The Molecular Epidemiology, Evolution and Phylogeography of Various Pathogens of Public Health Significance

The Molecular Epidemiology, Evolution and

Phylogeography of Viral Pathogens of Public

Health Significance

Dillon Charles Adam

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

The Kirby Institute

Faculty of Medicine

2020

Thesis/Dissertation Sheet

Surname/Family Name : Adam Given Name/s : Dillon Charles Abbreviation for degree : PhD Faculty : Medicine School : Kirby Institute The Molecular Epidemiology, Evolution and Phylogeography Thesis Title : of Viral Pathogens of Public Health Significance

Thesis Abstract

Infectious diseases represent a substantial and growing burden on global public health. Bayesian phylogenetic methods have become an increasingly popular approach to study the molecular epidemiology and dynamics of infectious pathogens, particularly viruses, and their use can supplement conventional public health approaches in epidemiology to aid decision makers during epidemics. Examples include estimating the location and time of epidemic origins, approximating epidemic curves, and uncovering transmission patterns and drivers of pathogen spread within and between populations. The aim of this thesis is to utilize Bayesian phylogenetic methods to study the molecular epidemiology and population-level transmission dynamics of various pathogens of public health significance. In Chapter Two, Bayesian phylogenetic reconstruction methods are used to test for significant demographic and clinical associations within and between Rhinovirus species and subtypes that are potentially concealed in traditional epidemiology studies. In Chapter Three, various phylogeography models are used to combine commonly sequenced Variola virus (smallpox) genes to rapidly characterise the pathogenicity and global transmission patterns of historic isolates, while evaluating the representativeness of said models in-lieu of whole genome sequences. In Chapters Four and Five, the molecular epidemiology and national and regional transmission patterns and drivers of Influenza A H3N2 and H1N1pdm09 are uncovered in Australia and New Zealand during a single influenza season in 2017, and from 2009 to 2017 in India respectively. In Chapter Four, the effectiveness and representativeness of various subsampling strategies are additionally evaluated against their effect on the results and potential for misguiding public health decision makers while in Chapter Five the impact of potential sampling bias is discussed. The final chapter discusses the impact and limitations of these results and Bayesian phylogenetic methods generally for the consideration of public health decision makers, whilst also discussing opportunities for improvement. Overall these results can be used to inform future epidemic prevention, investigation and outbreak control activities, complementing traditional approaches in public health from surveillance to action.

Declaration relating to disposition of project thesis/dissertation

I hereby grant to the University of New South Wales or its agents a non-exclusive licence to archive and to make available (including to members of the public) my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known. I acknowledge that I retain all intellectual property rights which subsist in my thesis or dissertation, such as copyright and patent rights, subject to applicable law. I also retain the right to use all or part of my thesis or dissertation in future works (such as articles or books).

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The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years can be made when submitting the final copies of your thesis to the UNSW Library. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research.

Publications Statement

UNSW is supportive of candidates publishing their research results during their candidature as detailed in the UNSW Thesis Examination Procedure.

Publications can be used in their thesis in lieu of a Chapter if:

• The candidate contributed greater than 50% of the content in the publication and is the “primary author”, i.e. the candidate was responsible primarily for the planning, execution and preparation of the work for publication. • The candidate has approval to include the publication in their thesis in lieu of a Chapter from their supervisor and Postgraduate Coordinator. • The publication is not subject to any obligations or contractual agreements with a third party that would constrain its inclusion in the thesis.

This thesis has publications (either published or submitted for publication) incorporated into it in lieu of a chapter and the details are presented below:

CANDIDATE’S DECLARATION I declare that: • I have complied with the UNSW Thesis Examination Procedure • Where I have used a publication in lieu of a Chapter, the listed publication(s) below meet(s) the requirements to be included in the thesis.

Candidate’s Name Signature Date (dd/mm/yy)

POSTGRADUATE COORDINATOR’S DECLARATION

I declare that:

• the information below is accurate • where listed publication(s) have been used in lieu of Chapter(s), their use complies with the UNSW Thesis Examination Procedure • the minimum requirements for the format of the thesis have been met. PGC’s Name PGC’s Signature Date (dd/mm/yy)

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Details of publication #1: Full title: The Molecular Epidemiology and Clinical Phylogenetics of Rhinovirus Infection among Paediatric Cases in Sydney, Australia. Authors: Adam DC; Scotch M; MacIntyre CR Journal or book name: Influenza and Other Respiratory Viruses Volume/page numbers: TBA Date accepted/ published: TBA Accepted and in In progress Status Published X press (submitted) The Candidate’s Contribution to the Work Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing-Original Draft Preparation, Writing – Review & Editing Location of the work in the thesis and/or how the work is incorporated in the thesis: This is Chapter Two of the Thesis. It establishes the use of basic Bayesian phylogenetic methods to study the molecular epidemiology of Rhinoviruses. Further chapters build on the basic methods used in this chapter by implementing progressively advanced modelling methods. Primary Supervisor’s Declaration I declare that: • the information above is accurate • this has been discussed with the PGC and it is agreed that this publication can be included in this thesis in lieu of a Chapter • all of the co-authors of the publication have reviewed the above information and have agreed to its veracity by signing a ‘Co-Author Authorisation’ form. Primary Supervisor’s name Primary Supervisor’s signature Date (dd/mm/yy)

Details of publication #2: Full title: Bayesian Phylogeography and Pathogenic Characterisation of Smallpox based on HA, ATI and CrmB genes Authors: Adam DC; Scotch M; MacIntyre CR Journal or book name: Molecular Biology and Evolution Volume/page numbers: 35(11):2607-17 Date accepted/ published: 7 August 2018 Accepted and in In progress Status Published X press (submitted) The Candidate’s Contribution to the Work Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing-Original Draft Preparation, Writing – Review & Editing Location of the work in the thesis and/or how the work is incorporated in the thesis: This is Chapter Three of the Thesis. It establishes the use of discrete-trait Bayesian phylogeography methods to study the transmission history of variola virus (smallpox). The discrete-trait approach is replicated in subsequent chapters with additional levels of complexity. Primary Supervisor’s Declaration I declare that: • the information above is accurate • this has been discussed with the PGC and it is agreed that this publication can be included in this thesis in lieu of a Chapter • all of the co-authors of the publication have reviewed the above information and have agreed to its veracity by signing a ‘Co-Author Authorisation’ form. Primary Supervisor’s name Primary Supervisor’s signature Date (dd/mm/yy)

Details of publication #3: Full title: The Phylogeography of Seasonal Influenza A/H3N2 within and between Australia and New Zealand Authors: Adam DC; Moa A; Costantino V; Scotch M; MacIntyre CR Journal or book name: Journal of Infectious Diseases Volume/page numbers: TBA Accepted and in In progress Status Published X press (submitted) The Candidate’s Contribution to the Work Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing-Original Draft Preparation, Writing – Review & Editing Location of the work in the thesis and/or how the work is incorporated in the thesis: This is Chapter Four of the Thesis. It replicates the used of discrete-trait phylogeography introduced in Chapter Tree to study the transmission patterns and drivers of influenza A/H3N2 in Australia and New Zealand. This chapter also introduces Generalised Linear Modelling methods to simultaneously measure the effect of demographic and ecological factors associated with transmission. Primary Supervisor’s Declaration I declare that: • the information above is accurate • this has been discussed with the PGC and it is agreed that this publication can be included in this thesis in lieu of a Chapter • all of the co-authors of the publication have reviewed the above information and have agreed to its veracity by signing a ‘Co-Author Authorisation’ form. Primary Supervisor’s name Primary Supervisor’s signature Date (dd/mm/yy)

Details of publication #4: Full title: Phylodynamics of Influenza A/H1N1pdm09 in India Reveals Circulation Patterns and Increased Selection for High Mortality Mutants Authors: Adam DC; Scotch M; MacIntyre CR Journal or book name: Viruses Volume/page numbers: 11(9): 791 Date accepted/ published: 27 August 2019 Accepted and in In progress Status Published X press (submitted) The Candidate’s Contribution to the Work Data curation, Formal Analysis, Investigation, Methodology, Software, Visualisation, Writing-Original Draft Preparation, Writing – Review & Editing Location of the work in the thesis and/or how the work is incorporated in the thesis: This is Chapter Five of the Thesis. It replicates the use of discrete-trait Bayesian phylogeography methods and Generalised Linear Modelling methods used in Chapter Four to study the complete transmission history of influenza A/H1N1pdm09 in India. This chapter also introduces Bayesian coalescent phylodynamic modelling to infer the past viral population dynamics of A/H1N1pdm09. Primary Supervisor’s Declaration I declare that: • the information above is accurate • this has been discussed with the PGC and it is agreed that this publication can be included in this thesis in lieu of a Chapter • all of the co-authors of the publication have reviewed the above information and have agreed to its veracity by signing a ‘Co-Author Authorisation’ form. Primary Supervisor’s name Primary Supervisor’s signature Date (dd/mm/yy)

I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral thesis only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted, I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.

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I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.

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Originality Statement

I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.

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I hereby certify that all co-authors of the published or submitted papers agree to Dillon Charles Adam submitting those papers as part of his Doctoral Thesis.

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Acknowledgements

First and foremost, I would like to gratefully acknowledge my two supervisors Professor Raina MacIntyre and Associate Professor Matthew Scotch for their constant support and guidance. I am particularly indebted to Raina for first bringing me into academia, and for her professional and personal mentorship.

I would like to acknowledge the National Health & Medical Research Council of Australia for providing me with a scholarship and funding to complete this thesis. I also received funding support from both Raina and Matthew for which I am especially grateful.

I would also like to thank all the staff and students from the School of Public Health and the Kirby Institute. I am particularly grateful to Aye Moa and Abrar Chughtai, and my colleagues from the Biosecurity Program for their academic support and friendship.

Lastly, I would like to thank my family, my friends and my partner, for their constant support both before and during the completion of this thesis. And finally, to my parents; thank you for encouraging me to pursue my interest in science from a young age, and for accepting me for who I am. Without your love and support, this thesis would not exist.

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List of Abbreviations

ATI A-Type Inclusion protein

BEAST Bayesian Evolutionary Analysis by Sampling Trees

BF Bayes Factor

BSSVS Bayesian Stochastic Search Variable Selection

CDC Centers for Disease Control and Prevention

CFR Case Fatality Ratio

CrmB Cytokine response modifier B

CTA Continuous-Trait Analysis

CTMC Continuous-Time Markov Chain

DTA Discrete-Trait Analysis

GISAID Global Inititive for Sharing All Influenza Data

GLM Generalised Linear Model

GTR General-Time Reversable

HA Haemagglutinin

HKY Hasegawa, Kishino and Yano

MCC Maximum-Clade Credibility

MCMC Markov Chain Monte Carlo

MRCA Most Recent Common Ancestor

NA Neuraminidase

PS Path Sampling

SSS Stepping-Stone Sampling

VARV Variola Virus

WHO World Health Organisation

List of Figures

Figure 1.1: Guide to thesis diagram grouping chapters and pathogens by methodology all within a Bayesian phylogenetics framework.

Figure 1.2: Rooted phylogenetic tree representing the inferred evolutionary relationship of 10 hypothetical taxa (T1 – T10).

Figure 1.3: Illustrative example demonstrating that as each step (i) of the Markov Chain moves through posterior space over time, an approximation of the target distribution of a figurative parameter is observed as the frequency of sample estimates within x.

Figure 1.4: Example of sufficiently mixed (left) and insufficiently mixed (right) MCMC trace for a figurative parameter.

Figure 1.5: Hypothetical example of a time-scaled phylogenetic tree containing 40 sequence taxa where internal nodes represent divergence times along the x- axis.

Figure 1.6: Hypothetical example of asymmetric and symmetric instantaneous rate matrices used in discrete-trait Bayesian phylogeography models to estimate spatial transmission across a phylogeny.

Figure 2.1: Unrooted radial phylogenetic tree of RV-A and RV-C species and subtype coloured by age group with branch lengths equally constrained.

Figure 3.1: Time-rooted phylogenetic characterisation of whole genome and single-locus models of HA, ATI and CrmB genes using identical taxonomic datasets.

Figure 3.2: Time-rooted phylogenetic characterisation of multi-locus models of HA, ATI and CrmB genes using identical taxonomic datasets extracted from isolates sequenced as WG.

Figure 3.3: Time-rooted phylogenetic and phylogeographic characterisation of VARV isolates.

Figure 3.4: Projection between eight discrete regions using Bayesian phylogeography coloured by sampling time.

Figure 4.1: Phylogenetic characterised of A/H3N2 clades circulating within Australia and New Zealand in 2017 by month of isolation.

Figure 4.2: Phylogeographic projection of 1,110 A/H3N2 taxa sequenced for HA and NA in 2017 from Australia and New Zealand.

Figure 5.1: (a) Bayesian Skygrid estimation of effective viral population size

(Ne) of A/H1N1pdm09 in India between 2009 and 2017. (b) Official A/H1N1pdm09 case and death counts (left axis) reported by the National Centre for Disease Control in Delhi.

Figure 5.2.: Relative structural locations of A/H1N1pdm09 residues within the HA monomer under positive selection in India.

Figure 5.3: Phylogeography of definitive A/H1N1pdm09 transmission between S/UT in India since 2009.

List of Tables

Table 2.1: Distribution of species and genotypes detected by species among 52 paediatric RV cases in Sydney, Australia between 2006 and 2009.

Table 2.2: Comparison of clinical symptoms of 28 paediatric RV cases in Sydney, Australia by RV species.

Table 2.3: Bayesian Posterior Phylogenetic Parsimony Score (PS) by genome region, case demographic and clinical symptoms.

Table 2.4: Bayesian Posterior Phylogenetic Association Index (AI) by genome region, case demographic and clinical symptoms.

Table 3.1: Ranked support out of a possible 56 asymmetric routes for VARV transmission between eight discrete regions from 1654 to 1977 by modelled loci compared to whole genomes.

Table 3.2: Representativeness and ranked support out of a possible 56 asymmetric routes of VARV transmission between eight discrete regions by taxon dataset.

Table 3.3: Count of taxa included for analysis by aggregate region and time period.

Table 4.1: Distribution of A/H3N2 sequences collected in 2017 in Australia and New Zealand by month and location of isolation.

Table 4.2: Phylogenetic characterisation of influenza A/H3N2 clades circulating in Australia and New Zealand by state, territory and major island of isolation.

Table 4.3: A/H3N2 transmission in 2017 between and within Australian and New Zealand by subsampling method and ranked by statistical support.

Table 4.4: Comparison of conditional effects of factors on A/H3N2 transmission within and between nine Australia and New Zealand States, Territories and Island in 2017 by subsampled dataset.

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Table 5.1: Haemagglutinin (HA) sequence dataset by year and State and Union Territories of India included for analysis.

Table 5.2: dN/dS ratios of codon sites identified in India under pervasive positive selection relative to dN/dS ratios of two distinct international samples.

Table 5.3: Predictors of A/H1N1pdm09 transmission in India between 14 States and Union Territories from 2009 to 2017.

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List of Publications

Below are a list of research publications arising from this thesis:

Adam DC, Scotch M, MacIntyre CR. Bayesian Phylogeography and Pathogenic Characterization of Smallpox Based on HA, ATI, and CrmB Genes. Molecular biology and evolution. 2018 Nov 1;35(11):2607-17.

Adam DC, Scotch M, MacIntyre CR. Phylodynamics of Influenza A/H1N1pdm09 in India Reveals Circulation Patterns and Increased Selection for Clade 6b Residues and Other High Mortality Mutants. Viruses. 2019 Sep;11(9):791.

The following chapters in this thesis have been submitted for publication:

Chapter Two: Adam DC, Scotch M, MacIntyre CR. Molecular epidemiology and clinical phylogenetics of rhinovirus infection among paediatric cases in Sydney, Australia.

Chapter Four: Adam DC, Moa A, Costantino V, Scotch M, MacIntyre CR The Phylogeography of Seasonal Influenza A/H3N2 within and between Australia and New Zealand.

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Table of Contents

Declarations ...... vi

Acknowledgements ...... viii

List of Abbreviations ...... ix

List of Figures ...... x

List of Tables ...... xii

List of Publications ...... xiv

Table of Contents ...... xv

1. Thesis Introduction ...... 1

1.1 Background & Guide to Thesis ...... 2

Part A: Pathogens of Public Health Significance ...... 4

1.2 Rhinoviruses ...... 4

1.2.1 Evolution, Genome & Taxonomy ...... 4

1.2.2 Epidemiology & Burden ...... 5

1.3 Seasonal Influenza ...... 9

1.3.1 Genome & Host Range ...... 9

1.3.2 Emergence in Humans ...... 11

1.3.3 Epidemiology, Prevention & Circulation ...... 13

1.4 Category A Bioterrorism Agents – Variola ...... 16

1.4.1 The History of Smallpox & Global Eradication ...... 16

1.4.2 Post-Eradication and the Threat of Bioterrorism ...... 18

Part B: Phylogenetic Methods & Applications ...... 20

1.5 Phylogenetic Tree Fundamentals ...... 21

1.5.1 Sequence Alignment ...... 23

1.5.2 Constructing Phylogenetic Trees – Distance Methods ...... 24

1.5.3 Constructing Phylogenetic Trees – Character Methods ...... 25

1.5.4 Assessing Topological Confidence ...... 27

1.6 Bayesian Phylogenetics ...... 28

1.6.1 Markov chain Monte Carlo (MCMC) ...... 29

1.6.2 Model Selection ...... 32

1.6.3 Software ...... 33

1.7 Models & Methods of Data Integration in BEAST ...... 34

1.7.1 Molecular Clocks & Time-Scaled Phylogenetic Trees ...... 34

1.7.2 Phylodynamics & Coalescent Theory ...... 37

1.7.3 Trait Association & Phylogeography ...... 39

1.7.4 Generalised Linear Modelling ...... 43

1.8 Applications to Public Health & Thesis Aims ...... 44

2. The Molecular Epidemiology & Clinical Phylogenetics of Rhinovirus

Infection among Paediatric Cases in Sydney, Australia ...... 47

2.1 Chapter Abstract ...... 48

2.2 Introduction ...... 49

2.3 Materials & Methods ...... 50

2.3.1 Compilation of Dataset & Sequencing ...... 50

2.3.2 Characterisation & Epidemiology ...... 51

2.3.3 Phylogeny-Trait Association ...... 52

2.4 Results ...... 53

2.4.1 Sequence characterisation ...... 53

2.4.2 Clinical and Demographic Epidemiology ...... 54

2.4.3 Phylogenetic Analysis ...... 55

2.5 Discussion ...... 59

2.6 Conclusion ...... 62

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3. Bayesian Phylogeography and Pathogenic Characterisation of Smallpox based on HA, ATI and CrmB genes ...... 63

3.1 Chapter Abstract ...... 64

3.2 Introduction ...... 65

3.3 Materials & Methods ...... 67

3.3.1 Compilation of Sequence Dataset ...... 67

3.3.2 Phylogenetic Characterisation ...... 68

3.3.3 Phylogeography of Variola Virus ...... 69

3.4 Results ...... 70

3.5 Discussion ...... 78

3.6 Conclusion ...... 84

4. The Phylogeography of Seasonal Influenza A/H3N2 within and between

Australia and New Zealand ...... 87

4.1 Chapter Abstract ...... 88

4.2 Introduction ...... 89

4.3 Methods ...... 90

4.3.1 Compilation of Sequence Dataset & Phylogenetic Characterisation 90

4.3.2 Discrete-Trait Phylogeography ...... 92

4.3.3 Generalised Linear Modelling ...... 94

4.4 Results ...... 95

4.4.1 Differences & Similarities in A/H3N2 Clade Composition ...... 95

4.4.2 Time-Calibrated Phylogenetic Analysis ...... 99

4.4.3 Phylogeography within and between Australia and New Zealand 100

4.4.4 Predictors of A/H3N2 Transmission ...... 103

4.5 Discussion ...... 104

4.6 Conclusion ...... 110

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5. Phylodynamics of Influenza A/H1N1pdm09 in India Reveals Circulation

Patterns and Increased Selection for High Mortality Mutants ...... 111

5.1 Chapter Abstract ...... 112

5.2 Introduction ...... 113

5.3 Materials & Methods ...... 115

5.3.1 Compilation of Sequence Datasets ...... 115

5.3.2 A/H1N1pdm09 Transmission within India ...... 118

5.3.3 Predictors of Transmission within India ...... 119

5.3.4 Positive Selection Analysis ...... 120

5.4 Results ...... 121

5.4.1 CFR & Viral Population Demographics ...... 121

5.4.2 dN/dS Selection Analysis and Amino Acid Variations ...... 122

5.4.3 Phylogeography of A/H1N1pdm09 ...... 124

5.4.3 Generalised Linear Modelling ...... 125

5.5 Discussion ...... 126

5.6 Conclusion ...... 135

6. Thesis Conclusion ...... 137

6.1 Thesis Overview, Key Findings & Strengths ...... 138

6.2 Limitations of Methods ...... 142

6.3 Conclusion & Recommendations ...... 146

Bibliography ...... 150

Appendix A: Supplementary Results ...... 184

Appendix B: A/H3N2 Acknowledgment Table ...... 225

Appendix C: A/H1N1pdm09 Acknowledgment Table ...... 240

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Chapter One

Thesis Introduction

1.1 Background & Guide to Thesis

Infectious diseases continue to represent a significant burden of disease globally, responsible for an estimated 27.85% of all premature death and disability in 2017, second only to all non-communicable diseases combined

(Roser and Ritchie 2020). Furthermore, emerging, re-emerging and potentially pandemic infectious diseases remain a growing and persistent threat to global public health. Recent examples include SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), Ebola, Zika and the current and ongoing COVID-19 (Coronavirus disease 2019) outbreak.

