
questions/concerns. 
10.2.4 Informed consent
HCWs will be recruited at the staff vaccination clinic or via study marketing materials. HCWs who 
express an interest in participation will be provided with a copy of the participant information and 
consent form, either in person (if recruited at the staff vaccination clinic) or by email (if recruited via 
study marketing materials). Written or electronic informed consent will be sought from all HCWs prior 
to enrolment in the study. Participation in the study will be voluntary. HCWs will be informed that they 
are free to withdraw from the study at any time.  The concept of voluntary participation will be clearly 
explained and ample time will be given to ensure that participants understand the content and have all 
of their questions answered. 
Willing HCWs will sign the informed consent to indicate assurance of their understanding and 
voluntary participation in the research. Informed consent will be administered by the site manager who 
will also sign his/her name. A copy of the signed and dated consent form will be given to the HCW. This 
process will be documented in the HCW’s study file. To minimise face-to-face contact, screening may 
take place over the phone. Consent will then be a two step process: verbal consent will first be obtained 
and recorded in the database; signed consent will then be collected electronically through REDCap. 
Participants will be able to sign the electronic consent form using a mouse (for desktop computers) or 
finger (for mobile devices). Participants may also sign and scan the PICF (HCWs can mail the original 
signed formed to the study team). In these cases, the HCW will retain the electronic copy of the PICF. 
Personal information will not be released without written permission of the participant, except as 
necessary for monitoring by the Human Research Ethics Committee.  The study coordinator and site 
PI’s contact details will be made available to participants should they have any queries or concerns 
during the study. Signed informed consent forms will be stored in the study office in a locked filing 
cabinet.
For the unvaccinated group of HCWs, the importance of vaccination will be explained and they will be 
offered the option of receiving the vaccine. If they still decide that they do not wish to be vaccinated, but 
do wish to participate in the study, we will enrol them and collect data and biological specimens as 
described above for the unvaccinated group. Those who decide to be vaccinated will be referred to the 
vaccination clinic, as per hospital protocols. 
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10.3 Data Collection
10.3.1 Overview of data collection  
The data collected from participants includes:
1.
Baseline questionnaire
2.
Social contacts questionnaire
3.
Additional information – participant contact information and influenza vaccination 
documentation
4.
Symptoms diaries (during the influenza season or during inter-seasonal period if there is an 
increase in COVID activity)
5.
Blood samples
6.
Respiratory swab samples
At each recruitment site, the same general procedures for obtaining informed consent, collecting 
baseline data and collection of specimens will be followed. Tables summarising the procedures at each 
visit are found below. 
Table 5. Scheduled and unscheduled visits for participants in the main study. Visits shown are for 1 
year only.
Procedures
Assessment/ Procedure
Screening 
& 
recruitment
Visit 1
(14d)*
Visit 2
(7m)
Visit 3
(ARI)
Visit 4
(7d post-
infA+ ARI)
Visit 5
(14d post-
infA+ ARI)
Weekly 
(during 
influenza 
season)
Informed consent
x
Demographic information
x
Vaccination history
x
Risk factors
x
Height/weight 
measurement
x
Vaccination
x
Sera collection
x
x
x
x
x
PBMC collection
x**
x
x
Symptoms diary 
x
Respiratory swab
x
*as more data become available the exact timing of the post-vaccination bleed may change; however the number of 
bleeds will remain the same. Blue shading indicates unscheduled visits. infA+ ARI = influenza A-positive ARI.
**if influenza A positive, PBMC collection to occur post-season
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Table 6. Scheduled and unscheduled visits for participants in the nested case control study 
comparing highly vaccinated and vaccine-naïve participants. Visits shown are for 1 year only. 
Procedures
Assessment/ 
Procedure
Screening & 
recruitment
Visit 1
(7d)*
Visit 2
(14d)*
Visit 3
(7m)
Visit 4
(ARI)
Visit 5
(7d post- 
infA+ ARI)
Visit 6
(14d post- 
infA+ ARI)
Weekly
(during 
influenz
a 
season)
Informed consent
x
Demographic 
information
x
Vaccination history
x
Risk factors
x
Height/weight 
measurement
x
Vaccination
x
Sera collection 
x
x
x
x
x
PBMC collection
x
x
x
x
x
x
Symptoms diary 
x
Respiratory swab
x
*as more data become available the exact timing of post-vaccination bleeds may change; however the number of bleeds 
will remain the same. Blue shading indicates unscheduled visits. infA+ ARI = influenza A-positive ARI. 
10.3.2 Baseline questionnaire
A baseline questionnaire will be used to collect information about HCW’s including demographics, 
height, weight, vaccination history, and risk factors for influenza infection such as workplace exposures 
and relevant medical history (39)(Appendix C). The past 5-years’ vaccination history of the HCW will be 
determined through self-report and corroborated with staff influenza vaccination records, where 
available. 
Participants will provide their email and mobile phone number, to allow a unique survey link to be sent 
for the baseline questionnaire. If paper questionnaires are being used, this information will be collected 
on the form and entered into the study database at a later time. 
10.3.3 Social contacts questionnaires
To help inform our understanding of transmission dynamics, HCW will be requested to complete a 
social contacts questionnaire. These are detailed questionnaires designed to inform contact networks 
analysis. A contact is defined as conversational, involving a two-way face-to-face conversation of 3 
words or more; or physical, involving physical touch.
For each contact, HCW will be asked:
1.
Age of contact. Enter age
2.
Gender of contact. Choose from:  Female, Male, Other, Unspecified
3.
Type of interaction. Choose from: Conversational (two-way face-to-face conversation of 3 words or 
more) OR Physical (handshake, hug, kiss, etc)
4.
Location of interaction. Choose from: Home, School, Work, Other
5.
Have you spoken to this person before? Choose from:  Yes or No
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Questionnaires will be requested at baseline and at a later time coinciding roughly with the peak of 
influenza activity. 
10.3.4 Vaccination documentation  
The 5 year influenza vaccination history will be determined through self-report and corroborated with 
staff vaccination records, where available. The brand and batch number of vaccine received at 
enrolment will be recorded. Only 3 vaccines are licensed for adults aged 18-60 years in Australia and all 
are quadrivalent.
COVID vaccination documentation may also be collected should a HCW be vaccinated for COVID. The 
date, brand and batch number of the vaccine received will be recorded.
10.3.5 Blood collection
10.3.5.1 Schedule of bleeds
The frequency of blood sample collection will vary depending on whether the participant consents to 
collection of additional samples for recovery of peripheral blood mononuclear cells (PBMCs) for the 
nested case control studies. All blood samples will be collected in a private room.
Main study: For the majority of vaccinated participants, serum samples will be collected according to 
the following schedule:
1.
3 visits at pre-specified times: baseline, post-vaccination and post-season
a.
1 blood sample will be collected at each visit in a 9ml serum tube
For unvaccinated participants, the post-vaccination blood sample is irrelevant and visit will be 
scheduled as follows: 
1.
2 visits at pre-specified times: baseline and post-season
a.
1 blood sample will be collected at each visit in a 9ml serum tube
Nested cohort study of highly vaccinated and vaccine-naïve HCWs: For a subset of roughly 60 highly 