The use of molecular analyses that include phylogenetic methods and advanced Bayesian modelling is a growing area of research interest in the field of infectious disease epidemiology and is increasingly positioned to support traditional public health approaches from routine surveillance to outbreak investigation, response and control. Examples of seminal research in this field include a retrospective analysis of the 2014-15 Ebola outbreak in West Africa where the integration of epidemiological data and Bayesian phylogenetic models revealed key international and domestic transmission events that significantly contributed to the overall magnitude and length the epidemic

(Dudas et al. 2017). Other studies have used Bayesian phylogenetic methods to identify the history of Zika virus emergence and spread across the Asian and

African continents that preceded and subsequently ignited the 2015-16 global epidemic that began in South America (Faye et al. 2014). As sequencing technology becomes gradually ubiquitous and affordable, the use of phylogenetic methods in the field of public health is expected to grow exponentially (Gardy and Loman 2018).

This thesis utilizes advanced Bayesian phylogenetic methods to study the epidemiology, evolution and phylogeography of various pathogens of public health significance to uncover clinical patterns of disease and inform epidemic response and control activities. In Part A of Chapter One, these pathogens are introduced, while in Part B, the methods utilised in their analysis are described.

Subsequent Chapters Two – Five present the original research findings of this thesis, and in Chapter Six, a summary of this theses’ contributions to the field, a discussion of limitations and future recommendations is offered. Figure 1.1 presents a graphical overview of these Chapters grouping the original research by the methods used and introduced in Part B of Chapter One.

Figure 1.1: Guide to thesis diagram grouping chapters and pathogens by methodology all within a Bayesian phylogenetics framework.

Part A: Pathogens of Public Health Significance

1.2 Rhinoviruses

Rhinoviruses are a diverse group of positive-sense, single stranded RNA

(+ssRNA) viruses currently consisting of 179 classified subtypes within three recognised species, RV-A, RV-B, and RV-C (King 2011). The high prevalence and historically benign clinical manifestations associated with rhinoviruses have defined their reputation as pathogens of little public health significance.

As such, rhinoviruses remain understudied compared to other respiratory pathogens such as influenza. Rhinoviruses however represent the most frequent aetiological agent of “the common cold”, and therefore represents the most common cause of all acute respiratory tract infections across all age groups throughout the year (Mäkelä et al. 1998; Monto 2002). Other aetiological agents of the common cold typically, although less significantly, include adenoviruses and coronaviruses among many others.

1.2.1 Evolution, Genome & Taxonomy

Rhinoviruses are formally classified within the Enterovirus genus and

Picornaviridae family (Carstens and Ball 2009; Fields et al. 2013) and are genetically similar to other enteroviruses such as poliovirus type 1 (PV-1) despite remarkably different clinical manifestations (Kitamura et al. 1981;

Stanway et al. 1984). Similar to other viruses within the Picornaviridae family, rhinovirus genomes (~7kb) encode a single open reading frame (ORF) beginning with a 5’ untranslatable region (5’UTR) containing a conserved cloverleaf motif and internal ribosome entry site (IRES) required for translation (Palmenberg et al. 2009). Translation produces a single polyprotein which is cleaved to form mature capsid and replication peptides (Palmenberg et al. 2010). Established

nomenclature is consistent with other enteroviruses: numeric subtypes are suffixed to species names. For example, subtype 16 within species A reads as

RV-A16 (McIntyre et al. 2013; Palmenberg and Gern 2015). The original classification system based on antigenic serology has now been replaced with a sequence-based system of comparative similarity, where new isolates are tentatively typed via comparison to either VP1 or VP4/VP2 capsid coding protein regions (McIntyre, et al. 2013; Palmenberg and Gern 2015). The 5’UTR region is typically sequenced for rapid diagnosis (Deffernez et al. 2004), however classification on the basis of this region has demonstrated incongruent typing when compared to VP1 or VP4/VP2 (Kiang et al. 2008; Savolainen-Kopra et al. 2009; Ratnamohan et al. 2016) due to inter and intra-species recombination within this region (Waman et al. 2014). In 2006, the transition to a sequence- based system of subtyping lead to the discovery and recognition of RV-C as a distinct species with less than 60% identity to either RV-A or RV-B (Lamson et al. 2006; Lau et al. 2007; McErlean et al. 2007; Renwick et al. 2007; Briese et al.

2008; Dominguez et al. 2008). Since then significant efforts have been made to understand the differential burden of disease between rhinovirus species.

1.2.2 Epidemiology & Burden

Rhinoviruses are a major cause of all acute respiratory illnesses throughout the year (Monto 2002). Rhinoviruses circulate freely around the world with no geographic structure of either species or type (McIntyre, et al.

2013) suggesting spatial structures have little impact on virus selection.

Transmission is person-to-person via direct contact (including fomites), aerosol, and inoculation via intranasal and conjunctival routes (Jennings and Dick 1987;

Hendley and Gwaltney 1988). The virus is not transmitted via the faecal-oral route like most other enteroviruses which invade the enteric system (e.g. PV-1).

The attributable burden of disease accounts for approximately 34.4 disability adjusted life years (DALY) per 1000 population among children under five

(Weiss and Sullivan 2001; Bertino 2002; Gaunt et al. 2011), while the socioeconomic burden has been estimated to cost approximately $60B/year in the

United States alone (Fendrick et al. 2003). Despite this, there is currently no vaccine available to prevent rhinovirus infection.

It has been now well established that infection with rhinoviruses can compromise both upper and lower respiratory tracts, in contrast with previous dogma that rhinoviruses were acute respiratory pathogens that solely invaded the upper respiratory tract (Papadopoulos et al. 2000). Asymptomatic infection is observed, however typical symptoms of upper respiratory infection include rhinorrhoea, nasal congestion, cough, headache and malaise (Peltola et al. 2008;

Fields, et al. 2013). Low grade fever is common among children but less so in adults. Lower respiratory infection can lead to increasingly severe clinical manifestations in both children and adults. For example, in children under five years of age, rhinoviruses are frequently associated with pneumonia and severe bronchiolitis, second only to respiratory syncytial virus (RSV) (Kellner et al.

1988; Hayden 2004). Lower respiratory infection among immunocompromised individuals has also been associated with severe pneumonia and even death.

Exacerbations of asthma and other pre-existing diseases such as chronic obstructive pulmonary disease and cystic fibrosis in both adults and children has also been observed in cases of lower respiratory infection with rhinoviruses

(Nicholson et al. 1993; Varkey and Varkey 2008), with exposure early in life a significant risk factor for the onset of asthma (Jackson et al. 2008; Jartti and Gern

2017).

There is increasing evidence to suggest RV-A and RV-C may be associated with more severe clinical outcomes compared to RV-B strains. Result from a birth cohort study published in 2012 found RV-A and RV-C were significantly associated (odds ratio 8.2 and 7.6 respectively) with moderate to severe respiratory illnesses compared to RV-B (Lee et al. 2012). Despite seasonal prevalence occurring in autumn and spring, disease severity was also higher in winter compared to summer. Other studies have also shown that RV-C is detected at greater frequencies among children hospitalised with acute respiratory tract infections compared with RV-A and RV-B (Jin et al. 2009;

Piralla et al. 2009; Lauinger et al. 2013), while among adults, RV-A has been associated with increased disease severity compared with RV-B and RV-C

(Chen et al. 2015). Furthermore, both RV-C and RV-A species have been associated with an increased risk of first asthmatic wheezing episodes among children (Turunen et al. 2016) as well as an increased risk of asthma recurrence

(Turunen et al. 2017). Other studies however have provided contrasting results.

For example, despite demonstrating a similar increase in the frequency of RV-C detection compared to RV-A and RV-B, one study failed to demonstrate statistical associations between RV-C and disease severity in a large hospital- based sample (Huang et al. 2009). Evidence of associations between species and severity have also broken down when studies have focused on outpatients rather than hospital inpatients (Moreira et al. 2011). Beyond molecular characterisations of severity based on species classifications, efforts have been made to examine the clinical significance of various RV subtypes, again with inconsistent results. A 2015 study provided some statistical evidence that RV-A genotypes 1, 6, 7 and 9, as well as RV-C genotypes 1-6 were significantly associated with lower respiratory tract infections among children presenting to the emergency room under three years of age (Martin et al. 2015). A similar

hospital-based study however found no association between clinical outcomes and rhinovirus subtypes, instead suggesting host-specific factors play a primary role in predicting severity (Bruning et al. 2015).

Reasons for species/subtype specific virulence of rhinoviruses is not yet well understood however some hypotheses have shown promise. For example, a comparable and highly variable spacer sequence within the 5’UTR region of

PV-1 has previously been implicated in neuro-virulence (Martin et al. 1996). The same highly variable spacer sequence has also been observed within rhinovirus genomes and may therefore be predictive of pathogenesis by subtype or species if functionally analogous to that of PV-1 (Palmenberg, et al. 2009). Furthermore, frequent error-prone replication during single host infection, known as quasi- speciation, has also been shown to contribute to increased neuro-virulence among PV-1 (Vignuzzi et al. 2006). It is currently unknown if this quasi- speciation occurs significantly during rhinovirus infection, however as an RNA virus, similar error-prone replication is typical and might therefore be expected

(Palmenberg, et al. 2010). How and if these specific genetic factors interact within hosts and contribute to disease severity has yet to be studied in any depth. There is a clear need for further research in understanding the role rhinovirus species and subtypes play in disease severity.

1.3 Seasonal Influenza

Seasonal influenza viruses are negative-sense, single stranded RNA (- ssRNA) viruses of the Orthomyxoviridae family (Webster et al. 1982; Webster et al. 1992) and represent a major and persistent burden of disease globally. There are currently four classified species of influenza designated types A, B, C and D, however types A and B are the predominate cause of seasonal influenza burden and disease among humans. Influenza C is rare and causes only mild respiratory disease such that is does not represent a significant pathogen of public health importance, while influenza D, predominantly affects cattle and pigs. No human cases of influenza D have ever been observed and reported.

Seasonal human influenza A viruses are further subdivided into two distinct subtypes: A H3N2 (A/H3N2) and A H1N1pdm09 (A/H1N1pdm09) determined by the immunogenic response towards the two surface glycoproteins haemagglutinin (HA) and neuraminidase (NA). In contrast, seasonal influenza

B viruses are subdivided into two distinct genetic lineages that define their classification: B Yamagata (B/Yam) and B Victoria (B/Vic). Influenza A/H3N2 is most often associated with increased disease severity relative to A/H1N1pdm09 and, but not always, B/Yamagata and B/Victoria (Kaji et al. 2003; Hayward et al.

2014; Su et al. 2014). Among human seasonal influenza viruses, this thesis focuses exclusively on influenza A which will therefore be the primary focus of the coming sections unless explicitly stated.

1.3.1 Genome & Host Range

The RNA genome of influenza consists of eight segmented genes coding for numerous structural, functional and accessory proteins (Webster, et al. 1982;

Webster, et al. 1992; Hutchinson et al. 2014). The ten primary proteins of influenza include the two surface membrane glycoproteins HA and NA,

nucleoprotein (NP), matrix proteins 1 and 2 (M1, M2), non-structural proteins 1 and 2 (NS1, NS2 or NEP). Additionally, polymerase basic 1 and 2 (PB1, PB2) and polymerase acidic protein (PA) form an RNA dependent-RNA-polymerase complex essential for viral replication (Webster, et al. 1982; Webster, et al. 1992).

Since 2001, a number of novel accessory proteins have been discovered including PB1-F2 (Chen et al. 2001), PB1-N40 (Wise et al. 2009), PA-X (Jagger et al. 2012), PA-N155 and PA-N182 (Muramoto et al. 2013), M42 (Wise et al. 2012), and NS3 (Selman et al. 2012). Additional proteins have also been predicted in silico (Vasin et al. 2014).

The error-prone RNA-polymerase complex is largely responsible for the constant and rapid evolution of influenza A viruses, which is termed antigenic drift. Genetic reassortment of gene segments between subtypes, termed antigenic shift, is another significant factor responsible for the evolution and emergence of influenza A viruses in humans. Of the 18 currently classified HA subtypes, and 11 NA subtypes, most predominately circulate among wild water and shorebird species such as Anseriformes and Charadriiformes which are considered the natural reservoir hosts of most influenza subtypes (Webster, et al. 1992; Olsen et al. 2006). Phylogenetically, HA subtypes cluster into two distinct groups: 1 and 2 (Gamblin and Skehel 2010).

The cellular binding specificity of the surface HA protein partially determines the host range of influenza A (Rogers and Paulson 1983; Paulson and Rogers 1987). For example, human seasonal influenza A virus A/H3N2 and

A/H1N1pdm09 HA are adapted to α2,6-linked sialic acid (SA) receptors located on epithelial cells of the upper respiratory tract, while avian adapted influenza

A species such as H5N1 and H7N9 are α2,3-linked SA adapted (Gambaryan et al. 1997; Russell et al. 2006). The binding-specificity of HA largely prevents

cross-species transmission of influenza A viruses; however, these zoonotic spill- over events between animals and humans do occur, and sometimes, ignite human pandemics. Humans, swine and equine species are the three major animal hosts of influenza A viruses however circulation in dogs, seals and bats have been detected. Bats currently represent the only known reservoir of H17 and H18 (Reperant et al. 2012; Tong et al. 2013; Wu et al. 2014).

1.3.2 Emergence in Humans

All current seasonally circulating influenza A viruses, first emerged in human populations as pandemics following the partial reassortment of influenza A virus genes from animal reservoirs such as avian and swine species, and human influenza A subtypes. There have been four documented human influenza A pandemics roughly within the last century, together, responsible for an estimated 50 million deaths (Cox and Subbarao 2000; Oxford 2000). In

1918, a novel A/H1N1 virus, termed the ‘Spanish Flu’, and believed to have emerged following a complex reassortment between avian, swine, and human influenza virus subtypes caused an estimated 40 million deaths (Shope 1936;

Johnson and Mueller 2002; Taubenberger et al. 2005). This novel virus subsequently circulated seasonally in humans every year until 1957 when another novel influenza A virus emerged: A/H2N2 (Schäffr et al. 1993). This virus, an avian-human reassortment termed ‘Asian Flu’ ignited another global pandemic and entirely replaced the seasonal A/H1N1 ‘Spanish Flu’ within the year (Cox and Subbarao 2000). The current seasonal A/H3N2 virus first emerged soon after in 1968 causing another global pandemic following the genetic reassortment of avian H3 and human N2 subtypes of the then seasonally circulating human ‘Asian Flu’ (Gething et al. 1980), which was again entirely replaced and eliminated from the human population. Termed ‘Hong Kong Flu’,

because it was first isolated in Hong Kong, this virus killed an estimated

1,000,000 people (Viboud et al. 2005). Unlike past pandemics, the re-emergence of A/H1N1 in 1977 failed to replace the then and currently seasonally circulating

A/H3N2 ‘Hong Kong Flu’, which together co-circulated until 2009 when a novel

A/H1N1 virus emerged in Mexico and the United States of America (Girard et al. 2010). This virus, termed ‘Swine Flu’ subtype A/H1N1pdm09, was distinct from the then co-circulating 1977 pandemic A/H1N1 virus, and was the result of a triple reassortment between avian, swine and human influenza viruses

(Peiris et al. 2009). Like previous pandemics, A/H1N1 was entirely replaced by

A/H1N1pdm09, yet A/H3N2 again persisted to circulate seasonally. To this day

(April 2020), both A/H3N2 and A/H1N1pdm09 continue to co-circulate as seasonal influenza A viruses.

Beyond pandemic events, multiple outbreaks caused by novel influenza

A virus subtypes have occurred in humans in recent years (Bui et al. 2017). Many of these viruses have originated from avian species and are mostly associated with visiting live bird markets in China (Bethmont et al. 2016) but have yet to sustain human-to-human transmission necessary to cause a pandemic. Most avian influenza viruses cause mild infections in humans however some have caused significant morbidity and mortality (WHO 2005). Of significant public health interest currently are influenza A H5N1 (A/H5N1) and H7N9 (A/H7N9) subtypes which emerged in 1997 and 2013 respectively and have considerably high mortality rate in humans, approximately 53% (861 cases and 455 deaths) and 40% (791 cases and 314 deaths) respectively (Bethmont, et al. 2016). Both viruses however are not adapted to mammalian α2,3-linked SA receptors, which means their ability to ignite a global pandemic with significant mortality is for now constrained, however the threat remains.

1.3.3 Epidemiology, Prevention & Circulation

Every year, it is estimated that 15% of the global population is infected with seasonal influenza viruses (Stohr 2002) resulting in an estimated 500,000 deaths (Iuliano et al. 2018). Seasonal influenza-associated hospitalisation and death is highest among adults aged 65 years and children under five years of age (Simonsen et al. 2005; Nair et al. 2011), however the elderly represent approximately two-thirds (67%) of all seasonal influenza-associated deaths globally (Paget et al. 2019). Mortality however can differ by subtype depending on the relative seasonal predominance. Studies have shown that in seasons dominated by pre-2009 A/H1N1 or A/H1N1pdm09, mortality is highest among those 65 years and under, compared to A/H3N2 predominant seasons, where mortality is highest among those 65 years and over (Simonsen et al. 2013; Li et al. 2018; Paget, et al. 2019). Furthermore, regional variations in influenza- associated morbidity and mortality can be predicted by overall health and socioeconomic development indicators. For example, significant excess mortality is observed among the elderly (> 65 years) in South-East Asia, while for those under 65 years, excess influenza-associated mortality is highest in Sub-

Saharan Africa. Unfortunately, seasonal influenza surveillance data in many developing countries is either limited or unavailable, meaning accurate estimates of burden in many parts of the world is severely lacking (Bresee et al.

2018).

Infection with influenza can be prevented through a single vaccination administered for both subtypes of influenza A and/or one or both lineages of influenza B (trivalent or quadrivalent). A range of vaccines are utilized, including inactivated, live attenuated, and recombinant HA vaccines, however, the majority of the world’s population has no access to vaccination, particularly

those in developing countries (Stohr 2002). Furthermore, vaccination is typically required each year due to significant antigenic drift of the HA surface glycoprotein leading to the rapid evasion of the adaptive immune response by subsequent seasonal strains (Boni 2008). The World Health Organization

(WHO) and respective collaborating centres around the world determine the composition of Northern and Southern hemisphere vaccines each year, however, due to the prolonged vaccine manufacturing cycles, mismatch, that is, when the selected vaccine strains do not adequately cover the future antigenicity of the predominately circulating strains, is unfortunately common and unpredictable (CDC 2012; Tricco et al. 2013; Ampofo et al. 2015).

Furthermore, there is growing evidence that repeat vaccination over subsequent seasons has reduced immunogenic effects, limiting overall vaccine effectiveness even in cases where vaccine strain selection is accurately forecast (Khurana et al. 2019; Music et al. 2019). Significant investments are being made in the development of universal influenza vaccines that can produce both durable and broadly protective effects against seasonally drifted strains, and a range of different influenza subtypes, including zoonotic and potentially pandemic influenza viruses (Fox et al. 2018). One promising strategy includes vaccines targeting the conserved stem region of HA, as opposed to readily adaptable head that varies significantly between seasons and subtypes (Neu et al. 2016).

Other strategies include the use of novel adjuvants, NA targeted vaccines, or combinations of both, however, ongoing concern regarding how pre-existing immunity shapes the longevity of the immune response following vaccination remains a challenge impeding the design of universal influenza vaccines (Henry et al. 2018; Jang et al. 2019).

The selection of seasonal influenza vaccine strains is further complicated by our changing understanding of global transmission dynamics. For example,

vaccine composition recommendations typically include strains that circulate in the opposing hemisphere during the preceding season, however, recent evidence has shown that the global circulation of seasonal influenza viruses is characterised by a source-sink model of continuously evolving strains in South-

East Asia that seed short-term seasonal epidemics in Oceania, North America and Europe (Rambaut et al. 2008; Russell et al. 2008; Bedford et al. 2015). This means that typical seasonal epidemics in the hemispheres do not significantly contribute to the long-term evolution of influenza viruses and affects current and future vaccine composition recommendations. Understanding the complex transmission dynamics of seasonal influenza remains an ongoing area of research interest, particularly within regions and countries. Such studies could help inform future vaccine composition recommendations, but also influenza surveillance, prevention and control strategies.

1.4 Category A Bioterrorism Agents – Variola

Category A bioterrorism agents are defined by the Centers for Disease

Control and Prevention (CDC) in the United States of America (USA) as pathogens or biological agents that pose a significant risk to national security because they (i) can be easily disseminated or transmitted from person to person; (ii) result in high mortality rates and have the potential for major public health impact; (iii) might cause public panic and social disruption; and (iv) require special action for public health preparedness (Darling et al. 2002). There are currently nine classified

Category A bioterrorism agents. Bacterial agents include (i) Bacillus anthracis, (ii)

Yersinia pestis and (iii) Francisella tularensis, each causing anthrax, plague and tularaemia disease respectively. Viral agents include (iv) Variola virus, the aetiological agent of smallpox, and a number of agents that cause severe viral haemorrhagic fevers including (v) Ebola virus and (vi) Marburg virus of the filoviridae family, and (vii) Lassa virus and (viii) Machupo virus of the Arenaviridae family. The ninth agent, (ix) botulinum, is the disease-causing toxin produced by the bacteria Clostridium botulinum, and can be disseminated independently for the purposes of bioterrorism (Darling, et al. 2002). Most of these agents circulate endemically, however smallpox is the only disease that has been successfully eradicated from the world. Despite eradication, smallpox remains a Category A bioterrorism agent because of the potential for deliberate release from laboratory stockpiles as an act of bioterrorism.

1.4.1 The History of Smallpox & Global Eradication

Variola virus (VARV) is a double-stranded DNA (dsDNA) virus of the

Orthopoxvirus genus and Poxviridae family. Approximately 186kb in length, this large virus is the aetiological agent of smallpox disease, which was declared successfully eradicated in 1980 (Behbehani 1983; Fenner et al. 1988). During the

20th century alone, the disease is estimated to have killed up to 500 million people worldwide (Koplow 2004), ten times higher than the last four influenza pandemics combined.

Characterised by rash lesions present predominantly on the distal extremities and face following a febrile prodrome, smallpox had an estimated

30% case fatality rate (CFR) (Behbehani 1983; Fenner, et al. 1988). Among infants, the CFR could be as high as 80% (Riedel 2005). The virus was primarily transmitted from person-to-person via respiratory droplets and contact with clothing and bedding contaminated from infected persons. Recent studies have also considered prior evidence of potential airborne transmission (Milton and microbiology 2012), including that of long-distance aerial convention of up to 1 mile and possibly further (MacIntyre et al. 2020).

Prevention of smallpox was safe and effective through vaccination with vaccinia virus or cowpox, discovered by Edward Jenner in 1796, however direct variolation or inoculation with smallpox as a means of prevention is known to have occurred centuries earlier in China and India (Gross and Sepkowitz 1998).

Protection via vaccination however is not lifelong, with complete immunity waning after 5 to 10 years (Rao 1972). Following the introduction of routine vaccination in many countries around the world, coordinated efforts to eliminate the disease began with varying degrees of success. In 1966 the WHO began the intensified smallpox eradication campaign which successfully eradicated the disease by 1979 (Behbehani 1983; Fenner, et al. 1988). The formal declaration of eradication occurred during the 33rd World Health Assembly in

1980 (Behbehani 1983; Fenner, et al. 1988). The last known naturally occurring case of smallpox occurred in Somalia in 1977 (Eyler 2003), however the last reported case of smallpox infection and death occurred in 1978 in Birmingham,

United Kingdom when a medical photographer Janet Parker was accidentally exposed to the virus stored in a laboratory at the University of Birmingham

Medical School (Hawkes 1979). Details of the exposure remain controversial to this day because airborne transmission via the hospital ventilation system was highly suspected but never proven (Hawkes 1979).

1.4.2 Post-Eradication & the Threat of Bioterrorism

Despite formal eradication, stockpiles of smallpox remain in two authorised WHO Collaborating Centres around the world for the purposes of ongoing research: the CDC in the USA and the State Research Center of Virology and Biotechnology (VECTOR) in the Russian Federation. Arguments both for and against the destruction of stockpiles have been made (Raymond 2011;

Thompson 2016), however the potential existence of unofficial stockpiles and novel developments in synthetic (Noyce et al. 2018) biology largely supports the indefinite storage of smallpox (Koblentz 2017).

Following the 2001 Terrorist Attacks in the USA, concerns regarding the intentional release of smallpox from these stockpiles for the purposes of bioterrorism grew (MacIntyre 2015; MacIntyre et al. 2018). Since then, new technologies including antivirals and next-generation vaccines have been developed however the persistent threat remains (Melamed et al. 2018) as the release of security sensitive pathogens due to laboratory accident is not without precedent (Engells and MacIntyre 2016). Adding to the concern of smallpox re- emergence is the growing population susceptibility to smallpox due to waning vaccine immunity, the loss of natural exogenous boosting effects from wild-type smallpox and rising levels of population immunosuppression (MacIntyre et al.

2018). In immunologically naïve populations, the CFR of smallpox could also be

as high as 70% in some cases (Gelfand and Posch 1971). Despite its eradication, smallpox clearly remains a significant pathogen of public health concern.

Part B: Phylogenetic Methods & Applications

In the last decade or so, the decreasing cost of sequencing (Christensen et al. 2015), the increasing accessibility of sequence data (Borry et al. 2018) from online repositories such as GenBank (Benson et al. 2011) and GISAID (Shu and

McCauley 2017), and availability advanced user-friendly software such as

MEGA (Molecular Evolutionary Genetic Analysis) (Kumar et al. 2016) and

BEAST (Bayesian Evolutionary Analysis by Sampling Trees) (Drummond and

Rambaut 2007) has seen the use of phylogenetic analysis across a range of diverse fields of study greatly expand. In simple terms, all phylogenetic analyses compare the similarities and differences between collections of genetic sequences to visualise their relatedness (phylogeny) in the form of a phylogenetic tree. Phylogenetic methods are particularly suited to the study of infectious diseases (Lam et al. 2010) and most recent advances relevant to molecular epidemiology have been in the development of sophisticated models and applications in Bayesian phylogenetics (Baele et al. 2018). Bayesian phylogenetic approaches were first described in the late 1990s (Rannala and

Yang 1996; Mau et al. 1997) and have only increased in popularity since

(Huelsenbeck et al. 2001). Critical to the recent success of Bayesian phylogenetics has been the efficient integration of diverse data sources including spatial and temporal traits associated with sequence phylogenies.

Such integration precipitates many novel insights relevant to infectious disease epidemiologists including the transmission dynamics of pathogens and opportunities for effective intervention. For example, using phylogenetics the transmission patterns of HIV have been shown to align with locations in social and sexual networks, which can subsequently be targeted with intervention strategies (Volz et al. 2013). Other examples include a quantitative understanding of global influenza transmission driven by air travel (Lemey et

al. 2009; Lemey et al. 2014). Since the field’s inception, Bayesian phylogenetics has continued to produce unique insights into the dynamics of infectious diseases with broad public health impacts. In this section we briefly review the construction of phylogenetic trees and their interpretation, before introducing

Bayesian phylogenetic analysis in detail with a focus on applications to public health.

1.5 Phylogenetic Tree Fundamentals

Phylogenetic tress are graphical representations of evolutionary relationships built from sequence data, either in the form of nucleotides (bases) or amino acids (Yang and Rannala 2012). In molecular epidemiology studies, these data commonly include the genetic sequences of genes or genomes sampled from pathogens such as viruses isolated from infected hosts.

Information about the inferred evolutionary relationships are presented as a diagrammatic ‘tree’ with ‘leaves’, ‘branches’ and sometimes a ‘root’ by comparing the similarity and differences between these sequence data. In Figure 1.2 an annotated example of a simple phylogenetic tree of 10 hypothetical viruses is shown.

The leaves of the phylogenetic tree, referred to as tips or external nodes represent the real-world genetic sequence data under investigation. The internal nodes are connected to the tips by branches. The length of a branch on along the x-axis typically indicates the genetic distance between nodes, that is how similar or different they are genetically, alongside the scale which is often by not always in units of nucleotide substitutions per site. The longer the branches or higher the scale, the greater the difference and vice versa. Internal nodes represent hypothetical ancestors to the sampled tips or adjacent internal nodes inferred during the analysis. For example, in Figure 1.2, samples T1 and T2 are closely

related and connected by a hypothetical ancestor convergent along their respective branches in which T1 is closer in evolutionary distance to than T2.

The hypothetical ancestor of T1 and T2 is connected to another sample T3 by another hypothetical ancestor common to T1, T2 and T3. Samples T9 and T10 are closely related but among all the samples, least related to samples T1 and

T2. All taxa (T1 – T10) are connected and related to the most recent common ancestor (MRCA) positioned at the root of the phylogenetic tree.

Figure 1.2: Rooted phylogenetic tree representing the inferred evolutionary relationship of 10 hypothetical viruses or taxa (T1 – T10). Random tree generated using the ape package in R.

Note however that the vertical distance on the tree is used only to aid visual interpretation of the phylogeny and does not inherently indicate relatedness, which is interpreted by tracing the branches between the tips. Some trees are said to be ‘rooted’ like Figure 1.2 which indicates an evolutionary

direction, while others are ‘unrooted’. Unrooted trees provide no information of the direction of evolution or common ancestry between sequences, with the focus instead simply on the relatedness between tips. Unrooted trees however are often arbitrarily rooted to the oldest sample in the set or to an outgroup which is typically a supplementary sequence included in the analysis that is known to be distantly related the primary set under investigation. Some phylogenetic trees can also be scaled in units of time; however, such trees will be discussed in later sections on Bayesian phylogenetics analysis.

1.5.1 Sequence Alignment

The construction of any phylogenetic tree begins with sequence data.

Data can be in the form of either nucleotides (bases) or amino acids, however, before a phylogeny can be generated, the data must be aligned such that positions or sites in the genome or peptide chain are arranged to identify regions of similarity or difference which is essential to infer relatedness. These differences arise due to errors in DNA replication resulting in base substitutions by point mutation or insertions or deletions (indels) which are represented by gaps. In the case of many viral species such as influenza, serial replication results in the accumulation of mutations over-time. Therefore, in very general terms, the greater the difference, i.e. the more mutations observed between sequences, the less related they are. There are numerous algorithms available to align sequence data. Examples include MUSLCE (Edgar 2004) and MAFFT (Katoh et al. 2002) yet deciding on which alignment algorithm to use is typically a practical one depending on the number of sequences in the dataset and desires for speed or accuracy (Chatzou et al. 2016).

1.5.2 Constructing Phylogenetic Trees – Distance Methods

Once sequences are aligned, there are several methods available to build a phylogenetic tree (Holder and Lewis 2003). These methods can be grouped into two major types: distance-based and character-based. Distance-based methods produce a phylogeny first by calculating the pair-wise genetic distance between sequences. Genetic distance is calculated as a fraction of the number of site differences in an alignment often assuming an evolutionary model of substitution. There are various evolutionary models available, and each defines the frequency of nucleotide bases, and the probability that one base will be substituted within an instantaneous rate matrix with increasing complexity. The

Jukes-Cantor 1969 (JC69) model is the simplest: the frequency of bases and substitution probabilities are all assumed to be equal (Jukes and Cantor 1969).

Other models such as Kimura 1980 (K80) similarly predicts the equal frequency of bases, however, sets different substitution rates for transitions, that is substitutions between purine (A«G) or pyrimidine (C«T) bases, and transversions, where a purine base is substituted for a pyrimidine base or vice versa (Kimura 1980). The Hasegawa, Kishino and Yano 1985 (HKY85) model also assumes different rates for transitions and transversion, however base frequencies are not considered equal (Hasegawa et al. 1985). In the general-time reversible (GTR) model, the frequency of bases can vary, and substitution probabilities all differ (Tavaré 1986). Additional model complexity can be added to account for variations in mutations rates across sites (Yang 1993), typically as a gamma distribution (Γ). The third codon position in most genes is a common example of rate variation across sites, because higher substitution rates are typically observed compared to the first and second codon positions due to redundancy in the genetic code. Other model parameters can also be incorporated to consider a proportion of sites invariable (I), that is, they do not

change over time, which is typically observed at sites under strong selective constraint for example.

Distance-based methods use this information stored in a ‘Distance-

Matrix’ to then infer a phylogenetic tree. Common distance-based methods include least squares (Cavalli-Sforza and Edwards 1967; Fitch and Margoliash

1967), minimum evolution (ME) (Rzhetsky and Nei 1993) and neighbour joining

(NJ) (Saitou and Nei 1987), which is by far the most popular. Each method relies on different algorithms or criterion to efficiently infer trees from the distance- matrix. These methods are very fast, especially for analyses with many sequences (Tamura et al. 2004). However, distance methods are known to perform poorly when sequences are highly diverged and when alignments contain a large number of gaps (Bruno et al. 2000).

1.5.3 Constructing Phylogenetic Trees – Character Methods

Character-based methods work directly on the given alignment rather than a distance-matrix. To do this, character-based methods utilize various optimality criterion to score or assess potential trees that best fit the alignment.

For example, in Maximum Parsimony (MP) methods, the optimal tree will be found amongst all potential trees that best minimizes the number of substitutions or character changes at each site to produce the original alignment.

Thus, the optimal tree becomes a prediction of the true evolutionary process given the alignment. Depending on the size of a dataset, it is often computationally intractable to evaluate all potential trees against an optimality criterion such as MP, and thus character-based methods will also usually employ various heuristic approaches to search possible trees within the potential tree space (Fitch 1971; Hartigan 1973; Sankoff 1975; Swofford 2001).

MP methods however are ‘model-free’ such that they do not explicitly

incorporate models of evolution, and thus the process of sequence evolution.

Because of this, multiple substitutions at highly variable sites are not considered and result in a common problem known as ‘long branch attraction’ (Felsenstein

1978; Huelsenbeck 1998).

Maximum likelihood (ML) is a very popular character-based method that scores trees using the phylogenetic likelihood equation:

[1] % = '()|θ)

Where p is the probability of observing the data, D (here the sequence alignment), given a specified evolutionary model and tree, θ (Felsenstein 1981).

As suggested, calculating the phylogenetic likelihood necessarily requires a predefined evolutionary model of substitution such as JC69, HKY85 or GTR. For each possible tree, the likelihood is calculated at each site given the evolutionary model and tree, with the total tree likelihood the product of probabilities across all sites and branches. The tree with the highest likelihood is considered the optimal representation of the true phylogeny given the sequence alignment.

Because ML is model based, unlike MP, it is possible to account for the probability of multiple substitutions in order to explain the data. Often substitution models are chosen that best fit the data, using tools such as

Modeltest (Posada and Crandall 1998) and jModelTest (Posada 2008), but choices can be made based on simplifying assumptions in order to balance computational performance with biological accuracy. Like MP, ML methods employ various heuristic search algorithms to efficiently estimate the optimal tree within the potential tree space and practical time limits. ML methods are highly accurate assuming the evolutionary model is first specified correctly.

This is especially the case for phylogenies with deep evolutionary histories, however, in contrast to other methods, ML is computationally expensive. For large datasets, and when the evolutionary model selected is complex e.g.

GTR+I+Γ, ML methods can sometimes take days to produce a tree. Examples of popular ML methods include PHYLIP (Felsenstein 2005), PhyML (Guindon and

Gascuel 2003), GARLI (Zwickl 2006) and RAxML (Stamatakis et al. 2004), which is the most efficient for large alignments.

1.5.4 Assessing Topological Confidence

For both distance and character-based methods, confidence in the final tree topology can be calculated using bootstrapping techniques (Felsenstein

1985). In bootstrapping, columns of sites from the original alignment are randomly drawn with replacement until a ‘bootstrap alignment’ of the same length as the original is produced. The bootstrap alignment is used to produce a ‘bootstrap tree’ using the same method as previously selected e.g. NJ, MP or

ML. This process is then repeated a pre-set number of times (convention states

1,000) producing a set of bootstrap trees. The frequency that common arrangements or clades appear across the bootstrap tree set is represented as a percentage. When clades appear across all trees in the set a bootstrap value of

100% is assigned indicating high confidence in the topology. Other confidence methods such jackknifing (Mueller and Ayala 1982), which employ different resampling approaches are available, but, are less popular compared to bootstrap methods for assessing topological confidence.

Bayesian phylogenetic methods are similarly character-based, and like

ML utilizes the phylogenetic likelihood (equation [1]), however, employs Bayes

Theorem and Markov chain Monte Carlo (MCMC) methods to produce a posterior probability distribution of trees which inherently records topological

confidence. Bayesian phylogenetic methods are the primary focus of this thesis and therefore discussed in particular detail in the following section.

1.6 Bayesian Phylogenetics

Bayes’ Theorem is the foundation of Bayesian statistical inference and therefore Bayesian phylogenetic methods (Rannala and Yang 1996; Mau, et al.

1997; Huelsenbeck, et al. 2001). In comparison to frequentist statistical inference where collected or observable data are alone used to produce statistical estimates, Bayesian statistical inference incorporates prior knowledge or hypotheses (a priori) about the observed data to calculate the posterior probability. According to the Theorem:

'()|θ)'(θ) [2] '(θ|)) = '())

where the posterior probability, !(θ|%), is proportional to likelihood of the data given the model, !(%|θ), times the prior probability of the model, !(θ), over the probability of the data, !(%) also known as the marginal likelihood. In pathogen phylogenetics, as with the likelihood equation before, the tree parameters and evolutionary model together specify the model, θ, for the data

D, which is necessarily an MSA of sampled taxa. Simply, this allows for the estimation of the posterior probability of a phylogenetic tree, that is, that the tree is correct given a sequence alignment and evolutionary model, whereas ML for example, estimates the likelihood that a tree will give rise to the observed sequence alignment, a particularly unintuitive interpretation.