vaccinated and 60 vaccine-naïve participants, samples will be collected according to the following 
schedule:
1.
4 visits at pre-specified times: baseline, 2 post-vaccination times and post-season
a.
3 blood samples will be collected at each visit:
i.
1 × 9ml serum tube 
ii.
2 × 9ml samples in sodium heparin tubes (for recovery of PBMCs)
Nested case control study of infected and uninfected HCWs: For HCWs who report a respiratory illness 
and are identified to have tested influenza-A positive, further blood samples will be requested. Where 
the subtype is known, follow up sampling will only be performed for H3N2-positive participants. The 
exact number is difficult to predict but is expected to be around 60 per year:  
1.
2 additional visits post illness onset
a.
3 blood samples will be collected: 
i.
1 × 9ml serum tube 
ii.
2 × 9ml samples in sodium heparin tubes (for recovery of PBMCs)
2.
Post-season, 2 additional blood samples will be collected (if not already being collected):
a.
2 × 9ml samples in sodium heparin tubes (for recovery of PBMCs)
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For all groups, the exact timing of the post-vaccination bleed(s) and post-infection bleed(s) may change 
as more data about optimal timing become available. Where the number of days is specified, some 
leeway is permitted.  For example, blood samples taken 7 days post-vaccination or infection may be 
taken 6-12 post; samples taken 14 days post-vaccination or infection may be taken 14-21 days post. 
However, the number of bleeds will remain the same. 
10.3.5.2 COVID vaccination
If the timing of the COVID and influenza vaccines do not coincide, HCWs who are vaccinated for COVID 
may be requested to provide additional blood samples. Schedules outlined in Tables 5 & 6 will be used 
to guide timing. Extra blood draws will be minimized as much as possible.
10.3.5.3 Blood collection procedures
Blood collection will be performed in line with each hospital’s phlebotomy policy. The use of a butterfly 
needle connected to a vacutainer tube is recommended as the best collection method to minimize 
haemolysis and to reduce the risk of needle stick injury. Blood collection will take place in a private 
room or behind a screen. 
10.3.5.4 Blood sample processing
Blood samples will be processed and stored at each recruitment site until the end of the data collection 
period, after which they will be cold-shipped to the Doherty Institute in Melbourne for analysis.  See 
section 13 for further details.
10.3.6 Active surveillance for acute illness
HCWs will be asked to complete symptoms diaries in the form of a simple, weekly online questionnaire 
(Appendix D). Weekly email and/or SMS reminders will include a link to the online survey.  Surveys will 
be sent during the influenza season, however disruptions to usual seasonality might mean that a 
different period is more appropriate. Weekly symptom surveys may recommence for a particular site 
during the inter-seasonal period, if there is an increase in community transmission of influenza or 
SARS-CoV-2.  
A range of symptoms commonly used in influenza symptom severity questionnaires will be graded by 
participants as absent, mild, moderate or severe. HCWs will have the opportunity to complete previous 
weeks’ reports, which has been found greatly improves the completeness of reporting. Frequent 
reminders should minimize recall bias and missing data.
HCWs reporting ≥1 respiratory symptom (e.g. cough, sore throat, stuffy nose, chest pain, difficulty 
breathing) and ≥1 systemic symptom (e.g. feverishness, temperature ≥38°C, chills, headache) or ≥2 
respiratory symptoms will be requested to self-collect a respiratory swab. Participants may be provided 
with a thermometer and will monitor their temperature for 3 days at enrolment to establish their own 
baseline for normal temperature and fever. 
Participants will be requested to complete daily symptoms diaries until their illness resolves, which will 
include only the symptoms questions from the weekly diary. Site staff will follow-up HCW to determine 
for how long they remained unwell and whether additional medical attention was required, if this 
information has not been provided in the weekly diaries.
10.3.6.1 Respiratory specimen collection
Participants will be provided with swab kits (swab plus vial of universal transport medium) at 
enrolment with instructions on how to use them (). Respiratory swabs in universal transport medium 
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can be stored in the home refrigerator if the HCW is on sick leave. The site manager will be alerted 
when a HCW has ARI symptoms and will follow up with the HCW to arrange for respiratory swabs to be 
forwarded to the hospital laboratory for testing. Depending on state government Department of Health 
and/or hospital requirements at the time, HCWs may be required to attend a COVID-19 screening clinic 
for a COVID-19 test. If so, study staff may retrieve swabs collected at a COVID-19 screening clinic to 
ensure the sample is tested for influenza and other respiratory viruses. Positive swabs will be 
forwarded to the Doherty Institute for virus characterization. 
11 LABORATORY PROCEDURES
11.1 Laboratory procedures relevant to Aim 1
11.1.1 Sero-response to vaccination
Pre-vaccination, post-vaccination and post-season serum samples will be tested for antibodies to the 4 
vaccine strains (cell- + egg-derived) in the current year’s quadrivalent influenza vaccine. We will 
primarily use haemagglutination inhibition (HI) assays to measure antibody titres. This assay measures 
the ability of antibodies in the blood to prevent haemagglutination—the attachment of influenza virus 
particles to red blood cells. Samples are serially diluted (titrated) and the highest dilution of serum that 
prevents haemagglutination is the HI titre. Reciprocal titres of 40 or higher are generally accepted as 
indicating protection against infection (“seropositivity”), with very high titres suggestive of recent 
infection (43). The HI assay is the standard assay used to measure antibody response to both infection 
and vaccination, and to assess the sensitivity of circulating influenza viruses to the vaccine. 
For A(H3N2) viruses, we may use a microneutralisation (MN) to assess A(H3N2) antibody responses to 
vaccination. This is a functional assay that measures the ability of antibodies to neutralize virus 
infectivity. Serum is mixed with virus and residual infectivity of the virus is assessed by adding the 
mixture to cells. The highest dilution of serum that neutralizes virus infectivity is the MN titre. 
Concordance between the HI and MN is high (44).
Other antibody assays may be performed as well to understand the breadth of the antibody response. 
These might include but would not be limited to neuraminidase antibody assays.
Participants will receive results of their antibody responses to vaccination (e.g. HI titre responses to the 
4 vaccine components) ().
11.1.2 Influenza testing and virus characterization
Respiratory swabs collected from HCWs reporting ARI symptoms will be tested using reverse 
transcription real-time polymerase chain reaction (RT-PCR), according to standard operating 
procedures in each hospital laboratory. Influenza-positive samples will be forwarded to the WHOCCRRI 
for virus characterization.  The virus subtype (for influenza A) or lineage (for influenza B) will be 
identified. Viruses will be isolated and tested by HI/MN or similar assay to assess antigenic match to 
vaccine, and sequenced to assess genetic match to the vaccine and to identify any genetic clusters 
within hospitals.
11.1.3 Reporting of influenza RT-PCR results
The site manager will receive RT-PCR results from their relevant diagnostic laboratory and enter the 
results into the study database. Participants will be informed of their test results and provided with the 
standard results sheet provided by each testing laboratory. Participants will be informed that: 
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
False positive and negative results are possible; 