1.6.1 Markov chain Monte Carlo (MCMC)

Direct calculation of the posterior probability as above however is computationally prohibitive as it first requires calculation of the marginal likelihood of the data, known as the normalising constant. Calculation of the normalising constant requires multidimensional integration over all possible trees which is effectively unknowable. Instead, the probability of the data is assumed to be constant, and a simulation algorithm known as MCMC is utilised to approximate the posterior probability instead by sampling from its target distribution (Metropolis et al. 1953; Hastings 1970). To do this, the MCMC algorithm begins at step one by initialising a random tree and all associated model parameters, θ!, and its posterior probability, !(θ!|%), is calculated un- normalised as per equation [2] excluding !(%). The next step in the chain is the proposal of a new tree and parameters, θ". At this step, the posterior probability again calculated and compared to the posterior probability at the previous step.

If the posterior value of the new step is higher than in the previous step, that is, if θ" > θ! , the step is accepted, a new randomisation step occurs, and the process repeats for a specified number of generations. If not, it is rejected, and another randomisation is performed and posterior calculated until it is accepted.

In this way, and given sufficient time, the MCMC algorithm converges on an equilibrium distribution within the posterior space. The time and frequency the

MCMC chain spends within this distribution is considered an approximation of posterior probability of the tree and model parameters given the data. An example of an MCMC approximation to a hypothetical target distribution is shown in Figure 1.3. Convergence and mixing are two essential properties of any MCMC chain that should be inspected and diagnosed. Convergence describes an MCMC chain that has searched and subsequently settled into a range within the target distribution.

Once a chain has converged, a sufficient number of posterior estimates should be drawn from the target distribution to accurately approximate that distribution and its corresponding uncertainty. When this occurs, a chain is also said to be well mixed, with convention stating greater than 200 effective estimates from the posterior as sufficient. Figure 1.4 compares a sufficiently mixed MCMC trace (left) to an insufficiently mixed trace (right). This problem however can often be remedied by increasing the number of generations in the

MCMC such that sufficient samples are taken from the posterior, at least for a parameter that has converged.

Figure 1.4: Example of sufficiently mixed (left) and insufficiently mixed (right) MCMC trace for a figurative parameter. Figure generated using R and adapted from (Nascimento et al. 2017).

In pathogen phylogenetics, because the MCMC algorithm effectively moves through the posterior tree space by sampling random trees and parameter estimates, the target distribution explored necessarily represents the inherent uncertainty. By capturing the central 95% of this distribution, a range analogous to 95% confidence intervals in frequentist statistics can be produced known as the Bayesian credible interval (BCI) or highest posterior density

(HPD). From this same tree distribution, a representative tree with the highest posterior probability can be selected known as the maximum clade credibility

(MCC) tree. Confidence in the final tree topology is represented as a proportion by the frequency at which clades appear on trees within the posterior tree distribution. For clades that appear in every posterior estimate, a value of 1 is given, indicating high confidence in the topology. Lastly, due to the inherently stochastic nature of the MCMC algorithm, early moments spent in the chain tend to fluctuate haphazardly until convergence on the posterior. For this reason, early posterior estimates are typically discarded so as not to skew the final distribution. This is called the burn-in period, with convention stating the

first 10% of the chain i.e. 10% burn-in, be removed. However, depending on analysis, substantial time can be spent searching posterior space before parameter convergence within the equilibrium, therefore results should always be inspected, and the necessary burn-in period determined.

1.6.2 Model Selection

Calculation of the posterior probability first requires the specification of the model parameters including the evolutionary model e.g. HKY or GTR, which is an important step for accurate posterior estimation. i.e. the model selected should fit the data provided. Model fit can be assessed by calculating the Bayes

Factor (BF):

'()|θ!) [3] BF = '()|θ")

where the BF is calculated as the ratio of the likelihood of the data given model one, !(%|θ"), and the likelihood of the data given model two, !(%|θ#). Convection states that a BF ratio of greater than 3, that is, the marginal likelihood of model one is at least three times greater than that of model two, indicates sufficient positive support (Jeffreys 1961; Kass and Raftery 1995). One popular method to estimate model likelihoods from MCMC outputs and calculate the

BF is the posterior harmonic mean estimator (HME) (Newton and Raftery 1994).

This method however has recently been shown to be susceptible to overestimation of model likelihoods, thus biasing the results (Baele et al. 2012).

Newer methods such as path sampling (PS) (Lartillot and Philippe 2006) and stepping-stone sampling (SSS) (Xie et al. 2011) have been shown to estimate model likelihoods more accurately and have seen increasing adoption in recent

years (Baele, et al. 2012). This thesis therefore exclusively adopts PS and SSS methods for evolutionary model selection.

1.6.3 Software

There are many modern software implementations that are dedicated to

Bayesian phylogenetic analysis. Two of the most popular include MrBayes

(Huelsenbeck and Ronquist 2001) and BEAST (Bayesian Evolutionary Analysis by Sampling Trees) (Drummond and Rambaut 2007) which are both freely available. Both implementations incorporate a large number of evolutionary models with informative priors and utilise the BEAGLE (Broad-platform

Evolutionary Analysis General Likelihood Evaluator) (Ayres et al. 2012) library which dramatically optimises calculation of the phylogenetic likelihood.

However, the extensibility of the BEAST framework, and its focus on producing time-scaled phylogenetic trees and their corresponding applications for infectious disease epidemiology, has seen its popularity greatly increase. For this reason, BEAST is the sole Bayesian phylogenetics application used in this thesis, and therefore the focus of the next section.

1.7 Methods of Data Integration in BEAST

As mentioned above, BEAST’s focus on time-scaled phylogenetics has seen its popularity as a tool in the study of infectious disease epidemiology, greatly increase, particularly within the last decade. Furthermore, its extensible framework means many novel models and data sources can be integrated into analyses. Examples include molecular clock, phylodynamic, phylogeographic and generalised linear models. These models, with their broad public health impacts, was the primary factor motivating the use of BEAST in this thesis.

1.7.1 Molecular Clocks & Time-Scaled Phylogenetic Trees

As mentioned in section 1.5.2 (Constructing Phylogenetic Trees –

Distance Methods) the genetic distance between any two related pathogens such as viruses can be roughly calculated from the accumulation of mutations in their genetic code generated due to errors made during replication. Over sufficient timescales, the genetic distance between any two related pathogens appears to increase linearly, which has driven the theory behind the molecular clock hypothesis (Zuckerkandl and Pauling 1962; Zuckerkandl and Pauling 1965). The molecular clock hypothesis proposes that the accumulation of genetic mutations correlates with time and allows for the estimation of mutation rates i.e. substitution rates. For RNA viruses, the protein responsible for replication,

RNA-polymerase, is particularly error-prone, and thus, RNA viruses typically have high substitution rates when assuming a molecular clock (Jenkins et al.

2002). This means for small RNA pathogens such as influenza A, which has an estimated substitution rate of 3x10-3 substitutions per site per year, sufficient genetic change can be observed within days or weeks (Nelson et al. 2006). In the case of DNA viruses, which are less error-prone, longer timescales are required to observe genetic change.

Figure 1.5: Hypothetical example of a time-scaled phylogenetic tree containing 40 sequence taxa where internal nodes represent divergence times along the x- axis. Two distinct clades are apparent, and the time at which they can be seen to diverge at the root is the TMRCA i.e. divergence in late June 2017. This tree assumes homochromous sampling, where sequence taxa are isolated on the same day. Time-scaled phylogenetic trees can also be constructed for heterochronously sampled sequences.

All trees constructed in BEAST employ a molecular clock model of some kind and therefore always produces ‘rooted’ trees with an evolutionary direction from past to present. An example of a rooted time-scaled phylogenetic tree can be seen in Figure 1.5. The simplest model, the strict clock model, assumes that the substitution rate across all branches of the tree is constant

(Bromham and Penny 2003). However, this is not always a biologically accurate assumption. For example, different viral lineages may encounter different selective pressures due their environment or host, leading to lineage or branch specific variations in substitution rates (Felsenstein 1988; Jenkins, et al. 2002;

Nelson and Holmes 2007; Ho and Duchêne 2014). The relaxed molecular clock model however allows for variations in the substitution rate across all tree branches, which has demonstrated good fit to a range of data sources over strict

clock models (Drummond et al. 2006). Other implementations include random and fixed local clock models (Drummond and Suchard 2010) which effectively allows the implementation of different strict clocks across specific branches, in contrast to relaxed clocks which allows for complete variation in substitution rate. BEAST provides all the described clock models along with a number of well-researched, informative prior distributions such as lognormal, exponential and logistic distributions by default.

Phylogenetic trees that assume and incorporate molecular clock parameters are scaled in substitutions per site per unit-time, in contrast to regular phylogenetic trees, like that shown in Figure 1.5, which is scaled in substitutions per site. A key feature of BEAST is the ability to additionally integrate the sampling-time of sequence data, which calibrates the substitution rate unit-time scale (Drummond and Rambaut 2007; Drummond et al. 2012).

This single development has had the greatest impact on molecular epidemiology studies, particularly for infectious diseases, with substantial applications for public health. For example, using a time-calibrated phylogenetic tree, the emergence of the 2009 pandemic influenza

A/H1N1pdm09 subtype in the human population was estimated to have occurred in early February 2009, almost two months prior to the first detected case in April 2009 (Hedge et al. 2013). Furthermore, the development of sophisticated epidemiological models in BEAST have all been made possible with the inclusion of time-stamped sequence data. The phylodynamic, phylogeographic and generalised linear models discussed in later sections all necessarily require the input of time-stamped sequence data to first calibrate the phylogenetic tree in units of time.

1.7.2 Phylodynamics & Coalescent Theory

Phylodynamics describes how underlying processes such as epidemiology and host immunology can alter the evolution and subsequent phylogeny of populations such as viruses (Grenfell et al. 2004). For example, the transmission dynamics of an evolving virus can change according to the susceptibility of the host population and changes in selection pressure such as the use of vaccination and antivirals respectively. In Bayesian phylogenetics, the construction of time-scaled phylogenetic trees and the use of phylodynamic models allows the influence of many underlying processes to be resolved.

Many of the phylodynamic models available to Bayesian phylogenetic analysis and BEAST are based on coalescent theory which describes that the time required for any two taxa to share a common ancestor correlates with the size of the taxa’s population (Kingman 1982). For example, by randomly sampling two viruses from a small population of viruses, the probability that they coalesce on a common ancestor sometime in the recent past is high, i.e. the short coalescent time correlates with the small population size from which they were drawn. In contrast, given a large viral population, the coalescent time of any two randomly sampled viruses will likely be longer. Because time-scaled phylogenetic trees inherently infer hypothetical ancestry, i.e. coalescent events between sampled viruses, and are calibrated in units of time, models based on coalescent theory have been developed to parameterise tree structures and draw inferences about the effective size of viral populations (Pybus et al. 2000).

Furthermore, because viruses require hosts to replicate and spread, inferred viral population sizes can also be an accurate indicator of epidemic size for example (Pybus et al. 2003).

In BEAST, various models are available to infer viral population demographics, along with corresponding tree priors. The simplest coalescent model assumes an idealised Wright-Fisher population (Fieher 1922; Wright

1931) of constant size that is also randomly mixing, free from recombination, selective pressure, overlapping generations and significant immigration or emigration. These strict model assumptions however do not always fit well to pathogen sequence data, notably epidemic diseases whose incidence may be in growth or decline. Other implementations such as the exponential growth and logistic growth models (Griffiths and Tavare 1994) relax these assumptions and incorporate additional parameters and tree priors to account for this demographic growth and decline respectively. The most useful coalescent models available in BEAST are ones that do not impose fixed assumptions of a growing or declining population. These non-parametric models are particularly useful in constructing the past demographic histories of viral populations over long periods of time where successive instances of growth and decline may have occurred. Two popular non-parametric models, the coalescent Bayesian skyline model (Drummond et al. 2005) and Skygrid model (Gill et al. 2013), calculate the effective population size through time by measuring the serial frequency of coalescent events on trees sampled during the MCMC process, thus also incorporating uncertainty in the demographic estimate. These models have proven useful particularly in estimating the true incidence of undetected infections in populations (Volz et al. 2009) as well as approximating epidemic curves when the past population size is unknown. Furthermore, estimation of viral population growth rates early in epidemics can also be used to calculate traditional epidemiological parameters such as R0 (Wallinga and Lipsitch 2007), which are essential for informing prevention measures and forecasting the impact of outbreaks.

1.7.3 Trait Association & Phylogeography

Beyond the effective extraction of phylodynamic properties, such as the viral population demographics that underlie time-scaled phylogenetic trees, other methods of data integration in Bayesian phylogenetics, supported by innovative models, can be used to answer increasingly relevant clinical and public health questions. The most prominent example at this time is phylogeography analyses where time-scaled phylogenetic trees are annotated with spatial sampling data to detect the presence of geographic structures within and between pathogen populations and infer respective transmission histories.

As mentioned previously in section 1.7.2 (Phylodynamics & Coalescent

Theory), the underlying shape of a phylogenetic tree is heavily influenced by its environment which includes geographic structures. Real-world examples of this include observations of unique genetic lineages that tend to circulate exclusively within particular geographic regions, such as the African and Asian lineages of

Zika virus (Haddow et al. 2012). Such obvious lineage specific differences can be easily resolved in most phylogenetic trees, not just Bayesian implementations, however novel approaches that utilise Bayesian posterior tree distributions can be used to uncover deeper spatial associations that may not be initially apparent (Volz et al. 2013). For example, location-traits can be assigned to each phylogenetic tree sampled in a Bayesian posterior MCMC chain, and the number of transitions between discrete-locations quantified as the observed distribution. Subsequently, randomisation of the same tip-locations across the posterior tree set can be performed to effectively produce a null distribution of the transition count statistic, that is, a distribution that describes a phylogenetic tree with no geographic structure. Comparison of the observed and null

distributions can therefore indicate the presence of geographic structure if the number of transitions in the real-world tree is lower than that in the null distribution (Chen and Holmes 2009). This approach of trait-annotation and randomisation across the posterior tree distribution is a method unique to

Bayesian phylogenetics, and has a number of practical implications beyond spatial-trait association. For example, any epidemiological trait can be annotated to sequence data and evaluated using established statistics such as the association index (AI) (Wang et al. 2001) or parsimony score (PS) (Slatkin and Maddison 1989). The ability to produce statistical distributions for any trait- annotated phylogeny from Bayesian posterior tree sets is particularly powerful

(Parker et al. 2008).

Figure 1.6: Example of asymmetric (left) and symmetric (right) instantaneous rate matrices used in discrete-trait Bayesian phylogeography models to estimate spatial transmission across a phylogeny sampled from hypothetical locations A:D.

Other approaches can be used to infer viral transmission histories from

Bayesian phylogenetic trees and are explicitly model based. The most widely used phylogeography model is the discrete-trait model or discrete-trait analysis

(DTA), due in-part to its simple implementation and early establishment in the

field (Lemey, et al. 2009). In the DTA, spatial information is incorporated as characters in an instantaneous rate matrix (Λ) similar to the character-based evolutionary mutations models described in section 1.5.2 (Constructing

Phylogenetic Trees – Distance Methods). However, in this case, characters in the model represent locations such as cities or countries instead of nucleotides or amino acids. As such, for K discrete locations, a matrix (Λ) of size K x K is generated to estimate all possible individual character transition rates (λjk). Two example matrices are shown in Figure 1.6 each with four hypothetical locations

A:D. In the first case (left), the transition rate between each location is non- reversable or asymmetric such that the rate of character transitions from locations A to B (λAB) is different to rate of character transition from locations B to A (λBA). In contrast, a simplified reversable symmetric model (right) specifies a rate matrix where the rate of character transition between locations A and B are equal (λAB).

In either case, at each step in the MCMC process, ancestral location states are estimated at each internal node of each tree but is independent to the tree generative process. The result is a posterior distribution of trees annotated from root to tip with spatial data identifying the minimum number of transitions necessary to explain the diffusion process in each instance. An MCC tree produced from this posterior set is annotated with the inferred location states at each internal node in the tree set, thus quantifying the uncertainty around each estimate. Visualisation of the diffusion process can be generated directly from the time-scaled MCC trees using software such as SpreaD3 (Bielejec et al. 2016), where diffusion between the given locations is inferred as shifts between the ancestral location state of internal nodes along the tree.

The significance of all possible transitions can be calculated during the

MCMC process by implementing a Bayesian stochastic search variable selection

(BSSVS) (Lemey, et al. 2009) procedure which assumes a priori that most possible transitions are unlikely and equal to zero. A BF test can be performed similar to equation [3] in section 1.6.2 (Model Selection) to estimate the contribution of each transition rate in the model according to:

' 2 # 4 1 − '# [4] BF = 5 2 # 4 1 − 5#

where the significance of location k, (BF$), is the odds that rates for k are

% non-zero as per the BSSVS procedure, + ! ,, over the prior odds that rates for "&%! ' k are non-zero, + ! ,, which is often assumed to be 50%. As before, a BF ratio "&'! > 3 indicates support, i.e. that transitions from location k were observed in the posterior at least three times more than the prior, and therefore contributes significantly to the model (Jeffreys 1961; Kass and Raftery 1995). In terms of public health and outbreak control, when these models are calibrated using temporally sampled sequence data, patterns of transmission can be identified, and where possible, interventions implemented.

Other popular phylogeography models include the continuous-trait model (Lemey et al. 2010) and various structured coalescent approximations (De

Maio et al. 2015; Müller et al. 2017). In the continuous-trait model or continuous- trait analysis (CTA), continuous-values such as geographic coordinates are inferred by a relaxed random walk model. In contrast to the DTA, where inferences are restricted to the explicit locations assigned to the tree tips, CTA

define a two-dimensional landscape which can be explored during the MCMC process by the model (Lemey, et al. 2010). Where coordinate data is provided, the application of the CTA can allow a more realistic inference of viral transmission compared to DTA (Faria et al. 2011). Structured coalescent approximation (SCA) models are similar to DTA such that all discrete locations available for phylogeographic inference are explicitly assigned to the extant tips, however, migration is approximated from, and directly incorporated into the

MCMC process (De Maio, et al. 2015; Müller, et al. 2017). Furthermore, by employing a structured variety of the coalescent model described in section

1.6.2, underlying subpopulations can explicitly affect the tree generative process, and more accurate estimates of migration, particularly within deeper phylogenies, can be constructed. The SCA approach however remains a computational expensive implementation among phylogeography models despite recent improvements (Müller, et al. 2017). The choice to employ DTA,

CTA or SCA methods to study pathogen transmission however is a practical one, largely dependent on the sampling resolution available (discrete vs continuous), and computational limits for analyses (DTA vs SCA).

1.7.4 Generalised Linear Modelling

A powerful variation of the DTA phylogeography model allows the integration of additional epidemiological and ecological covariates. These covariates, such as aggregate population densities and climate factors associated with each discretised model location, can be simultaneously evaluated as potential predictors of transmission by additionally parameterising all possible pair-wise location rates as a log linear function in a Generalised Linear

Modelling (GLM) framework (Lemey, et al. 2014) as per equation [5]:

[5] logΛ$% = β!δ!log='!$%> + β"δ"log='"$%> + ⋯ + β&δ&log('&$%)

where logΛ12 represents the logarithm of the same instantaneous rate matrix of all possible transitions between each discrete location as described in section 1.6.3, and the contribution to Λ of each predictor, !(, between all possible pairwise locations log(!()*) is incorporated by its effect size, β(, and a binary indicator variable, δ( . The binary indicator (0,1) function of δ( dictates each predictors inclusion at each step of the MCMC process allowing BSSVS implementation as described previously. As such, a BF test of significance can be performed as per equation [4] by calculating the ratio of each predictor’s posterior odds of inclusion over the prior odds of equal prior probability of inclusion or exclusion in the model. In this way, the significance of each predictors effect on the transmission process is calculated, the hidden dynamic effects of endemic and epidemic diseases uncovered, and future outbreak response and control activities informed.

1.8 Applications to Public Health & Thesis Aims

Phylodynamic, phylogeography and GLM models are increasingly being used to uncover the spatial dynamics and molecular epidemiology of various endemic infectious diseases. For example, phylodynamic models have been used to uncover the underlying growth and decline of methicillin- resistant Staphylococcus aureus (MRSA) populations in the USA over-time, which has been shown to correlate with changing prescription rates of β-lactam antibiotics (Volz and Didelot 2018). Various phylogeography models have been employed to quantity the zoonotic transmission dynamics of Yellow fever virus

(YFV) in the Americas (Faria et al. 2018), and avian influenza viruses in China and Indonesia (Bui et al. 2018; Njoto et al. 2018) effectively characterising the

future risk of spread in human populations. These same methods have also been applied in the retrospective analysis of epidemic diseases. For example, phylogeographic analysis of the 2014-2015 Ebola outbreak in West Africa demonstrated that a single unobserved transmission event between neighbouring provinces in Guinea and Sierra Leone sometime in late December

2013 most likely established the epidemic and guaranteed that subsequent border closures were overall ineffective (Dudas, et al. 2017). In the same study,

GLM methods were used to demonstrate that the virus's establishment in urbanised regions was a major factor contributing to the overall scale and length of this unprecedented epidemic (Dudas, et al. 2017). This suggests that pre- emptive border closures and focused public health efforts to prevent urban establishment could reduce the potential impact of future Ebola epidemics. The increasing availability of pathogen sequence data also means these methods and models are increasingly being positioned for use in real-time during ongoing epidemics (Baele, et al. 2018). The application of these methods can provide insights that can be used to supplement traditional epidemiological data sources and inform public health decision makers (Gardy and Loman 2018).

In this thesis, we aim to utilize the advanced Bayesian phylogenetic methods and models described to characterise the molecular epidemiology, evolution and phylogeography of various pathogens of public health significance. We aim to uncover clinical and spatial patterns of disease that can be used to inform and support future surveillance and outbreak investigation activities including epidemic response and control. Our approach also aims to evaluate the effect of sample size and sample representativeness on our results, and to consider the implications for public health decision makers in the context of endemic and epidemic disease surveillance and control.

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Chapter Two

The Molecular Epidemiology and Clinical

Phylogenetics of Rhinoviruses among Paediatric

Cases in Sydney, Australia.

This chapter has been prepared for publication and includes the following co- authors: Scotch M, MacIntyre CR. This study was a direct result of my research towards this PhD, and reproduction in this thesis will not breach copyright upon publication.

2.1 Chapter Abstract

Rhinoviruses (RV) represent the most common aetiological agent of all acute respiratory tract infections across all age groups and a significant burden of disease among children. Recent studies have shown that RV-A and RV-C species are associated with varying degrees of disease severity and clinical symptoms however contrasting evidence suggests these results remain inconclusive. In this study, we aimed to uncover potential associations between

RV species and subtypes, and clinical disease severity using a matched dataset of 52 RV isolates sampled from children (<18 years) in Sydney, Australia between 2006 and 2009 using epidemiological and phylogenetic methods. We found that RV-C was significantly more likely to be isolated from paediatric cases under two years of age compared to RV-A although no significant differences in recorded symptoms were observed. Significant phylogenetic-trait associations between age and the VP4/VP2 capsid protein phylogeny suggests age-specific variations in infectivity among subtypes might also be possible.

Future studies could aim to identify specific genetic markers associated with age-specific infectivity of RV which could inform treatment practices and public health surveillance of RV.

2.2 Introduction

Rhinoviruses (RV) are a highly prevalent and diverse respiratory pathogen of the Enterovirus genus and are a major cause of all acute respiratory illnesses throughout the year in both adults and children (Mäkelä, et al. 1998;

Monto 2002). The attributable burden of RV in both developed and developing countries is significant, accounting for approximately 34.4 disability adjusted life years (DALY) per 1000 population among children under five (Weiss and

Sullivan 2001; Bertino 2002; Gaunt, et al. 2011). The direct and in-direct socioeconomic burden of RV has also been estimated to cost approximately $60B/year in the USA alone (Fendrick, et al. 2003). There are three recognised species of

RV denoted RV-A, RV-B, and RV-C, among which there are currently 179 classified subtypes based on sequence homology to former serology-based prototype strains. Since the shift from serology-based to sequence-based typing of RV, our understanding of the RV genome and diversity has increased. For example, RV-C was first recognised as a distinct RV species in 2006 (Lamson, et al. 2006; Lau, et al. 2007; McErlean, et al. 2007; Renwick, et al. 2007; Briese, et al.

2008; Dominguez, et al. 2008) but had evaded serology-based detection until molecular methods became routine (Arden et al. 2006; Kistler et al. 2007). The transition to sequence-based RV typing has also led to numerous taxonomic changes as old serotypes were regrouped (such as RV-A98 into RV-A54 (Rathe et al. 2010)), and some species re-classified entirely (such as RV-A87 to

Enterovirus-D68 (EV-D68) (Blomqvist et al. 2002; Savolainen et al. 2002)).

Despite the sheer diversity of RV, they circulate freely around the world with no geographic structure of either species or subtype (McIntyre, et al. 2013).

Typical symptoms of upper respiratory infection with RV include rhinorrhoea, nasal congestion, cough, headache, and malaise (Fields, et al. 2013).

However asymptomatic infection is also prevalent, particularly within households (Peltola, et al. 2008). It is now well established that RV can also compromise the lower respiratory tract, in contrast to previous evidence

(Papadopoulos, et al. 2000), and are sometimes associated with severe disease such as pneumonia and bronchiolitis (Hayden 2004). Exacerbations of asthma and other pre-existing diseases such as chronic obstructive pulmonary disease and cystic fibrosis in both adults and children has furthermore been associated with lower respiratory infection with RV (Nicholson, et al. 1993; Varkey and

Varkey 2008). There is increasing (Jin, et al. 2009; Piralla, et al. 2009; Lee, et al.

2012; Lauinger, et al. 2013; Chen, et al. 2015), but yet inconclusive (Huang, et al.

2009; Moreira, et al. 2011) evidence to suggest RV-A and RV-C species may be associated with more severe disease compared to RV-B strains, and similar efforts have been made to examine the varied clinical manifestations between

RV subtypes, again with inconclusive or contrasting results (Bruning, et al. 2015;

Martin, et al. 2015).

In order to better understand potential associations between RV species and clinical features among paediatric cases, we aimed to integrate genetic and epidemiological data using Bayesian phylogenetic methods.

2.3 Materials & Methods

2.3.1 Compilation of Dataset & Sequencing

We extracted the sequence data of 91 RV isolates collected between 2006 and 2009 in Sydney, Australia from GenBank. Thirty-eight sequences (42%) were originally collected as part of a household transmission study (MacIntyre et al. 2009) and the other 53 (58%) isolated from a hospital department

(Ratnamohan, et al. 2016). Among these, 52 RV isolates (28 & 24 from each study

respectively) were sampled from paediatric cases defined here as under the age of 18 years. Where available, we matched these sequences to corresponding clinical and demographic data collected during the same period: demographic data on age was available for all 52 paediatric isolates, while clinical data was only available for 28 sequences. Sequences were first generated according to

(Ratnamohan, et al. 2016) with relevant detail included here. RNA was extracted directly using LabTurbo Viral DNA/RNA Extraction kits (TaiGen Biotechnology

Inc., Taiwan) according to the manufacturer's specifications, and cDNAs synthesized using SuperScript III reverse-transcriptase (Invitrogen, USA). Both

5’ untranslatable regions (5’UTR) and VP4/VP2 capsid protein segments were sequenced in both directions using respective forward and reverse primers listed in Supplementary Table S2.1. Amplicons were purified from agarose gel using QIA PCR purification kits (Qiagen GmbH, Hilden, Germany) and sequenced bi-directionally to confirm specificity in an ABI-3730 XL DNA

Analyzer (Applied Biosystem Inc., Foster City, CA).

2.3.2 Characterisation and Epidemiology

Previous phylogenetic analyses found serotype disagreements between the phylogenetic results of RV-A and RV-C species using the 5’UTR region

(Ratnamohan, et al. 2016). This incongruence can be explained by inter and intra-species recombination, particularly between the 5’UTR segment of RV-A and RV-C (Waman et al. 2014). We therefore recharacterized isolates by RV species and genotype if possible, primarily using assembled VP4/VP2 sequences

(or 5’UTR sequences where VP4/VP2 amplification or assembly failed) with

NCBI BLAST. We constructed contingency tables by RV species and symptoms for all 28 paediatric cases with matching clinical data. We considered all 52 paediatric isolates for analysis by age groups here separated as over and under

two years of age. We performed Fisher Exact tests to measure statistical associations between age groups and clinical symptoms by RV species using R v3.4.4.

2.3.3 Phylogeny-Trait Association

We confirmed the suitability of both 5’UTR and VP4/VP2 sequence regions for temporal Bayesian phylogenetic analysis by producing preliminary

Maximum Likelihood trees using RAxML v8.2 (Stamatakis 2014) via Geneious v11.1.5 (Kearse et al. 2012) and Tempest v1.5.1 (Rambaut et al. 2016)

(Supplementary Figure S2.1). We excluded 17 sequences from the demographic phylogenetic analysis (N = 35) and seven from the clinical phylogenetic analysis

(N = 21) since either the 5’UTR and VP4/VP2 sequences were not available. We aligned sequences using MUSCLE v3.8 (Edgar 2004) and used BEAST v1.10.4

(Suchard et al. 2018) to produce posterior phylogeny distributions of both 5’UTR and VP4/VP2 independently and as a multi-locus partition model. We specified a relaxed molecular clock (uncorrelated lognormal prior) (Drummond, et al.

2006), constant demographic tree prior, and GTR+I+Γ4 nucleotide substitution model as determined per previous studies (Waman, et al. 2014). We ran each model individually for 50 million Markov Chain Monte Carlo generations

(MCMC) sampling every 5,000 steps. We checked for parameter convergence and sufficient mixing of the posterior (effective sample size > 200) using Tracer

1.7 (Rambaut et al. 2014). For each model, we calculated phylogenetic parsimony scores (PS) (Fitch 1971) association index (AI) (Wang, et al. 2001) statistics as a measure of phylogeny-trait correlation for the matched demographic and clinical symptoms. We used BaTS v0.9.0 (Parker et al. 2008) to account for statistical uncertainty in each phylogeny across a posterior distribution of 9,000 trees (after removing 10% burn-in) for each model. As a

positive control, we also calculated the PS and AI statistic between serotypes.

This study was approved by the UNSW Human Research Ethics Committee

(HC17284).

2.4 Results

2.4.1 Sequence Characterisation

In Table 2.1, we show the complete distribution of RV species and subtype characterisation with additional metadata in Supplementary Table S2.2.

Both 5’UTR and VP4/VP2 sequences were available for 35 (67%; N=35/52) isolates while 5’UTR was only available for the remaining 17 (33%; N=17/52) isolates. The majority (92%; N=48/52) of sequences isolated were characterised as RV-A (48%; N=25/52) and RV-C (44%; N=23/52) species. We determined the complete subtype recharacterization for 43 isolates (83%; N=43/52). The majority

(78%; N=7/9) of untyped isolates was among the 17 only sequenced for 5’UTR.

Overall, RV-C11 was the most frequently detected subtype among characterised isolates (9%; N=4/43) followed by RV-A01 (7%; N=3/43). Just over half of all characterised isolates (51%; N=22/43) were unique i.e. had no common subtype among all isolates characterised.

Table 2.1: Distribution of species and genotypes detected by species among 52 paediatric RV cases in Sydney, Australia between 2006 and 2009 Species Count Genotypes A00, A01, A12, A15, A20, A21, A22, A24, A28, A31, A 25 A33, A43, A49, A56, A57, A61, A67, A71, A89 B 4 B06, B17, B48, B91

C 23 C00, C02, C06, C11, C16, C26, C28, C32, C37, C40

2.4.2 Clinical and Demographic Epidemiology

Among the 28 RV isolates with matching clinical data, the majority were characterised as RV-A and RV-C. Only a single RV-B isolate had matching clinical data. Cough (96%; N=27/28), nasal congestion (96%; N=27/28) and sneezes (79%; N=22/28) were reported in the majority of cases with matching clinical data (Table 2.2). All other reported symptoms were in the minority (46% and below).

Table 2.2: Comparison of clinical symptoms of 28 paediatric RV cases in Sydney, Australia by RV species Serotype

Total Symptoms A (N=11) B (N=1) C (N=16) P value* (N=28) n % n % n % n % Abdominal pain 3 27% 0 0% 2 13% 5 18% 0.37 Chills 1 9% 0 0% 4 25% 5 18% 0.62 Cough 11 100% 1 100% 15 94% 27 96% 0.99 Diarrhoea 2 18% 0 0% 2 13% 4 14% 0.99 Earache 5 45% 0 0% 4 25% 9 32% 0.41 Lethargy 9 82% 1 100% 10 63% 20 71% 0.41 Loss appetite 7 64% 1 100% 12 75% 20 71% 0.68 Congestion 11 100% 1 100% 15 94% 27 96% 0.99 Sneezes 8 73% 1 100% 13 81% 22 79% 0.66 Sore throat 7 64% 1 100% 5 31% 13 46% 0.13 Vomiting 3 27% 0 0% 7 44% 10 36% 0.48 *Fisher exact test for difference between proportions of RV-A and RV-C only

Cough was absent in a single case of subtype RV-C11 while nasal congestion was absent in a case of RV-C02, however, both subtypes were detected among other cases reporting these symptoms. Diarrhoea was the least reported symptom (14%; N=4/28). The limited number of RV-B isolates in the dataset precluded inclusion in subsequent statistical comparisons. Differences therefore between RV-A and RV-C species were considered only, however there

was no statistically significant difference between clinical symptoms by RV species (Table 2.2). Among the demographic dataset of 52 RV isolates, 34 cases

(65%; N=34/52) were under the age of two years. Within species, RV-A was isolated from 13 cases under two years (52%; N=13/25), one from RV-B (25%;

N=1/4), and 20 from RV-C (89%; N=20/23). Again, only RV-A and RV-C were tested for significant differences by demographic age group due to low numbers of RV-B isolated from cases under two years, and for consistency with the previous clinical analysis. In this case RV-C was significantly more likely to be isolated from cases under two compared to RV-A (p = 0.01).

2.4.3 Phylogenetic Analysis & Trait Association

Figure 2.1. Unrooted radial phylogenetic tree of RV-A and RV-C species and subtype coloured by age group with branch lengths equally constrained.

In each case, our positive control performed as expected, demonstrating significant phylogeny-trait associations by serotype for both PS and AI statistics

(Tables 2.3 and 2.4). Overall, no clinical symptom demonstrated significant phylogeny-trait associations between or within species. There was a significant association between the VP4/VP2 capsid protein and age group for the PS statistic (p < 0.01) but not AI statistic (p = 0.12). Figure 2.1 shows an unrooted maximum clade credibility tree generated from the Bayesian posterior tree distribution coloured by age group.

< 0.01 0.27 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.23 0.56

13.0) 12.7) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 5.00) 1.00) 5.00) 7.96) 7.00)

– ------

Null CI

(9.02 (7.99 (2.91 (3.00 (1.00 (3.00 (3.00 (3.00 (3.88 (1.00 (3.87 (5.14 (4.88

Combined 1.00) 11.0) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 5.00) 1.00) 5.00) 7.00) 6.00)

– – ------95% CI 95%

(1.00 (10.0 (3.00 (4.00 (1.00 (4.00 (4.00 (3.00 (4.00 (1.00 (4.00 (5.00 (5.00

1.00 10.37 3.69 4.00 1.00 4.00 4.76 3.77 4.77 1.00 4.76 6.43 5.89

< 0.01 0.01 0.99 0.99 0.99 0.99 0.99 0.99 0.15 0.99 0.99 0.28 0.46

13.4) 12.0) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 5.00) 1.00) 5.00) 7.97) 6.99)

------

Null CI (9.13 (9.06 (3.00 (3.01 (1.00 (3.00 (3.18 (3.05 (3.82 (1.00 (3.61 (4.97 (4.98

VP4/VP2 1.00) 9.00) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 4.00) 1.00) 5.00) 7.00) 6.00)

– – ------95% CI 95%

(1.00 (7.00 (3.00 (4.00 (1.00 (4.00 (4.00 (4.00 (4.00 (1.00 (4.00 (5.00 (6.00

PS 1.00 7.69 3.91 4.00 1.00 4.00 4.90 4.00 4.01 1.00 4.90 6.19 6.02 ) by genome region, case demographic and clinical symptoms. clinical and demographic case region, genome ) by

PS ( P

< 0.01 0.60 0.10 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.35 0.51

Score

12.98) 12.8) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 5.00) 1.00) 5.00) 8.00) 7.00)

------

Null CI (9.38 (8.85 (3.00 (3.00 (1.00 (3.00 (3.09 (3.00 (3.71 (1.00 (3.19 (4.94 (4.19 Parsimony

5’UTR

2.00) 11.0) 4.00) 4.00) 1.00) 4.00) 5.00) 4.00) 5.00) 1.00) 5.00) 7.00) 6.00)

– – ------95% CI 95% (2.00 (10.0 (3.00 (4.00 (1.00 (4.00 (5.00 (4.00 (5.00 (1.00 (5.00 (5.00 (5.00

PS 2.00 10.72 3.19 4.00 1.00 4.00 5.00 4.00 5.00 1.00 5.00 5.72 5.76

(N=21)

(N=35)

: Bayesian Posterior Phylogenetic Phylogenetic Posterior : Bayesian

(N=35)

2. Serotype Age Group Abdominal pain Chills Cough Diarrhoea Earache Lethargy Loss appetite Nasal Congestion Sneezes Sore throat Vomiting Table Control Demographic Clinical features

< 0.01 0.27 0.48 0.88 0.20 0.17 0.32 0.09 0.13 0.91 0.21 0.30 0.59

2.57) 2.54) 1.04) 1.05) 0.36) 1.04) 1.09) 1.22) 0.36) 1.24) 1.66) 1.46)

1.23) ------

- Null CI

(1.22 (1.09 (0.26 (0.38 (0.08 (0.41 (0.4 (0.33 (0.48 (0.08 (0.51 (0.73 (0.65

Combined 0.00) 1.76) 0.95) 1.09) 0.03) 0.92) 0.94) 0.62) 0.88) 0.34) 0.94) 1.38)

1.35) – ------

- 95% CI 95% (0.00 (1.27 (0.39 (0.75 (0.03 (0.46 (0.63 (0.37 (0.42 (0.34 (0.63 (0.68 (0.8

0.0 1.53 0.74 0.99 0.03 0.60 0.73 0.43 0.61 0.34 0.73 1.07 1.08

.01 P

< 0 0.12 0.47 0.45 0.10 0.22 0.48 0.55 0.16 0.65 0.51 0.29 0.80

2.7) 2.53) 1.16) 1.21) 0.36) 1.21) 1.41) 1.27) 1.39) 0.36) 1.31) 1.81) 1.61)

------

Null CI

(1.34 (1.32 (0.36 (0.39 (0.03 (0.38 (0.45 (0.38 (0.52 (0.10 (0.48 (0.59 (0.61

VP4/VP2 0.00) 1.77) 0.83) 1.08) 0.03) 0.85) 1.09) 1.04) 0.80) 0.35) 1.09) 1.34) 1.51)

– ------ome ome region, case demographic and clinical symptoms. 95% CI 95% (0.00 (1.21 (0.55 (0.60 (0.03 (0.30 (0.58 (0.47 (0.30 (0.33 (0.58 (0.76 (1.29

0.00 1.45 0.77 0.85 0.03 0.62 0.90 0.83 0.64 0.33 0.90 1.02 1.38

P < 0.01 0.41 0.10 0.84 0.40 0.19 0.27 0.27 0.25 0.91 0.23 0.40 0.58

2.64) 2.67) 1.17) 1.24) 0.36) 1.30) 1.37) 1.11) 1.39) 0.36) 1.35) 1.75) 1.65)

------

Null CI (1.34 (1.15 (0.32 (0.39 (0.03 (0.43 (0.45 (0.33 (0.51 (0.03 (0.50 (0.57 (0.76

5’UTR

0.08) 1.85) 0.81) 1.04) 0.03) 0.62) 0.79) 0.54) 0.83) 0.34) 0.79) 1.43) 1.25)

------95% CI 95% (0.08 (1.41 (0.39 (0.79 (0.03 (0.54 (0.71 (0.45 (0.55 (0.34 (0.71 (0.86 (1.00

78 .03 AI 0.08 1.66 0.55 1.01 0 0.61 0.78 0.53 0.71 0.34 0. 1.09 1.18

(N=21)

(N=35)

l pain

: Bayesian Posterior Phylogenetic Association Index (AI) by gen by (AI) Index Association Phylogenetic Posterior : Bayesian

(N=35)

2. Serotype Age Group Abdomina Chills Cough Diarrhoea Earache Lethargy Loss appetite Nasal Congestion Sneezes Sore throat Vomiting Table Control Demographic Clinical features

2.5 Discussion

In this study, we report and compare the molecular epidemiology and clinical features of a small sample of paediatric RV cases in Sydney, Australia.