Participants should consult their personal healthcare provider if they have questions, concerns, 
or any medical needs related to their illness; 

They should follow their employer’s guidelines for reporting illnesses and returning to work.  

Participants will be reminded that they will be contacted to schedule post-infection follow-up 
bleeds.
11.2 Laboratory procedures relevant to Aim 2
For aim 2, samples collected from HCW in the two nested studies will be further assessed. These 
include: 
1.
Highly vaccinated HCWs
2.
Vaccine-naïve HCWs at the time of recruitment
3.
Infected HCWs
Samples from other participants may also be assessed. For example, if after initial comparisons of 
highly vaccinated and vaccine-naive HCW, it becomes necessary to understand how responses might 
differ in HCWs vaccinated 2 or 3 times prior to recruitment, we may select sera from such participants 
for further assessment. 
11.2.1 Antibody landscapes 
We will build antibody landscapes (24) by testing sera in HI assay (as described in 11.1.1) against 
influenza A(H3N2) strains that have circulated since 1968, including currently circulating strains and 
candidate vaccine viruses to assess “future” protection (see Figure 1). The panel will probably include 
3-4 viruses from each antigenic cluster. We have already established a panel of ~33 A(H3N2) viruses 
(see Figure 1), to which we will add additional viruses as the study progresses. 
11.2.2 HA-reactive B cell response
PBMCs will be used to enumerate influenza haemagglutinin (HA) reactive B cells and their phenotypic 
subsets. Analysis on day 7 will focus on B cells that have actively participated in the response, indicated 
by high expression of proliferation (CD71) markers and/or by expression of plasmablast markers 
(CD38hi,CD27hi)(45).
A mixture of fluorescent-labeled HAs representing HAs of vaccine and past strains will be used to 
differentiate B cells that react only against the vaccine strain from those that cross-react against vaccine 
and past-strains. This analysis will define whether there is greater dominance of cross-reactive memory 
B cells over vaccine strain-only-reactive B cells in highly vaccinated HCW compared to previously 
vaccine naïve HCW, and whether differences in the composition of the B cell response underlie 
differences in the breadth of the serum antibody response.
To further validate whether B cells that react with vaccine HA only originate from the naïve B cell pool 
while HA cross-reactive B cells originate from the memory pool they will be individually sorted for 
selected HCW. B cell receptors (BCR) will be amplified, bar-coded and sequenced using high-throughput 
Illumina. The selection of HCW and time-points (e.g. d7 or 14) will be based on frequencies observed 
during the initial analyses described above. 
To examine whether repeated vaccination is associated with focusing of B cell HA responses BCR 
diversity will be compared amongst cross-reactive memory B cells from selected vaccine naïve and 
highly vaccinated HCWs who have highly expanded clones. 
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11.2.3 Antibody focussing
Antigenic site-specific monoclonal antibodies (mAbs)(28) and reverse engineered viruses containing 
single substitutions in HA antigenic sites will be used to investigate antibody focusing (28, 29). Sera 
from HCWs exhibiting a range of responses will be assessed, including, but not limited to:
1.
Pre-vaccination sera from HCWs who did not appear to respond to the vaccine
2.
Pre- and post-vaccination and post-infected sera from HCWs who appeared to respond to the 
vaccine but were subsequently infected 
3.
Pre- and post-vaccination sera from HCWs who appeared to respond to the vaccine and did not 
get infected despite probable exposure. Probable exposure will be defined exposure to 
colleagues or patients with confirmed influenza.
4.
Additional groups selected as further information about the antibody responses become 
available during the study. 
5.
Preference will be given to sera from HCWs in the nested study to facilitate comparison of B cell 
probe reactivity and serum antigenic site reactivity
mAbs procedures: competitive ELISAs utilizing biotinylated mAbs will be used to detect epitope specific 
antibodies in human sera. The mAb panel will gradually be augmented as B cell receptors (BCRs) from 
sorted HA-reactive and cross-reactive B cells from our vaccine studies are expressed. We will only use 
mAbs that inhibit HI at concentrations ≤ 1ug/ml.
Reverse-engineered viruses: Viruses with engineered point mutations within HA antigenic sites (28) 
will be use to compare serum HI titres against viruses containing wild-type versus engineered HA. The 
extent to which antibodies are focused on a given antigenic site in a vaccine virus will be indicated by 
the extent to which titres are reduced by substitution(s) within that site. We will focus on introducing 
substitutions within an antigenic site(s) that has been preserved in successive strains, and/or that 
appears to be the focus of serum antibody binding based on mAb studies. Where possible we will 
introduce substitutions that subsequently occur in circulating strains, and also examine whether this 
contributes to any loss in titre. 
12  DATA CONSIDERATIONS
12.1 Sample Size
We aim to recruit 1,500 HCWs. This is based on the number of HCW we reasonably expect to recruit 
and retain (~250 per site), based on our previous studies of HCW (see 6.1) and detection of meaningful 
effects.
We expect that there will be relatively more highly vaccinated HCW willing to participate. We will 
encourage recruitment of unvaccinated and vaccine-naïve HCW to ensure there are adequate numbers 
of these groups for comparisons with HCW vaccinated repeatedly. The target samples size by 
vaccination group is approximately:

600 frequently vaccinated(vaccinated 4/5 or 5/5 prior years), 

300 infrequently vaccinated (2/5 or 3/5 prior)

200 vaccinated once (1/5 prior)

200 vaccine-naïve (0/5 prior) 

200 unvaccinated (0/5 prior and not vaccinated in current year).
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Enrolment will be open for recruitment of new vaccine-naïve and unvaccinated HCWs in each study 
year. Annual analyses will be separated because our underlying hypothesis presupposes that the repeat 
vaccination effects will not be apparent in all years. Samples sizes were calculated in R 3.4.1 using the 
TrialSize package.
12.1.1 Primary objective
For the primary objective of Aim 1 (immunogenicity), the power analysis considers expected post-
vaccination seropositivity among vaccinated HCWs in any one study year (46). A recent US study (47) 
observed a trend in post-vaccination seropositivity against A(H3N2) from 49% in frequently 
vaccinated, to 57% in infrequently vaccinated and 69% in vaccine-naïve HCWs; i.e. a roughly 10 
percentage point difference, which we consider meaningful. In our study at RMH, sero-positivity among 
frequent vaccinees was 76% and the ratio of frequent: infrequent vaccinees was 2.5:1; thus the sample 
needed to see a trend in sero-positivity from 76% to 86% at =0.05, =0.2 is at least 162 vaccine-naive 
α
β
and 405 frequently vaccinated HCWs. 
12.1.2 Secondary objectives
For the secondary objective of Aim 1 (attack rates), with a sample of 1,500, we expect ARIs to be 
reported among ~40% of HCWs, among whom ~20-25% will test positive, for an annual attack rate of 
~3.5% (n≈50/ year; 200 total). This is consistent with rates reported in the Ha Nam cohort and our 
previous HCW studies. Published data suggest attack rates will be around 1.2% among vaccine naïve 
and 5.44% among the unvaccinated (42), and that the odds of infection will follow a trend, with the 
odds lowest for vaccine-naïve, followed by unvaccinated, then the repeat vaccinees (22). If we assume a 
monotonic trend within levels of repeated vaccination, with the odds of infection highest for the highly 
vaccinated (and comparable with the unvaccinated), with at least 159 HCW per vaccination group, at 
=0.05, this study will have 80% power ( =0.2) to detect a trend in attack rates from 1.2 in vaccine-
α
β
naïve, to 5.4 in frequently vaccinated/unvaccinated. We therefore aim to recruit at least 200 HCWs per 
vaccination group (to account for potential attrition). Based on surveillance data, >50% of infections are 
expected to be A(H3N2), and if we are able to collect day 14 post-infection sera from at least 50%, we 
will have a feasible number of infections for the immunological analyses proposed in Aim 2 (n≈50).
The total staff population at the 6 hospitals is 25,500, with vaccination uptake in 2017 at 50-80%. Thus, 
we expect to meet our target sample size of 1,500, which represents just 6% of staff.  
12.2 Data Analysis
All statistical analyses will be performed in the statistical software package R.
12.3 Data cleaning
The data collected will be reviewed and cleaned prior to any statistical analyses. Cross-tabs will first be 
run for categorical variables to identify any unusual associations. The paper/electronic record will be 
checked to identify the data entry error.  For continuous variables, histograms and boxplots will be 
generated to potential identify outliers.  All outliers will be checked against the original data collection 
records.  
12.4 Missing data
Missing data patterns will be explored and the method of multiple imputation will be used for handling 
missing data and to assess the sensitivity of the results to the missingness (48).
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12.5 Analyses relevant to Aim 1
12.5.1 Baseline comparisons
Descriptive statistics will be calculated to summarise the data collected at baseline, following STROBE 
guidelines.  Histograms will be generated to examine the distribution of continuous variables.  
Descriptive statistics to be reported will be frequency (%) for categorical variables, and mean (standard 
deviation) (or median, 25th-75th percentiles) and range for continuous variables.  The baseline 
distribution of log2 antibody titre, age, sex, number of years worked at the hospital, occupation type, 
employment status and presence of high-risk conditions, will be presented by vaccination groups. 
12.5.2 Serological endpoints
We will compare day post-vaccination HI titres among vaccination groups each season: 
1.
Seropositivity among vaccination groups will be calculated and compared using logistic 
regression, with seropositivity coded as 1 if the titre ≥40, and 0 if the titre is <40.  We will test 
for trend among vaccination groups, assuming seropositivity will be lowest in the most highly 
vaccinated. 
2.
Seroconversion post-vaccination will be calculated and compared among vaccination groups by 
logistic regression, with seroconversion coded as 1 if the fold-rise in titre is ≥4 and 0 if the fold-
rise in titre is <4.  We will test for trend, assuming seroconversion will be lowest in the most 
highly vaccinated.
3.
The post-vaccination fold-rise in antibody titres will be assessed using linear regression.  The 
Jonckheere-Terpstra test will be used to test for a trend, assuming the log2 antibody titres in the 
highly-vaccinated group will be lower than the rarely vaccinated group, which in turn will be 
lower than the vaccine-naïve group. 
The main exposure of interest is the vaccination experience of HCWs (categorical). All models will be 
adjusted for potential confounders and factors that may influence immune response; e.g. baseline titre, 
age, sex, BMI. 
12.5.3 Influenza attack rates and VE
Evidence of influenza infection will be based on RT-PCR-confirmed infection, only, as serological 
evidence may be biased in vaccinees who elicit a good antibody response to vaccination (22). Attack 
rates will be calculated for each vaccination group as the number of cases during the person-time at 
risk. 
VE will be estimated using a Cox proportional hazards regression model comparing the risk of influenza 
infection (coded as 1 for infected or 0 for uninfected) among HCWs by vaccination status: VE = (1-HRadj) 
× 100%. If there are sufficient cases, the model will be adjusted for potential confounders (e.g. age 
group), and factors that may modify the risk of infection. Using virus characterization data, we will 
assess if failures are associated with antigenic mismatch. 