Almost all RV species isolated were characterised as RV-A (48%; N=25/52) or

RV-C (44%; N=23/52) with very few RV-B (8%; N=4/52) cases observed. This is consistent with many molecular epidemiology studies on RV that demonstrate the predominant circulation of RV-A and RV-C species compared to RV-B

(Saraya et al. 2017; Arden et al. 2020). We found that RV-C was significantly more likely to be isolated from paediatric cases under two years of age compared to RV-A (p = 0.01). This significant difference was also apparent (p <

0.01) in one of two Bayesian phylogeny-trait association test statistics (PS not

AI) of the VP4/VP2 capsid gene (Tables 2.3 and 2.4). Previous studies have shown differences in the rates of RV infection by species among early and late childhood (Bochkov et al. 2018). Together with our results, this reinforces the potential for age-specific infectivity by RV species, particularly RV-C at younger age groups. The clustering of clinical features within RV subtypes in our dataset may also indicate associations with age of infection beyond the current species classification system, e.g. RV-C02, RV-A01 and RV-A20 in children under two years of age (Figure 2.1). Future studies could look to identify genetic markers exclusively observed among these subtypes which could be associated with susceptibility at younger ages. This could also include identifying the specific structural changes associated with any genetic markers, and their subsequent effect on viral traits. For this purpose, larger phylogenetic-trait association studies should be implemented.

In our study we examined a wide range of clinical RV features. The most frequent symptoms reported among the 28 cases with matching clinical data

were cough, nasal congestion and sneezes (Table 2.2). A variety of other symptoms such as loss of appetite, abdominal pain and vomiting were also reported in the minority. Previous studies have reported inconsistent results concerning the clinical presentation and severity between RV species (Huang, et al. 2009; Jin, et al. 2009; Piralla, et al. 2009; Moreira, et al. 2011; Lee, et al. 2012;

Lauinger, et al. 2013; Bruning, et al. 2015; Chen, et al. 2015; Martin, et al. 2015).

For example, in a sample of hospitalised patients in Taiwan, upper respiratory tract infection and nasal congestion was significantly higher in paediatric cases compared to adults (Hung et al. 2019). In other studies, exacerbations of asthma were often observed among cases of RV-A and RV-C, but not RV-B (Bizzintino et al. 2011; Zhao et al. 2016). Furthermore, between species, lower respiratory compromise and pneumonia has been commonly reported among cases of RV-

C but less so for RV-A (Linder et al. 2013). In other molecular epidemiology and phylogenetic studies (Tapparel et al. 2011), like ours, no significant difference in clinical features were observed between species (Table 2.2). These contrasting results mean it is difficult to determine conclusively the presence of epidemiological differences in the clinical presentation of RV species. Our approach to explore phylogeny-trait associations aimed to identify potential clustering by clinical traits within RV subtypes, however these results were also not significant (Tables 2.3 and 2.4). This suggests that potential variations in symptom presentation might be dependant of individual host-pathogen responses rather than exclusively viral markers (Makris and Johnston 2018).

Further research however is required to understand the role these factors play on clinical RV presentation.

Overall this study has a few limitations. Firstly, the small sample size and limited availability of matched clinical data means any significant associations between RV species and clinical features are potentially concealed. While some

differences were observed, e.g. cough was reported among 64% of RV-A cases compared to 31% in RV-C (Table 2.2) they did not reach significance (p = 0.13).

Our approach, particularly the unique application of clinical RV phylogeny-trait association testing within a Bayesian phylogenetic framework could be replicated across larger datasets containing match clinical data to overcome this in the future. Secondly, the clinical features recorded in our study could be attributable to coinfection with other viruses. Although RV continue to represent the most common aetiological agent of all acute respiratory tract infections across all age groups (Mäkelä, et al. 1998; Monto 2002), coinfection with other viruses such as respiratory syncytia virus (RSV) is common particularly among children (Aberle et al. 2005; Franz et al. 2010). While our study did not determine any significant associations between clinical presentation and RV species or subtype, any future study must consider the potential for any confounding due to viral coinfection. Finally, this study only incorporated the commonly sequenced 5’UTR and VP4/VP2 gene regions into our analysis. Translation of the approximately 7kb RV genome produces a single polyprotein which is cleaved to form mature capsid (VP1-4) and replication (2A-C, 3A-D) peptides (Palmenberg, et al. 2010). Studies have shown that the 5’UTR common among species within the Enterovirus genus have significant impacts on viral pathogenesis (Kawamura et al. 1989; Guest et al.

2004), however differences in clinical manifestation are also attributed to other translatable peptides and genome regions including the 3’UTR (Merkle et al.

2002). Additionally, mutations within the VP1 gene of EV-A71 are important determinants of pathogenesis in mice (Zaini et al. 2012), while VP1 and VP3 are critical for viral attachment of RV to nasal and bronchial epithelia (Blaas et al.

2016). Studies incorporating whole genome sequencing could be used to

uncover significant clinical associations between RV strains, with the aim of identifying genetic markers of severity in the future.

2.6 Conclusion

Our results show that RV-C is significantly more likely to be isolated from paediatric cases under two years of age compared to RV-A despite near equal predominance in circulation. These results were supported by Bayesian phylogenetic-trait association testing of the VP4/VP2 capsid protein suggesting genetic factors may influence the age-specific infectivity of RV species. No statistically significant difference in clinical symptom manifestation was detected between species or subtypes using phylogenetic methods, however the small sample size likely limited statistical power. These results add to the growing body of epidemiological evidence concerning RV. Improving surveillance and testing for RV, including routine whole genome sequencing may improve our understanding of the varied disease outcomes of RV species and subtypes.

Chapter Three

Bayesian Phylogeography and Pathogenic

Characterisation of Smallpox based on HA, ATI and CrmB genes

This chapter has been published in the journal Molecular Biology and

Evolution with the following citation:

Adam DC, Scotch M, MacIntyre CR. Bayesian Phylogeography and Pathogenic

Characterization of Smallpox Based on HA, ATI, and CrmB Genes. Molecular biology and evolution. 2018 Nov 1;35(11):2607-17.

This publication was a direct result of my research towards this PhD, and reproduction in this thesis does not breach copyright.

3.1 Chapter Abstract

Variola virus is at risk of re-emergence either through accidental release, bioterrorism or synthetic biology. The use of phylogenetics and phylogeography to support epidemic field response is expected to grow as sequencing technology becomes miniaturised, cheap and ubiquitous. In this study, we aimed to explore the use of common VARV diagnostic targets haemagglutinin (HA), cytokine response modifier B (CrmB) and A-type inclusion protein (ATI) for phylogenetic characterisation as well as the representativeness of modelling strategies in phylogeography to support epidemic response should smallpox re-emerge. We used Bayesian discrete-trait phylogeography using the most complete dataset currently available of whole genome (n=51) and partially sequenced (n=20) VARV isolates. We show that multi-locus models combining HA, ATI and CrmB genes may represent a useful heuristic to differentiate between VARV Major and subclades of VARV Minor which have been associated with variable case-fatality rates (CFR). Where whole genome sequencing is unavailable, phylogeography models of HA, ATI and

CrmB may provide preliminary but uncertain estimates of transmission, while supplementing whole genome models with additional isolates sequenced only for HA can improve sample representativeness, maintaining similar support for transmission relative to whole genome models. We have also provided empirical evidence delineating historic international VARV transmission using phylogeography. Due to the persistent threat of re-emergence, our results provide important research for smallpox epidemic preparedness in the post- eradication era as recommended by the World Health Organisation (WHO).

3.2 Introduction

Variola virus (VARV), is a large (~186kb), linear, double-stranded DNA virus of the Orthopoxvirus (OPV) genus (King et al. 2011), and the etiological agent of smallpox (Ledingham 1931). Smallpox is considered a disease of antiquity, however, its use as a bioterrorism agent has been debated for decades

(Henderson and Arita 2014). In 2017, scientists successfully completed the de novo synthesis of a poxvirus believed to be no-longer circulating in nature

(Tulman et al. 2006; Noyce et al. 2018), prompting fears of potential smallpox re- emergence through advances in synthetic biology (Koblentz 2017). Currently, there are 571 known VARV samples in two WHO authorized Collaborating

Centres in the United States of America (USA) and the Russian Federation

(Alcami et al. 2010). Despite strict regulations surrounding VARV research, insider threat, accidental release and unauthorised experimentation and storage of potential agents of bioterrorism are not without precedent (MacIntyre and

Engells 2016; Pillai 2016). Furthermore, the threat of synthetic biology means regulated containment or the destruction of remaining samples no longer represents a barrier or failsafe against future outbreaks (Damon et al. 2014;

MacIntyre 2015; Mitchell and Ellis 2017). A recent study modelling the impact of re-emergent smallpox has shown widespread infection among immunologically naïve persons as vaccination for VARV declined following eradication (MacIntyre et al. 2018). However, the highest mortality would occur in those aged > 45 years as vaccine induced immunity wains concurrent with unprecedented levels of iatrogenic and infectious disease-associated immunosuppression in contemporary society (MacIntyre, et al. 2018). As modern molecular methods and tools become more readily accessible and affordable, the threat posed by re-emergent smallpox cannot be ignored.

Prior to official eradication in 1980 (Behbehani 1983), VARV existed as two primary clades associated with differing mortality: high-mortality “major”

(>10% CFR) and low-mortality “minor” known as alastrim (<1% CFR) (Massung et al. 1995; Massung et al. 1996). Records are suggestive of a third clade common to West Africa in the late 20th century associated intermediate mortality (1-10%

CFR) (Shaffa 1972; Fenner, et al. 1988), and efforts were made in the late 1960’s to classify VARV into three taxonomic subspecies (Dumbell et al. 1961). Whole genome phylogenetic studies correlating VARV isolates with aggregate country

CFR further support the existence of a third clade common to Western Africa associated with intermediate mortality, however, there remains some phylogenetic disagreement between CFR within VARV major, particularly isolates from East Africa (Esposito et al. 2006). Diagnosis through laboratory confirmation requires the detection of VARV DNA using polymerase chain reaction (PCR). Common VARV specific nucleic acids targets include the haemagglutinin (HA), cytokine response modifier B (CrmB) and A-type inclusion protein (ATI), and can differentiate VARV from other OPV such as monkeypox and camelpox (Ropp et al. 1995; Meyer et al. 1997; Hansen et al.

2009; Kurth and Nitsche 2011; Okeke et al. 2012; Okeke et al. 2014). The potential for VARV differentiation by clade using reduced datasets including HA, ATI or

CrmB has yet to be explored, but could assist with future preparedness planning and outbreak response due to approximate associations by clade with CFR and therefore innate-viral pathogenesis (Esposito, et al. 2006; Li et al. 2007).

Understanding the use of genes like HA as a potential heuristic approximation to whole genome (WG) phylogeography may also be useful to support rapid real-time outbreak response in the field should smallpox re-emerge.

In this study, we aim to use Bayesian phylogenetic methods to explore the utility for characterisation of VARV based on diagnostic genes HA, ATI, and

CrmB as well as explore the phylogeographic signal available to each individual gene and in combination. We aim to describe the phylogeography of smallpox using all currently sequenced whole VARV genomes and investigate representative biases by supplementing WG models with additional isolates sequenced for HA only.

3.3 Materials & Methods

3.3.1 Compilation of Sequence Datasets

We performed a comprehensive search for all publicly available VARV gene and genome sequences. Of the 571 VARV samples remaining in the USA and Russia, 48 whole genome (WG) isolates have been sequenced and are available via GenBank. Recently, three additional VARV isolates have been uncovered from historic remains and their genomes sequenced (Duggan et al.

2016; Pajer et al. 2017; Porter et al. 2017) totalling 51 WG isolates (Supplementary

Table S3.1). We identified 53 GenBank records for VARV isolates sequenced for

HA. Cross referencing these 53 HA taxa to the 51 WG taxa with isolate records

(Alcami, et al. 2010) indicated that 31 HA taxa had duplicate sample sources with WG taxa, leaving 22 additional samples sequenced for HA but not yet as whole genomes. In order to maintain direct comparison between locations sampled between phylogeography models, two HA isolates, AF377891 and

AF377893, sampled from the US in 1927 and 1940, were excluded from the final analysis. This has the effect of concealing transmission to and from the US as no other isolates from the USA have been made publicly available. Table 3.3 details the count of the final dataset by region and period. Additional details of HA taxa exclusion can be seen in Supplementary Table S3.2, and breakdown of the final dataset by country and aggregated regions can be seen in the

Supplementary Table S3.3.

Two of the three additional WG isolates (LT706528 and LT706529) discovered in historic remains come from the Czech Republic, and the other from Lithuania (dated prior to the divergence between major and minor VARV clades). Medical records associated with these isolates were destroyed during

World War II meaning it is not possible to know the cause of death or condition of each respective case (Smrčka et al. 2009; Pajer, et al. 2017). Images present both specimens with discrete umbilicated pock lesions which are typical of ordinary and modified VARV major but also minor (Pajer, et al. 2017).

Therefore, it is insufficient to classify either on this basis also. The results of

Pajer, et al. 2017 classify LT706528 (V563) as VARV major and LT706529 (V1588) as alastrim minor (our results also mirror this). It is stated that V1588 has mutations D1705N within gene O1L, while the VARV major allocated V563 does not (Pajer, et al. 2017). This gene has been investigated as a biomarker for VARV pathogenesis (Smithson et al. 2014) and considering it’s basal position within the alastrim minor clade, this strain may have seeded Western African strains which later were imported into South America (De Jong 1956; Angulo 1976;

Fenner, et al. 1988; Li, et al. 2007; Pajer, et al. 2017).

3.3.2 Phylogenetic Characterisation

We used a Bayesian Markov Chain Monte Carlo procedure to characterise the phylogeny of the 51 heterochronously sampled VARV WG sequences as implemented in BEAST 1.8.4 (Drummond and Rambaut 2007;

Ayres et al. 2011). We extracted sequences coding for diagnostics genes HA, ATI and CrmB from WG VARV isolates using Geneious v10.1.2 (Kearse, et al. 2012).

Alignments were performed using MAFFT v7.3 (Katoh and Standley 2013) and visually inspected for errors. We used TempEst v1.5.1 (Rambaut, et al. 2016) to demonstrate the suitability of each gene and gene combination for temporal

analysis by a root-to-tip regression of genetic distance against sampling year

(Supplementary Tables S3.4 & S3.5), confirmed the strong temporal signal of

WG VARV (r2 = 0.714) as previously shown (Duggan, et al. 2016). For this, maximum likelihood (ML) trees were generated using RAxML v8.2 (Stamatakis

2014). Where available, exact dates of sample isolation was used (Alcami, et al.

2010). We set the sampling year for the three WG isolates sampled from historic remains (BK010317, LT706528 and LT706529) to 1654, 1929, and 1925 respectively based on previous work (Porter, et al. 2017). We specified a general- time-reversible substitution model with invariant sites and site rate heterogeneity modelled across four gamma distributions (GTR+I+Γ4). We compared these preliminary models using path sampling and stone stepping marginal likelihood estimation (Baele, et al. 2012; Baele et al. 2012). For final models we specified the same substitution model under a strict molecular clock and a constant coalescent tree prior based on model testing and previously published results (Duggan, et al. 2016; Porter, et al. 2017). We characterized sequences of HA, ATI and CrmB independently and in multi-locus partition models in various combinations to allow for independent rates of evolution across genes. We inspected log files for convergence and sufficient mixing using

Tracer 1.6 (Rambaut, et al. 2014) after removing 10% burn-in. We produced maximum clade credibility (MCC) trees using TreeAnnotator (Drummond and

Rambaut 2007) and visually inspected trees for incongruence using FigTree v1.4.2 (Rambaut 2009).

3.3.3 Phylogeography of Variola virus

For the phylogeographic analysis, we used BEAUti (Drummond and

Rambaut 2007) to additionally specify an asymmetric discrete-trait phylogeography model utilising a Bayesian stochastic search variable selection

(BSSVS) framework (Lemey et al. 2009) as a metric for comparing the geographic signal between datasets. For computational efficiency, we aggregated the 32 sampled countries into eight discrete regions in the asymmetric model as per

Supplementary Table S3.3. We calculated Bayes factors indicating transmission support using SpreaD3 v0.9.6 (Bielejec, et al. 2016). Support was defined as

Bayes factor >3 as per convention (Lemey, et al. 2009) with higher support interpreted according to Supplementary Table S3.8 (Jeffreys 1961). We generated phylogenetic plots using the ggtree library in R (Yu et al. 2017). For

WG datasets supplemented with additional HA isolates we specified the same

Bayesian discrete trait models in BEAUTi as previously described to further delineate the phylogeography and investigate representativeness compared to the WG-only models. As mentioned above, all but two of the additional HA sequences fell within the eight discrete regions previously specified and were therefore removed to maintain comparison between models. Additional details of the methods are described in the supplementary appendix.

3.4 Results

We estimated a mean genome-wide evolutionary rate of 1.20 x 10-5 substitutions per site per year, s/s/y (95% Bayesian credible interval = 1.12–

1.28 × 10-5 s/s/y). The mean rate of HA, ATI, and CrmB was 2.35 × 10-5, 1.30 × 10-5 and 2.42 × 10-5 s/s/y respectively. For single gene and multi-locus models, the time to most recent common ancestor (tMRCA) had significantly wider credible intervals (range 324 years to 414 years, median 334 -345 years) compared to the

WG (range 324 years to 335 years, median 328). Tip date sampling refined the estimated age of isolate BK010317 from 1654 to 1669 ACE (95% Bayesian credible interval = 1658 – 1676 ACE) with the addition of six whole genomes and exact dates of sampling not previously used for analysis in published results.

Figure 3.1: Time-rooted phylogenetic characterisation of whole genome and single-locus models of HA, ATI and CrmB genes using identical taxonomic datasets (n=51). Values on ancestral nodes represent posterior probabilities. Tip are coloured by sampling region and edges coloured by inferred ancestral origin.

Maximum Clade Credibility (MCC) trees using reduced datasets of HA,

ATI, or CrmB demonstrated various degrees of incongruence compared to the

WG model. Minor subclades could not be differentiated in any single gene model of HA, ATI, or CrmB (Figure 3.1). The topology of taxa within clades also varied across all single models compared to the WG. Temporally and spatially

related taxa grouped together yet the inferred ancestral relationships between groups of taxa differed between models. The posterior probability at most ancestral nodes was also reduced in each model when compared to WG models.

Figure 3.2: Time-rooted phylogenetic characterisation of multi-locus models of HA, ATI and CrmB genes using identical taxonomic datasets (n=51) extracted from isolates sequenced as WG. Values on ancestral nodes represent posterior probabilities. Tip are coloured by sampling region and edges coloured by inferred ancestral origin using a Bayesian Stochastic Search Variable Selection framework.