12.5.4 Duration of illness
The number of days ill with influenza (count) will be compared among vaccination groups, adjusted for 
age. Because of the excess of 0 counts (people who never get infected), zero-inflated negative binomial 
regression will be used.
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12.6 Analyses relevant to Aims 2 and 3
The laboratory analyses conducted in Aim 2 are relatively new and exploratory and there are therefore 
no well-established or well-accepted statistical methods for their comparison. The project statistician 
will consult with statistical collaborators at the Doherty Institute and with PI Kucharski to explore 
potential statistical methods as new data become available. For analysis of memory B cell data, only a 
small number of samples assayed will be assessed and it is likely that the sample will be too small for 
meaningful statistical analyses.  This is due to the high cost of these assays. Thus, these data will be 
qualitatively assessed. 
Aim 3 specifically addresses the parameterisation and interpretation of antibody data generated from 
Aim 2 by building mathematical and statistical models.  Once parameterised, the immunological data 
will be used in inform mathematical and statistical models to understand risk of infection and the 
expected benefits of HCW vaccination programs. 
Although several analyses are proposed herewith, as modelling tools are developed during the life of 
the project additional and/or modified analyses of these data will be conducted. 
12.6.1 Interpreting the landscapes
Landscapes will initially be plotted for different groups of interest (i.e. vaccine-naïve, highly vaccinated 
and infected) and compared qualitatively. These initial plots will be based on predicted titres output 
from a random effects model that estimates the titre for each antigen, separately by time of blood 
collection. Some further exploratory statistical analyses of the antibody landscapes may be performed, 
such as using dimension reduction techniques and latent-class models to classify the shape of the 
antibody dynamic over time. 
As part of Aim 3, models of antibody dynamics and individual-level exposures will be develop to 
quantify the different aspects of the antibody response that generated observed immunological profiles. 
These models will have linked components incorporating individual-level infection and vaccination 
history, cross-reactive antibody dynamics, as well as an observation process to account for noisy titre 
measurements (
 ). We will include parameters to control for each feature of the antibody response such as antigenic 
seniority, back-boosting, cross-reaction and waning of responses. These will be separately modelled to 
capture the relative contributions of prior infection and vaccination to underlying titres (Error: 
Reference source not found). We will also include observation error and censoring to convert the 
continuous model of expected titres into discrete serological measurements. 
 
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Figure 9. Antibody dynamic model 
The model includes three components that reflect processes that are generally unknown or only partially observed: (A) 
individual-level infection or (B) vaccination history; via a model cross-reactive dynamics (C), these are linked to the third 
component (D), a ‘smooth’ underlying antibody landscape that results from a given history. An observation process (E) 
converts the three partially observed components into the final component, a set of noisy observed titres (F). By iteratively 
resampling model parameters using Markov chain Monte Carlo, it is possible to infer the unobserved processes from 
observed titre data.
Figure 10. Model of antibody processes
The model translates exposure history into expected antibody titres, as indicated by arrow (C) in Figure 8. Here λij 
denotes expected titre against strain j for individual i, with other parameters described in 
.
With this model, we will be able to calculate the likelihood of observing a particular set of titres against 
the test strains, given a specific infection history and set of antibody response parameters. This will 
make it possible to estimate infection and vaccination histories as well as antibody response 
parameters in a statistically robust way, using Bayesian tools such as Markov chain Monte Carlo. As well 
as HI titre data, the inference process will include observed data on individual infections and 
vaccinations as informative priors, to reduce uncertainty about prior influenza exposures. We will use 
simulation studies to ensure that the inference methods can provide reliable parameter estimates given 
the size and structure of the available datasets. Using this modelling approach, we will be able to 
quantify the differences between immune responses following natural infection and vaccination, and 
obtain estimates of prior infections and vaccination in the study population.
Table 7. Parameters to be estimated in the antibody dynamic model, as defined in Figure 9
Letters in the ‘process’ column correspond to the labels in 
 . Although we will initially use simple functions to model processes such as antigenic seniority, these will be refined 
based on the findings from the B cell dynamics measured in Aim 2.
Process
Parameter to be fitted
Functional form in model
A. Infection history
Set of times at which infections 
occurred
Vector of timings
B. Vaccination 
history
Set of times at which vaccination 
occurred
Vector of timings
C. Model of antibody 
dynamics
Long-term boosting μ1(m) following 
exposure to strain m
Constant value, fitted separately for infection and 
vaccination exposures
Long-term cross-reactive response 
against strain j following exposure to 
strain m
Linear decline in titre based on antigenic map distance 
between strains, scaled by a cross-reaction parameter 
σ1
Short-term boosting μ2(m) following 
exposure to strain m
Constant value, fitted separately for infection and 
vaccination exposures
Short-term cross-reactive response 
against strain j following exposure to 
strain m
Linear decline in titre based on antigenic map distance 
between strains, scaled by a cross-reaction parameter 
σ2
Waning
Linear decline in titre with time, scaled by a waning rate 
ω
Antigenic seniority
Linear decline with number of infections, scaled by 
parameter α
E. Observation 
Observation error
Normally distributed with standard deviation parameter 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
26

model
ε, with censoring at discrete values]
12.6.2 Using dynamic antibody landscapes to predict infection risks
To investigate the implications of antibody landscapes for vaccination effectiveness, we will translate 
individual-level immune responses into landscapes that reflect protective immunity against specific 
strains. We will combine antibody responses with empirical data on correlates of protection, as well as 
observed infections in our cohort, to estimate infection risk against current and future influenza strains. 
In particular, the model will include a process parameter to adjust for individual HCW exposure risk, as 
infections will depend both on the extent of protective immunity and the potential for exposure. This 
will produce a generalizable model that can simulate individual-level immunity against any given 
antigenic variant, and show how this immunity is expected to change as antibody responses are boosted 
via infection or vaccination, and wane afterwards. As the model is refined we will identify a minimum 
set of titres against past or forward strains that capture the underlying ‘smooth’ antibody landscape and 
provide a reliable correlate of protection. 
12.6.3 Evaluating current influenza vaccine effectiveness
We will use our validated model to refine estimates of the reduction in infection and disease resulting 
from HCW vaccination. The mechanistic nature of our mathematical model means we will be able to 
estimate immune responses following any hypothetical combination of infections or vaccination, not 
just those actually observed in our study population. Our model will therefore be able to account for the 
prevalent subtype or lineage; vaccination coverage, including different levels of HCW coverage and the 
mix of vaccine experience among HCWs; VE factors, including the expected VE dependent on the 
vaccine experience of each HCW; and vaccine composition, including whether the vaccine strains have 
been updated between seasons. We will also consider the influence of pre-existing immunity – both 
natural and vaccination-induced – based on dynamic antibody responses parameterized from antibody 
titre data. Using these data, we will use our model to explain variation in vaccination response among 
HCWs in light of their prior infection histories, vaccination experience, and risk of infection to 
understand why VE might differ by vaccination experience. 
12.6.4 Evaluating alternative influenza vaccine scenarios
With our model in place, we will also compare the performance of current vaccination programs with 
simulated alternatives to predict the impact of repeated vaccination and circulating virus on VE under 
different scenarios. In particular, we will examine the potential impact of: highly-valent vaccines, which 
include more than a single strain for each subtype; universal vaccines that generate a broadly cross-
reactive response against conserved influenza epitopes; and near-universal vaccines that produce a 
broader response, but still have potential to generate effects such as antibody focusing or seniority, 
which could reduce effectiveness.
13 STORAGE OF BIOSPECIMENS
13.1 Serum samples storage and shipping
Blood samples collected in serum tubes will be centrifuged within 24 hours of collection to separate the 
clotted blood from the serum and the serum removed to a clean tube. The clotted blood may be 
discarded. Sera will be aliquoted into separate tubes, the number of which will depend on the volume 
recovered, but is expected to be around 3 aliquots of approximately 200-500 l each. Each aliquot will 
μ
be labelled with the study identiﬁcation number and stored at -20°C. 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
27

Serum aliquots will be stored at the recruitment site until the end of each study year when they will be 
shipped on ice to the Doherty site in Melbourne. At the Doherty, 1 serum aliquot will be stored at -20°C, 
while the remaining aliquots will be stored in a -70°C freezer or in liquid nitrogen. All freezers are 
locked and located in secured areas. Transportation of samples to Doherty will be noted in the 
participant records in the study database. 
13.2 Peripheral Blood Mononucleocytes (PBMCs) storage and shipping
Blood samples collected in heparin tubes will be processed for recovery of PBMCs on the same day or 
within 24 hours of collection. The recovered cells will be aliquoted in to ~4 tubes. Each aliquot will be 
labelled and cryogenically stored in liquid nitrogen at the recruitment site until the end of each study 
year.
Aliquots will be shipped on dry ice to the Doherty site in Melbourne at the end of each study year. When 
received at the Doherty, they will be logged using the WHOCCRRI Centre database, and will be stored in 
liquid nitrogen. The cryogenic storage facility is in secured area. PI Fox has access to this facility. 
Transportation of samples to the Doherty will be noted in participant records in the study database. 
13.3 Respiratory swabs
Respiratory swabs will be tested for influenza at each hospital’s laboratory. The laboratories are all 
NATA accredited to provide diagnostic testing for influenza. Media from swabs that test positive for 
influenza virus will be aliquoted into two tubes and stored at -70C until shipping on dry ice to the 
Doherty on an ad-hoc basis.  Samples will be stored at 4°C if shipping is planned within 2-3 days of 
specimen collection; otherwise samples will be stored in -70°C freezers. Remaining influenza virus 
negative swabs/media will be stored at -70°C in on-site freezers.
Influenza positive samples received at the Doherty will be logged using the WHOCCRRI Centre 
database. On arrival, one aliquot will be inoculated into established cell lines to obtain influenza isolates 
for antigenic and genetic testing. Isolates will be assessed using antigenic assays such as the HI assay 
(see 11.1.2). Both original specimens and isolates may be genetically sequenced (note that viral RNA 
will be sequenced, not human DNA). Both the original specimen and any isolates recovered will be 
stored in -70°C freezers, according to NATA-approved SOPs at the WHOCCRRI. All freezers are locked 
and located in secured areas. Samples will be stored indefinitely using study numbers. 
13.4 Consent to biobanking of specimens
Samples will be stored indefinitely and may be retested as new technologies for understanding 
immunological responses to vaccination and infection become available. During the informed consent 
process participants will be asked if their samples can be stored for future studies. 
14 DATA ENTRY AND MANAGEMENT
14.1 Data security
14.1.1 Study database
A study database will be established and managed using the REDCap electronic data capture tool 
developed by Vanderbilt University and hosted at Melbourne Health. REDCap provides detailed 
information about the security measures they have taken to secure data storage 
https://projectredcap.org/software/mobile-app/. Briefly, data are transmitted using secure, encrypted 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
28