HA+ ATI+ CrmB 2 4 5 3 1 7 8 26 6 21

ATI+ CrmB 10 9 6 4 1 8 3 29 5 22

HA+ CrmB 8 2 5 4 1 9 7 18 6 19

HA+ ATI 1 2 6 3 9 7 50 23 4 22

Dataset

CrmB 10 6 4 7 1 8 3 23 5 21

ATI 2 12 5 1 13 7 17 27 3 24

HA 2 1 6 3 10 7 31 17 4 20

: cytokine response modifier B modifier response : cytokine WG 1 2 3 4 5 6 7 8 9 10 B Crm outes for VARV transmission between eight discrete regions from 1654 to 1977 .

type inclusion protein inclusion type

: ATI

To East Africa/Middle East Southern Asia Europe Latin America Southern Africa Western Africa Asia Pacific East Asia East Asia East Asia

From emagglutinin. a Europe Europe Europe h Transmission Route East Asia : HA Southern Asia Southern Asia Southern Asia Latin America Western Africa Ranked support out of a possible 56 asymmetric r

1. 3. East Africa/Middle East Table by locimodelled to compared the whole (WG). genome Supported routes in bold (Bayes Factor >3). WG: Whole genome.

Only multi-locus models combining two (HA and ATI) or three (HA, ATI, and CrmB) loci were able to differentiate between subclades (Figure 3.2), however in both final MCC trees, the monophyly of alastrim had low posterior support (0.4). The topology also varied within the minor clades of the multi- locus HA and ATI model compared to the WG model. Only models combining all three loci (HA, ATI and CrmB) generated sufficient statistical support to differentiate both minor subclades as well as the topology within each subclade as seen in the WG model, but the alastrim monophyly again had low posterior support (0.4). While topologies within the VARV Major clade varied between all models compared to the WG, in no instance was taxa characterised as VARV major in the WG model allocated to either minor subclade in the MCC tree of the reduced dataset models. In WG models supplemented with additional isolates sequenced for HA only, taxa were characterised alongside spatially and temporally related WG taxa, yet the posterior probability was reduced while the range on node heights widened (Figure 3.3B). In the final MCC tree, a monophyletic clade of HA-only isolates sampled between 1944 and 1946 from

East Asia and Europe is separate from all other East Asian and European clusters with related sampling times (posterior probability 0.8). In the final MCC tree using HA only sequences extracted from WG isolates and supplemented with additional HA isolates, this cluster remains (Supplementary Figure S3.1B).

Phylogeography analysis of all VARV taxa sequenced and available as whole genomes (n=51) revealed ten statistically supported (BF > 3) routes of transmission between eight discrete regions (56 possible asymmetric routes).

These are ranked by strength of support in Table 3.1. The highest support for transmission (considered decisive as BF > 100) occurred between Southern Asia and East Africa & the Middle East (BF = 4703.65), and the lowest (supported as

10 > BF > 3) from Western Africa to East Asia (BF = 3.48). Single and multi-locus

models of HA, ATI, and CrmB, supported the top six statistically supported routes from the WG model with variable strength and are also ranked in Table

3.1. Rankings were discordant between the reduced dataset models and the WG model. Route Southern Asia to Asia Pacific was supported (10 > BF > 3) only in

WG models (BF = 8.37) and all models incorporating CrmB with variable support

(BF = 6.59 to 24.15; strong support as 30 > BF > 100). Transmission route Latin

America to East Asia (BF = 4.11) and Western Africa to East Asia (BF = 3.48) was supported only in the WG model.

Table 3.2. Representativeness and ranked support out of a possible 56 asymmetric routes of VARV transmission between eight discrete regions by taxon dataset. Supported routes in bold (Bayes Factor >3) Dataset Transmission Route WG WG+20HA HA+20HA From To Southern Asia East Africa/Middle East 1 1 2

East Asia Southern Asia 2 2 1

Southern Asia Europe 3 5 7

Europe Latin America 4 4 3

East Africa/Middle East Southern Africa 5 10 10

Europe Western Africa 6 7 5

Southern Asia Asia Pacific 7 6 15

Latin America East Asia 8 23 18

Europe East Asia 9 3 6

Western Africa East Asia 10 22 16 WG: Whole genome taxonomic dataset (n=51). WG+20HA: Whole genome taxonomic dataset (n=51) supplemented with additional isolates sequenced for haemagglutinin only (n=20). HA+20HA: Haemagglutinin sequence extracted from whole genome isolates (n=51) supplemented with additional isolates sequenced for haemagglutinin only (n=20).

1.0 DQ441439/SOM77 0.2 DQ437581/BGD75 0.1 1.0 DQ441438/SOM77 DQ441422/BGD74 A B 0.8 DQ441421/BGD74 0.2 1.0 DQ437590/SOM77 DQ441420/BGD74 0.1 AF375136/BGD74/HA 1.0 DQ441424/ETH72 0.1 1.0 0.2 AF375140/BGD74/HA DQ441425/ETH72 Region 1.0 AF375125/BGD74/HA 1.0 DQ441440/SDN47 AF375134/BGD74/HA 0.6 DQ441441/SDN47 DQ437588/NPL73 0.7 DQ441446/UNK47 Asia Pacifc 1.0 1.0 DQ441418/BWA73 DQ437584/GER58 1.0 0.3 1.0 DQ441417/BWA72 DQ441417/BWA72 0.7 1.0 DQ441418/BWA73 DQ441436/ZAF65 0.6 DQ441436/ZAF65 East Africa / Middle East 0.5 DQ441435/ZAF65 DQ441435/ZAF65 0.7 0.2 DQ437583/COG70 0.7 DQ437583/COG70 1.0 0.4 DQ441423/COG70 East Asia 1.0 DQ441423/COG70 AF375131/COG70/HA 1.0 0.3 DQ441443/TZA65 0.4 DQ441443/TZA65 DQ441427/IND53 Europe 0.2 DQ441421/BGD74 0.7 AF375127/AFG71/HA 1.0 DQ441420/BGD74 0.2 AF375126/AFG72/HA 0.4 DQ441442/SUM70 0.2 DQ437581/BGD75 0.2 1.0 1.0 DQ437591/SUM70 Southern Africa DQ441422/BGD74 0.4 AF375128/IND58/HA 1.0 DQ437588/NPL73 AF375144/UNK46/HA 1.0 DQ441445/UNK46 DQ437584/GER58 0.9 Southern Asia 0.8 0.6 DQ437592/SYR72 DQ441446/UNK47 0.9 DQ437587/IRN72 1.0 DQ441427/IND53 1.0 DQ441448/YUG72 Major DQ437589/PAK69 Latin America 0.9 Major 1.0 DQ441442/SUM70 DQ437580/AFG70 0.6 0.6 DQ437591/SUM70 DQ441433/KWT67 1.0 DQ437585/IND64 DQ441445/UNK46 Western Africa 1.0 DQ437586/IND64 0.6 0.6 1.0 DQ437587/IRN72 AF375137/IND62/HA 0.9 1.0 DQ441428/IND53 DQ437592/SYR72 1.0 1.0 DQ441429/JPN46 DQ441448/YUG72 DQ441431/JPN51 1.0 DQ437589/PAK69 0.8 DQ441430/JPN51 1.0 0.8 DQ441432/KOR47 1.0 DQ437580/AFG70 0.9 0.7 DQ437582/CHN48 1.0 DQ441433/KWT67 0.7 LT706528/CZE25 DQ441444/UNK44 1.0 DQ437586/IND64 1.0 0.3 DQ437590/SOM77 DQ437585/IND64 1.0 AF375143/SOM77/HA 1.0 X69198/IND67 0.9 DQ441438/SOM77 0.7 0.8 DQ441439/SOM77 DQ441444/UNK44 1.0 DQ441424/ETH72 LT706528/CZE25 0.8 DQ441425/ETH72 P-I 1.0 0.4 AF375135/TZA65/HA 1.0 DQ441430/JPN51 1.0 DQ441441/SDN47 1.0 DQ441431/JPN51 1.0 DQ441440/SDN47 X69198/IND67 1.0 DQ441429/JPN46 0.6 0.2 AF377892/KOR46/HA DQ441428/IND53 0.4 AF377889/CHN45/HA 1.0 1.0 DQ441432/KOR47 AF377887/JPN44/HA 1.0 AF377890/BEL47/HA DQ437582/CHN48 0.8 1.0 AF377888/JPN46/HA 1.0 DQ441419/BRZ66 1.0 Y16780/BRZ66 0.2 AF375130/BRZ68/HA 1.0 Y16780/BRZ66 1.0 DQ441447/UNK52 1.0 1.0 DQ441419/BRZ66

1.0 LT706529/CZE29 Alastrim DQ441447/UNK52 1.0 1.0 LT706529/CZE29 1.0 DQ441426/GIN69 Alastrim 1.0 0.5 AF375142/SLE68/HA P-II 1.0 DQ441437/SLE68 1.0 DQ441437/SLE68 1.0 0.8 DQ441416/BEN68 0.8 DQ441426/GIN69 DQ441416/BEN68 Minor 1.0 DQ441434/NER69 AF375138/NIG61/HA BK010317/LTH54 BK010317/LTH54 DQ441434/NER69 Minor

1600 1650 1700 1750 1800 1850 1900 1950 2000 2050 2100 1600 1650 1700 1750 1800 1850 1900 1950 2000 2050 2100 Divergence time Divergence time

Figure 3.3: Time-rooted phylogenetic and phylogeographic characterisation of VARV isolates. Values on ancestral nodes represent posterior probabilities. Tip names are coloured by sampling region and edges coloured by region state. (A) Fifty-one WG VARV isolates (B) Fifty-one WG VARV isolates aligned with 20 additional isolates sequenced for HA only. HA-only taxa are suffixed with “HA”. A single monophyletic clade of HA-only taxa of East Asian/European origin is indicated with an arrow. Red bars indicate uncertainties in node height.

In WG models supplemented with additional isolates sequenced for HA

(WG+20HA), some differences in transmission support can be observed when compared to WG models (Table 3.2). Top supported routes Southern Asia to

East Africa & the Middle East and East Asia to Southern Asia remained

decisively supported (BF = 2207.14 and 118.42 respectively) across all datasets.

Supported routes Latin America to East Asia (BF=4.11) and Western Africa to

East Asia (BF=3.48) in the WG-only model were not significant (3 > BF) in the

HA supplemented model (BF=1.42 and 1.56 respectively). Supported route

Europe to East Asia in the WG model increased to very strong support (100 > BF

> 30), or from rank nine to rank three when supplemented with additional HA

(BF = 4.07 to BF = 68.75 respectively). Route East Africa & Middle East to

Southern Africa was reduced from rank five (BF = 82.31) to rank ten (BF = 5.29).

Absolute Bayes Factor values for each route and model are available in

Supplementary Tables S3.6 & S3.7.

Figure 3.4: Projection between eight discrete regions using Bayesian phylogeography coloured by sampling time (1654 and 1977) based on WG+20HA taxon dataset. Direction is indicated by arrows. Southern Asia is shown to be a modern hot spot for historic international transmission to Europe, Eastern Africa and the Middle East, Southern Africa and the Asia Pacific.

Figure 3.4 shows the spatial projection of VARV based on the WG model supplemented with additional HA isolates (WG+20HA). Considerable transmission originating from Southern Asia in the late 20th century can be

observed. Consistent with our Bayes Factor analysis in Table 3.2, Southern Asia represents one of only two discrete origin locations most frequently supported

(n=3), the second being Europe. Similarly, consistent Bayes Factor support for the wide dissemination of VARV from Europe to Latin America, East Asia and

Western Africa in Table 3.2 can be observed in Figure 3.4 with transmission occurring between the 17th and 20th century.

Table 3.3. Count of taxa included for analysis by aggregate region and time period Region Count (WG) Count (HA) Total Before After Before After Total Total 1966 1966 1966 1966 Asia Pacific 2 0 2 0 0 0 2 Eastern Asia 5 5 0 4 4 0 9 East Africa & Middle East 11 3 8 2 1 1 13 Europe 9 8 1 2 2 0 11 Latin America 3 0 3 1 0 1 4 Southern Africa 6 2 4 1 0 1 7 Southern Asia 11 4 7 8 2 6 19 Western Africa 4 0 4 2 1 1 6 Total 51 22 29 20 10 10 71

3.5 Discussion

We have shown that VARV phylogenies based on reduced genetic datasets of HA, ATI or CrmB can characterise historic VARV major isolates, and models combining HA and ATI or HA, ATI and CrmB can further characterise minor subclades. Discrete-trait phylogeography has also empirically described historic routes of international smallpox transmission for the first time, and transmission events unresolved in models using currently available WG isolates are observed when supplemented with additional isolates sequenced for HA

only. As the future promise of real-time phylogenetic analysis in the field supported by portable direct DNA sequencing technology begins to take shape

(Faria et al. 2016; Gardy and Loman 2018), this is of diagnostic significance in the event of re-emergent smallpox as minimal genomic material could be promptly sequenced to support a rapid outbreak response. These results likely owe to VARV’s low inter-genomic sequence diversity (Esposito, et al. 2006) meaning a high degree of precision is available to infer the relationship between epidemiologically linked isolates. Historic aggregate population CFR offer a reasonable but imperfect approximation of innate VARV pathogenesis by clade, as these estimates are known to be modified by the immunisation status and specific age groups of affected persons (Esposito, et al. 2006). However, as public health surveillance and epidemic field response are expected to increasingly rely on genomics in the future (Baele et al. 2016; Dellicour et al. 2017; Gardy and

Loman 2018), understanding the potential for genes like HA, ATI and CrmB to characterise VARV roughly according to pathogenesis could complement traditional laboratory methods (Loveless et al. 2009). Furthermore, an understanding of the relative advantages and limitations of phylogeography approaches to modelling VARV transmission is important should smallpox re- emerge.

The use of real-time phylogeography for outbreak response is an emerging concept but has yet to be applied during an epidemic. Whole genome

Bayesian phylogeography of the 2014-15 Ebola outbreak in West Africa revealed significant transmission events between contiguous Guinea, Liberia and Sierra

Leone that sustained the length of the epidemic (Dudas et al. 2017). It has been suggested that the real-time identification of these transmission chains could have improved response plans and potentially reduced the overall impact of the outbreak (Holmes et al. 2016). Others have used phylogeography to examine the

emergence and spread Zika virus across Africa and Asia (Faye et al. 2014).

However, in time-sensitive outbreak settings, whole genomes may be unavailable due to limited sequencing capacity (Helmy et al. 2016). In the case of VARV, WG sequencing may also be impractical considering the size of the genome (Gilchrist et al. 2015), so rapidly sequencing diagnostic genes HA, ATI and CrmB using standardised diagnostic protocols may provide a timely advantage for VARV characterisation and the identification of transmission using phylogeography. Based on our results, models using HA, ATI and CrmB could provide preliminary estimates of transmission to support response planning, however, decision makers should note the potential for reduced statistical support compared to WG models. Single gene models based on HA,

ATI or CrmB supported at least seven out of ten transmission routes supported in WG models from the same taxonomic dataset, although rankings were discordant (Table 3.1). Multi-locus models combining HA, ATI and CrmB genes support eight out of ten WG supported routes and discordance was reduced.

Discordance between models however may be the result of recombination between VARV (Smithson et al. 2014; Babkin and Babkina 2015), a disadvantage which could misinform response plans. In the event of recombination, multi- locus models could therefore prove advantageous over WG models.

A combination of models using both WG and diagnostic genes such as

HA like that shown in Figure 3.3B could provide both timely information that reveals additional transmission events if the representativeness of the sample improves. In our study, when WG-only samples were supplemented with additional VARV taxa sequenced for HA-only, statistical support for transmission from Europe to East Asia increased from rank nine to rank three

(Table 3.2). This increase is likely due to the appearance of two separate clusters of East Asian/European taxa, one of which contains HA-only taxa (Figure 3.3B

arrow). Of note, a taxon (AF377887/JPN44/HA) within this cluster is described as “Skin Lesion WWII” (Supplementary Table S3.2). Historic reports describe the movement of smallpox from East Asia to Europe when infected soldiers were repatriated in 1944 following the end of WWII (Fenner, et al. 1988). The appearance of these clusters provides some level of support (posterior probability 0.8) of at least two traceable lineages of smallpox circulating between

East Asia and Europe following WWII. This approach of maximizing the sample size of phylogeography models by supplementing samples of WG-only taxa with isolates sequenced for HA-only may have improved the relative geographic and temporal representativeness of the sample, as these lineages were not observed in the smaller sample using isolates sequenced only for whole genomes (Figure 3.3A).

Whilst this approach appears to resolve additional transmission events unseen in the WG-only models, the posterior probability on many other ancestral nodes within VARV Major in particular was reduced, and the node heights widened (Figure 3.3B). This may be a critical limitation to the application of this approach, as even the most probable transmission events identified at some nodes may still be highly uncertain due to the introduction of large amounts of missing data into the alignment. The potential for misinformed estimates of transmission therefore increases with this uncertainty, meaning in some cases, determining the true origins of some transmission events may be difficult. In such instances, preference might be given to the Bayes factor analyses (Table 3.2), which considers the statistical uncertainty of ancestral location states at each node, rather than limiting visualisation to the most probable transmission events only (Figures 3.3 and 3.4). For example, we show that despite incongruent topologies, eight out of ten WG supported transmission routes were statistically supported in the supplemented model

(WG+20HA), and the top two were identically ranked (Table 3.2), meaning sufficient data remained within nodes to identify these routes out of the possible

56 in most instances of the model. In practice, these results should ideally be interpreted in contrast to statistically stronger WG-only models. As outbreaks progress, preliminary models might be established using genes like as HA, ATI and CrmB, and later replaced when whole genomes become available, therefore increasing certainty and supporting adaptive response planning. Studies examining the phylogeny of cowpox viruses however have shown distinct topologies can be generated from different genomic regions, sometimes with a high degree of statistical support; another caveat to this approach (Dabrowski et al. 2013; Mauldin et al. 2017). This means that phylogeography models using

HA, ATI and CrmB could produce incorrect or discordant results (occasionally with high statistical support) relative to WG analyses. A degree of caution is therefore warranted if first interpreting the results of phylogeography based reduced datasets like HA, ATI and CrmB without considering the potential for distinct topologies with high support.

The primary limitation of this study is the small sample size of VARV sequences available for analysis. Of the 571 VARV isolates currently stored in the USA and the Russian Federation, 334 have date and location of sampling metadata (Alcami, et al. 2010), meaning they could potentially be used for future phylogeography studies if sequenced and made available for analysis. As additional VARV genomes have been uncovered in recent years outside of these collections, the results of previous phylogenetic studies have been contested (Li, et al. 2007; Duggan, et al. 2016; Porter, et al. 2017). We therefore cannot claim our results to be truly representative of past transmission when additional isolates remain un-sequenced, only relative to the current gold-standard using available WG data. Even if all additional isolates were sequenced and available

for analysis, the sample could still be unrepresentative due to sampling bias. For example, the well-documented 1972 outbreak of VARV major in Yugoslavia following transmission from Iraq (Čobeljić 2004) is absent in Figure 3.4. The strain circulating in Iraq at the time is believed to have been imported from Iran in 1971, itself imported from Afghanistan in 1970 (Tucker 2002). Figure 3.4 confirm this overall narrative, visualising recent transmission from Southern

Asia (Afghanistan) to Europe (Yugoslavia), however, as samples from Iraq are no longer stored or were never taken (Alcami, et al. 2010), transmission via the

Middle East will remain unresolved in any future phylogeographic analysis.