transmission (SSL/HTTPS). Additional validation checks are also used when communicating with the 
server. The REDCap Mobile App employs encryption-at-rest on the mobile device’s hard drive so that all 
important data and information stored on the device is properly protected from unauthorized or 
malicious users.
If, during the course of the study it is determined that other software may better meet the needs of the 
study, the team may migrate the study database to different software. 
14.1.2 Data entry
Data collected in questionnaire and generated from laboratory assays will be collected in paper form or 
electronically. HCWs will be able to enter responses to the baseline questionnaire directly using a 
mobile device. This will avoid the need for transcribing data from paper questionnaires and enable the 
use of validity checks to ensure that impossible or improbable entries are avoided. Paper 
questionnaires may also be used if there are technical issues at the study site. The site manager will 
enter the data into the study database. 
For symptoms diaries, HCWs will be sent a link to an online form to enter their information directly into 
the study database. 
14.2 Data storage
Hard copies of any information collected (e.g. informed consent) will be stored in locked filing cabinets 
in the site managers’ offices. Electronic copies of data collected will be stored using the study database.
Each participating study institution will store any paper study records (i.e., informed consent forms) in 
a physically secure location that is only accessible to authorized study staff. All research data will be 
stored for a minimum of 15 years after completion of the project.    
14.3 Participant identification and confidentiality
Each participant will be given a unique study ID which will be used on all study materials and 
specimens. Participant confidentiality is strictly held in trust by the participating investigators, research 
staff, and the sponsoring institution and their agents. This confidentiality is extended to cover testing of 
biological samples in addition to the clinical information relating to participants. The study protocol, 
documentation, data and all other information generated will be held in strict confidence. No 
information concerning the study or the data will be released to any unauthorized third party, without 
prior written approval of the sponsoring institution (University of Melbourne). Authorized 
representatives of the sponsoring institution may inspect all documents and records required to be 
maintained by the Investigator, including but not limited to, medical records (office, clinic or hospital). 
The clinical study site will permit access to such records. All laboratory specimens, evaluation forms, 
reports and other records that leave the site will be identified by the participant ID number only to 
maintain subject confidentiality. Clinical information will not be released without written permission of 
the subject, except as necessary for monitoring by HREC or regulatory agencies.
15 PARTICIPANT SAFETY AND WITHDRAWAL
15.1 Risks to participants
Study investigators and institutions are committed to protecting personal health information through 
the maintenance of privacy and security of each subject’s personal information in this study. To protect 
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29

confidentiality, we will use a study assigned number instead of personal information on study forms 
and we will store data in locked files and/or secured computers. If information from this study is 
presented publicly or published in a medical journal, results will be presented using aggregate 
statistics; individuals will not be identified by name or by any other personally identifiable information. 
Only specific and authorized researchers (e.g. site managers) in this study may see personal health 
information but will not disclose personally identifying information about individual participants to 
others. 
Collection of respiratory swabs is not typically associated with pain or discomfort. However, if the 
lower nostril is sore or irritated due to illness, rotating a swab in this area may cause minor and very 
brief discomfort. Collection of blood will be completed by trained healthcare staff and should only be 
associated with minor and brief discomfort associated with the insertion of the needle. Any 
complications related to collection of respiratory swabs and/or blood should be reported to the site 
manager, and will be investigated by study staff, including a medical doctor.  
15.2 Risk management and safety
15.2.1 Definitions
Adverse Event (AE): Any untoward medical occurrence in a patient enrolled into this study regardless 
of its causal relationship to study treatment.
Adverse events are classified as serious or non-serious.
Serious Adverse Event (SAE): An SAE is defined as any event that:

results in death; or

is immediately life threatening; or

requires inpatient hospitalisation; or

results in persistent or significant disability/incapacity.
Important medical events may be considered an SAE when, based upon appropriate medical judgement, 
they may jeopardize the participant and may require medical or surgical intervention to prevent one of 
the outcomes listed in this definition.
15.2.2 Eliciting adverse event information
Adverse events will be recorded from the time the HCW signs the Informed Consent Form until 7 days 
after each visit
15.2.3 Assessment and documentation of adverse events
The study coordinator is responsible for recording all adverse events, regardless of their relationship 
with the exposure, with the following exceptions:

Conditions that are present at screening and do not deteriorate will not be considered adverse 
events.

Abnormal laboratory values will not be considered adverse events unless deemed clinically 
significant by the investigator and documented as such.
Adverse events will be recorded using the Adverse Events Report Form, which will record:

A description of the AE;
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
The onset date, duration, date of resolution;

Severity (mild, moderate or severe);

Seriousness (i.e. is it an SAE?)

Any action taken (e.g. treatment, follow-up tests);

The outcome (e.g. recovery, death, continuing, worsening);

The likelihood of the relationship of the AE to the exposure being investigated (e.g. Unrelated, 
Possible, Probable, Definite).

Whether and when reported to the HREC
All AEs will be followed to adequate resolution.
15.2.4 Serious adverse event reporting
Any SAE occurring in a study participant will be reported to the Human Research Ethics Committee of 
the relevant hospital within 24 hours of the investigator becoming aware of the event, in accordance 
with the hospital policy. The SAE reporting form will be completed, signed and submitting by an 
investigator.
15.3 Participant withdrawals
HCWs may withdraw from the study at any time.  The site PIs or site managers may withdraw a HCW 
from the study if s/he is showing significant distress towards the blood draw procedures.  Information 
collected to that point will be kept unless the HCW explicitly requests otherwise.  This information will 
be used for comparison with HCWs who complete all study procedures. 
15.3.1 Follow up of withdrawn participants
All participants will be included in the study analyses, so it is important to have information on as many 
participants as possible.  If a participant wishes to withdraw from the study, every reasonable effort will 
be made to complete a final evaluation of participants who exit the study early and the reason(s) for 
withdrawals will be recorded in the participant’s study records. 
15.4 Replacements
Recruitment to the cohort will be open, with preferential recruitment of new vaccine-naïve and 
unvaccinated HCWs in years 2-4.
15.5 Incidental findings
As part of participation in the study, HCWs will submit respiratory swabs for influenza testing. Testing 
is usually done using a multiplex PCR respiratory virus panel. These panels include from 5 to 17 viruses, 
and include, at a minimum: influenza A, influenza B, and respiratory syncytial virus. HCWs will receive 
the results of their test via the pathology information system at each hospital. Conditions which require 
additional treatment (e.g. influenza) or booster vaccination (e.g. pertussis) will be treated as per 
standard policy at each hospital. The antiviral drug Oseltamivir is available to HCWs through state 
health department programs to protect HCWs.  
15.6 Vulnerable subjects
Pregnant women will not be excluded from the study. There are no special risks to pregnant women 
and they are identified as a target population for influenza vaccination in Australia and eligible for free 
vaccination under the National Immunisation Program 
(http://www.immunise.health.gov.au/internet/immunise/publishing.nsf/Content/Handbook10-
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home). However, because this study requires long follow-up, pregnant women may be ineligible if they 
cannot meet the follow-up requirements; e.g. if they take extended parental leave.
15.7 Potential benefits of the proposed research to participants and others
Participants will not personally benefit from participating in this study. This study may provide 
information that will be useful to local study sites and facilities which may improve operations and 
infection prevention and control for all HCP. For example, local sites will receive information on 
occupational groups at increased risk for influenza illness and information. This study will provide 
additional understanding of immune responses to influenza vaccination and add to the existing 
knowledge on antibody focusing and enhancement that influences vaccination responses. This study 
will update models of the potential benefits of HCW vaccination. This information is useful for re-
assessing influenza vaccination programs in hospitals.
16 RESOURCE SHARING PLAN
16.1 Data Sharing Plan 
We intend the share the data generated by this research in several ways. 
Publications: It is expected that at least 2-4 publications per aim will arise from this research. 
Publications will be formatted according the STROBE statement, where appropriate. We will target key 
journals in epidemiology, immunology and computational biology. 
Presentations at Scientific Meetings: We intend to share our findings at international meetings and 
symposia, and will look for opportunities to collaborate and share resources with other investigators. 
We will submit abstracts to both local meetings and to key research meetings for influenza, 
immunology and epidemiology, including, for example, Options for the Control of Influenza, the 
International Congress of Immunology, and the World Congress of Epidemiology.
Nested studies: Nested studies that utilize the cohort will be encouraged. Any nested studies proposed 
will be reviewed by the project steering committee and will of course require IRB approval. 
Sharing original data: The proposed study will collect demographic and clinical information, as well as 
blood and respiratory specimens from participants. Because we will be conducting longitudinal follow-
up, we will be collecting identifiable information. Any data shared will be stripped of identifiers prior to 
release for sharing. However, there remains the possibility of deductive disclosure of participants with 
unusual characteristics. Thus, data will only be shared with new collaborators under a data-sharing 
agreement that provides for: (1) a commitment to using the data only for research purposes and not to 
identify any individual participant; (2) a commitment to securing the data using appropriate computer 
technology; and (3) a commitment to destroying or returning the data after analyses are completed. 
Sharing of study protocols and SOPs: We will share our study protocols, SOPs, data collection tools and 
other study materials with other researchers/potential collaborators, upon request. The study protocol 
will be registered with https://clinicaltrials.gov/. 
Sharing of code: All code for statistical analyses and mathematical models will be developed in R. All 
code will be shared upon request. Code for the mathematical models behind the analysis in each paper 
will be published simultaneously on GitHub under a GPLv3 license. This allows anyone to reuse and 
modify the code as long as any changes are made publicly available. For some components of the 
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analysis, we will develop packages in R which will be downloadable from the Comprehensive R Archive 
Network, https://cran.r-project.org/. 
Study website: Information about the study, copies of any publications or presentations arising from 
the research and links to relevant external resources containing code will be shared via a study website. 
16.2 Genomic Data Sharing 
This study will not generate human genomic data. However, all virus sequencing data generated will be 
uploaded to the Global Initiative on Sharing All Influenza Data (GISAID) website, as part of standard 
surveillance practices of the WHO Collaborating Centre for Reference and Research on Influenza. 
HCW Cohort Protocol. Version 4.3, 4 March 2021 
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18  APPENDICES
Appendix A Screening form
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Appendix B Advertising materials
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Appendix C
Baseline questionnaire
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Appendix D Weekly symptom diary 
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Appendix E
Instructions for collection of respiratory swabs
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Appendix F
Template for antibody results for participants
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Appendix G Participant cards
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