Another consequence of unrepresentative sampling can also be seen in Figure

3.4, which shows transmission of minor Alastrim strains from Europe to Latin

America in 1952. In this instance however, the reverse is true, as the European strain DQ441447/UNK52 is known to have been imported from Latin America

(Fenner, et al. 1988). Because isolates taken from Latin American prior to 1952 do not exist (Alcami, et al. 2010), our analysis, and future analyses may still infer the direction incorrectly. Beside these notable cases, the sample is roughly consistent with the known spatial and temporal epidemiology of VARV at the time. In our study, just over half of all sequences (n=39/71; 55%) were sampled between 1966 and 1980 during the WHO intensified smallpox eradication campaign when VARV circulation was most intense in Africa and Southern Asia

(Fenner, et al. 1988). Compared to other regions included in our analysis, a clear majority of sequences are sampled from Southern Asia and African & Middle

Eastern countries (Table 3.3), most of which were sampled after 1966, 86%

(n=13/19) and 73% (19/26) respectively. For sequences sampled prior to 1966, most (n=19/33; 58%) come from Europe and East Asia when circulation and outbreaks were present, however other regions are still well represented.

Finally, the generalisability of our approach to other emerging VARV specific diagnostic targets such as 14kDa (Olson et al. 2004), 39kDa, and A36R

(Kondas et al. 2015) is an important consideration. Sequence datasets analysed under time-measured Bayesian phylogenetic assumptions such as phylogeography must contain a measurable accumulation of mutations over time i.e. a temporal signal (Baele et al. 2016). This is necessary to infer the relative similarity of related VARV sequence and subsequent associations to epidemiological processes such as spatial transmission. Sequences such as

14kDa, 39kDa, and A36R however are remarkably conserved (Olson, et al. 2004;

Kondas, et al. 2015), such that the ability to differentiate between unrelated taxa sampled over time could be difficult. As such, the generalisability of this approach is limited to targets that can demonstrate sufficient temporal signal.

Considering this and the potential for recombination within OPV species

(Smithson et al. 2014; Babkin and Babkina 2015), future studies could investigate the utility of other diagnostic genes in both single and multi-locus combinations with HA, ATI and CrmB for characterisation and phylogeography.

Overall, our results utilise the most comprehensive taxon dataset publicly available to date to explore VARV phylogeny, and the first to statistically model historic spread using Bayesian phylogeography. The methods used here have demonstrated the potential utility for the rapid characterisation of VARV strains based on diagnostic genes HA, ATI and CrmB, as well as the exploration of VARV transmission which may be of use in outbreak situations should VARV re-emerge in the future.

3.6 Conclusion

Our results show Bayesian multi-locus phylogenetic models combining

VARV genes HA, ATI and Crmb can differentiate between VARV major and

minor subclades roughly associated with high, intermediate and low CFR suggesting they may be used as indicators of potential mortality in future outbreaks. Multi-locus Phylogeography models combining HA, ATI and CrmB discordantly supported eight out of ten transmission routes supported by WG models and might be considered preliminary to inform outbreak response plans when whole genomes are unavailable. Increasing sample representativeness by supplementing WG phylogeography models of VARV with taxa sequenced for

HA only could also be used to resolve additional transmission events compared to WG models alone, however, with increasing uncertainty. The methods used here are also the first to empirically describe global transmission of historic

VARV isolates using phylogeography revealing two discrete lineages of VARV

Major circulating between East Asia and Europe following WWII.

Understanding phylogenetic and phylogeography modelling strategies for the rapid characterisation of VARV is critical to support future preparedness plans and public health policy for epidemic response should smallpox re-emerge in the future.

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Chapter Four

The Phylogeography of Seasonal Influenza

A/H3N2 within and between Australia and New

Zealand

This chapter has been prepared for publication and includes the following co- authors: Moa A, Costantino V, Scotch M, MacIntyre CR. This study was a direct result of my research towards this PhD, and reproduction in this thesis will not breach copyright upon publication.

4.1 Chapter Abstract

In 2017, the A/H3N2 influenza season in Australia was both severe and widespread. New Zealand however experienced a mild A/H3N2 season despite high levels of travel between the two countries. We used Bayesian phylogenetic methods and models to uncover the regional dynamics of A/H3N2 within and between Australian and New Zealand. We found a wide yet similar diversity of

A/H3N2 circulating within Australia and New Zealand throughout the year.

Frequent reciprocal transmission along the southeast coast between the

Australian states of New South Wales, Victoria, and South Australia was observed, while very strong evidence linked transmission from Australia to

New Zealand via New South Wales. There was no evidence supporting transmission from New Zealand to Australia in 2017. These results largely exclude viral factors as potentially explaining the differences in observed severity. Our novel insights into the dynamics of A/H3N2 in the region have important public health implications, particularly future vaccine composition recommendations and preparedness activities. Future studies investigating the international role of importation and exportation of A/H3N2 warrants further study.

4.2 Introduction

It is estimated that up to 645,000 deaths (8.8 per 100,000 population) occur each year globally due to seasonal influenza (Iuliano, et al. 2018). Among seasonally circulating strains, influenza A H3N2 (A/H3N2) is often associated with increased disease severity (Hayward, et al. 2014). Excluding pandemic years, influenza-associated hospitalization and death is highest among adults aged 65 years and over (Simonsen, et al. 2005) and children under 5 years (Nair, et al. 2011). In Australia and New Zealand, seasonal influenza epidemics typically occur between May and October each year in temperate and cool- temperate regions (Tay et al. 2013), however sporadic cases do occur continuously throughout the inter-seasonal period (November - April), particularly in the tropics (Moa et al. 2019).

In 2017, Australia experienced the highest level of influenza activity and associated hospital admissions of confirmed influenza since the 2009 pandemic year (Cheng et al. 2019). In contrast, New Zealand experienced a relatively low- level of seasonal activity (Huang et al. 2017). Both Australian and New Zealand seasons however were predominately A/H3N2 and adjusted vaccine effectiveness (VE) against A/H3N2 strains was similarly low: 8% VE in New

Zealand (Huang, et al. 2017), and 10% in Australia (Sullivan et al. 2017). This was compared to roughly 50-60% VE against co-circulating A/H1N1pdm09 and influenza B viruses respectively between the two countries (Huang, et al. 2017;

Sullivan, et al. 2017). Differences in observed seasonal activity occurred despite equally high levels of travel between the two countries: roughly 1.5 million travellers from Australia visit New Zealand each year compared to 1.4 million visitors coming from New Zealand. There was also some geographic variation in influenza activity within Australia, with Western Australia experiencing

activity similar to a typical influenza season in contrast to the high-level of activity observed elsewhere (Baele, et al. 2012) despite strong transport connections between major Australian cities across the continent.

The dynamics and variations in seasonal A/H3N2 activity within and between Australia and New Zealand have yet to be explored in the literature.

Since Northern Hemisphere influenza vaccine composition recommendations typically include viruses that circulated in Australia during the preceding year, and countries in the Northern Hemisphere typically anticipate increased influenza activity following a severe Southern Hemisphere season, understanding the dynamics and circulation of A/H3N2 in the region could assist in anticipating succeeding seasonal influenza activity and severity around the world.

Aims: To understand the circulation and transmission dynamics of

A/H3N2 within and between Australian and New Zealand using the 2017 season as a case study.

4.3 Materials & Methods

4.3.1 Compilation of Sequence Dataset & Phylogenetic Characterisation

We searched the Global Initiative for the Sharing All Influenza Data

(GISAID) (Shu and McCauley 2017) for all available A/H3N2 isolates with both haemagglutinin (HA) and neuraminidase (NA) gene sequences collected in

Australia and New Zealand between 1 January and 31 December 2017. We found 1,110 non-duplicate records with both full-length HA and NA gene sequences (Dataset 1) and complete sampling date and sampling location metadata out of 1,257 records (Appendix B). In Table 4.1, we show the distribution of A/H3N2 isolates collected by month and aggregate location. We

further collected 33 A/H3N2 reference strains sequenced for HA with known clade designations according to World Health Organisation (WHO), including two vaccine strains (A/Hong Kong/4801/2014 and A/Singapore/INFIMH-16-

0019/2016) from GISAID. We aligned the reference strains with the 1,110 HA genes previously collected (Total N=1,143) using MAFFT v1.3.7 (Katoh and

Standley 2013) and manually removed non-coding bases from 5’ and 3’ ends in

Geneious Prime v2019.1.1 (Kearse, et al. 2012). We constructed a Maximum

Likelihood phylogenetic tree using RAxML v8.4 (Stamatakis 2014) to characterise clade membership, annotating taxa with FigTree v1.4.4 (Lemey et al.). We determined residue frequencies at notable sites using Geneious v10.1.2

(Kearse, et al. 2012). Unless indicated (such as 3C.3A), we abbreviate 3C.2A clade names onwards by dropping 3C.2 e.g. 3C.2A1 to A1.

Table 4.1. Distribution of A/H3N2 sequences collected in 2017 in Australia and New Zealand by month and location of isolation. Month

Country/Location Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Australia 54 39 102 76 48 90 207 158 166 40 14 14 1,008 ACT 1 - 1 2 - 3 10 8 6 - - - 31 NSW 12 9 60 31 3 20 30 35 19 3 3 3 228 NT 6 12 8 1 2 - 1 1 - - - - 31 QLD 14 3 11 9 6 1 9 22 7 9 8 3 102 SA 15 13 18 22 31 40 16 5 24 2 1 8 195 TAS 4 1 - - - - 30 11 12 9 - - 67 VIC 1 - 1 9 2 23 103 70 88 9 - - 306 WA 1 1 3 2 4 3 8 6 10 8 2 - 48 New Zealand 3 - 14 19 4 12 37 4 5 3 - - 102 NI 3 - 13 19 4 3 36 3 3 - - - 84 SI - - 1 - - 9 1 1 2 3 - - 18 Total 57 39 116 95 52 102 245 162 171 43 14 14 1,110 Abbreviations: Australian Capital Territory (ACT), New South Wales (NSW), Northern Territory (NT), Queensland (QLD), South Australia (SA), Tasmania (TAS), Victoria (VIC), Western Australia (WA), North Island (NI), South Island (SI). Dash (-) indicates zero to improve legibility.

For subsequent Bayesian phylogenetic analyses, we generated two additional data subsets from Dataset 1 to control for potential sampling bias. We

generated Dataset 2 by randomly subsampling 20 taxa from location-months with greater than 20 taxa to create a more equitable spatiotemporal distribution of sequences across the study period. This removed sequences from New South

Wales (N=74), Queensland (N=2), South Australia (N=37), Tasmania (N=10),

Victoria (N=204), and the North Island of New Zealand (N=16) resulting in a dataset totalling 735 taxa (Supplementary Table S4.1) or 66.2% of Dataset 1

(N=735/1,110). We generated Dataset 3 by first removing all duplicate HA and

NA nucleotide sequences within each discretised location (duplicates between locations were still possible and not removed) resulting in 508 unique HA and

NA sequences by location that shared the same isolate source i.e. both HA and

NA isolated from the same case in time (Supplementary Table S4.2). We further subsampled these sequences randomly by location-month to evenly distribute samples across the study period, this time subsampling 15 taxa from location- months with greater than 15 taxa. In this case, only sequences from New South

Wales (N=18) and Victoria (N=53) were removed and resulted in a dataset totalling 437 taxa (Supplementary Table S4.3) or 39.4% (N=437/1,110) of Dataset

1. For all three Datasets, samples from Australian Capital Territory (ACT) in

Australia were grouped with samples from New South Wales (NSW) were combined due to low sampling frequencies and regional proximity. We aligned each sequence Dataset using MAFFT v1.3.7 (Katoh and Standley 2013) and manually inspected and removed non-coding bases in Geneious Prime v2019.1.1 as before (Kearse, et al. 2012).

4.3.2 Discrete-Trait Phylogeography

We characterised transmission within and between Australia and New

Zealand using an asymmetric discrete-state continuous time Markov chain

(CTMC) model as per Lemey et al. (Lemey, et al. 2009). We determined a

sufficient but low correlation between the sampling time and root-to-tip divergence of both HA (R2 = 0.11) and NA (R2 = 0.08) using Tempest v1.5.1

(Rambaut, et al. 2016) (Supplementary Figure S4.1) which indicated support for subsequent time-scaled phylogenetic analysis. We generated posterior phylogenetic distributions using BEAST v1.10.4 (Suchard, et al. 2018) incorporating both HA and NA together in a multi-locus partition model to allow for independent rates of evolution. We used path sampling and stepping- stone sampling to test a range of substitution models, molecular clocks and demographic tree priors (Baele et al. 2012; Baele, et al. 2012). We found some disagreement between the HA and NA datasets which fit constant and exponential tree priors respectively better over nonparametric Skygrid tree priors (Supplementary Table S4.4). In each case, a strict molecular clock and

GTR+Γ4 nucleotide substitution model was preferred over the HKY+Γ4 and relaxed molecular clock models (uncorrelated lognormal prior). For simplicity and to reduce the potential for over parametrisation, we specified constant tree priors for all subsequent analyses. All models tested specified a two-partition

(1+2)3) codon structure (Yang 1994). We estimated transition rates between the discretised states and territories of Australia and two major islands of New

Zealand using a non-reversible (asymmetric) transition model consisting of 72 rate parameters (all possible asymmetric pair-wise routes) each with individual indicator variables as per the Bayesian stochastic search variable selection

(BSSVS) framework (Lemey, et al. 2009) available in BEAST v1.10.4 (Suchard, et al. 2018). We also explored differences in phylogeographic signals between HA and NA genes by additionally specifying independent BSSVS models for

Dataset 2 only. We ran each model twice with 150 million Markov Chain Monte

Carlo (MCMC) generations sampling every 15,000 steps for Dataset 1, 100 million generations sampling every 10,000 steps for Dataset 2, and 50 million

generations sampling every 5,000 steps for Dataset 3. We inspected each output independently for convergence and sufficient sampling from the posterior (ESS

> 200) using Tracer v1.6 (Rambaut, et al. 2014) before combining runs in

LogCombiner 10.0.4 (Suchard, et al. 2018) and removing the first 10% from each as burn-in. We produced MCC trees using TreeAnnotator v1.10.4 (Suchard, et al. 2018) specifying median node heights and calculated statistical support for all possible pair-wise transmission routes as Bayes factors and to render phylogeographic projections using SpreaD3 v0.9.6 (Bielejec, et al. 2016). We defined sufficient support for transmission as Bayes factor >3.0 as per convention (Lemey, et al. 2009) with higher levels of support defined as per

Supplementary Table S4.5. We used the ggtree v1.10.5 (Yu, et al. 2017) package in R v 3.4.4 to visualise the MCC trees.

4.3.3 Generalised Linear Modelling

We quantified the effect of various ecological and demographic factors as potential predictors of transmission by extending the multi-locus partition models above using a generalised linear modelling (GLM) framework as per

(Lemey, et al. 2014). Preliminary factors of interest included longitude, latitude, annual mean temperature and precipitation, state/island population density, state/island percentage population growth, gross state/island product (GSP), capital population density, capital percentage population growth. We assessed each factor for potential collinearity issues using R v.3.4.4, settling on five final predictors: (i) capital population density, (ii) capital percentage population growth, (iii) gross state/island product (GSP), and (iv) annual mean temperature and (v) precipitation. We were also interested in evaluating the presence of oseltamivir resistance markers in N2 on transmission, however no commonly described markers (E119V, Q136K, N142S, R292K) (Hussain et al. 2017) were identified in our dataset, and therefore could not be assessed as a potential

factor. We included aviation passenger flux as a factor by calculating the total number of domestic and international passengers travelling within and between all commercial airports in Australia and New Zealand in 2017 using data obtained from the Australian Bureau of Infrastructure, Transport and Regional

Economics and the Civil Aviation Authority of New Zealand. We excluded passenger counts for travel between airports located within the same state of

Australia or island of New Zealand. We also included great circle distance between each pair-wise route and sample size by state/island per dataset to assess for sampling effects. The effect of each factor on transmission, excluding great circle distance, was evaluated as a predictor by origin location, and destination, resulting in 15 factors total. We ran each GLM model twice as before testing both HA and NA genes combined as a single multi-locus partition model across all three Datasets. We calculated each predictors posterior probability of inclusion in the model, Bayes Factor support (assuming a 50% prior probability of inclusion), and the effect size using R as per (Lemey, et al. 2009).

4.4 Results

4.4.1 Differences & Similarities in A/H3N2 Clade Composition

Phylogenetic characterisation of A/H3N2 clades circulating between

Australia and New Zealand showed some differences and similarities both within countries (Table 4.2) and between them over time (Figure 4.1). We found that all A/H3N2 viruses in our dataset belonged to the parent clade 3C, with the overwhelming majority (99.5%; N=1,105/1,110) included within the subgroups of clade 3C.2A (onwards 2A). Only a single cluster of five taxa sampled from

Newcastle in NSW, Australia, between 21 March and 6 June were characterised as belonging to clade 3C.3A. When aggregated, subclades A1b and A4 constituted the majority genetic groups characterised among the sampled

A/H3N2 taxa in Australia and New Zealand in 2017 representing 34.4%

(N=347/1008) and 23.8% (N=240/1008) in Australia respectively, and both equally (each 30.4%; N=31/102) in New Zealand (Table 4.2). Differences however could be seen within countries. For example, the majority (50.7%; N=34/64) of taxa sampled in Tasmania (TAS), a large island state south of the Australian continent, belonged to clade A2 while almost none (4.5%; N=3/67) were characterised within clade A4. Similarly, in the Northern Territory (NT), the overwhelming majority of samples (90.3%; N=28/31) belonged to clade A3

(Table 4.2) and none from A4, while in Queensland (QLD), a near equal predominance of A1b and A2, A3, and A4 subclades was identified. Between the major islands of New Zealand and among the limited taxa available for analysis, few noteworthy differences could be observed relative to the aggregate national data (Table 4.2).

Using sequenced taxa as a proxy for observed cases over time, we can reconstruct corresponding epidemic curves by clade for the 2017 A/H3N2 season in both Australia and New Zealand. We show a bimodal epidemic curve in Australia (Figure 4.1A) and New Zealand (Figure 4.1B) over the course of

2017, first reaching an early inter-seasonal peak (local maximum) in March and

April in Australia and New Zealand respectively, followed by the seasonal peak

(global maximum) in July and August respectively. Similarities and differences among the circulating clades of A/H3N2 as a proportion over time also varied during the year between Australia (Figure 4.1C) and New Zealand (Figure

4.1D). During the early inter-season in January, clade A3 viruses represented the largest proportion of circulating diversity in both Australia (25.9%; N=14/54) and New Zealand (66.6%; N=2/3) before progressive replacement by other clades, particularly A1b by November and August respectively. At the end of the inter-season in December, only A1b and A2 clades were present. In

Australia, clade A1b progressively replaced most other strains, particularly A1a, over the course of 2017 despite limited circulation during the early inter-season in January and February (Figure 4.1C). A similar picture can be seen in New

Zealand, where clade A1b viruses began to emerge to the apparent detriment of clade A1a between May and June (Figure 4.1D). During the mid-season (June-

August) in New Zealand, almost all taxa isolated (90.7%; N=49/54) and sequenced belonged to either A1b (35.2%; N=19/54) or A4 (55.6%; N=30/54). In contrast, while clades A1b and A4 similarity predominated (64.9%; N=296/456) during the mid-season in Australia (32.2%; N=147/456 and 32.7%; N=149/456 respectively), they circulated alongside other clades (35.1%; N=160/456) across the near full diversity of taxa characterised excluding 3C.3A and parent clade

3C.2A1. In both countries, clade A4 also observed a notably expansion from

May, before peaking in July, and progressively retreating completely by

September in New Zealand (Figure 4.1D) and November in Australia (Figure

4.1C).

% 0.5 1.9 ------0.5

3C.3A N 5 5 ------5

% 23.8 25.9 - 22.5 20.0 3.0 33.7 12.5 30.4 28.6 38.9 24.4

3C.2A4 N 240 67 - 23 39 2 103 6 31 24 7 271

% 16.0 19.3 90.3 25.5 10.3 4.5 10.5 4.2 12.7 13.1 11.1 15.7

3C.2A3 N 161 50 28 26 20 3 32 2 13 11 2 174

% 14.6 7.7 3.2 18.6 14.9 50.7 11.8 16.7 3.9 2.4 11.1 13.6

3C.2A2 N 147 20 1 19 29 34 36 8 4 2 2 151

% 34.4 26.3 6.5 24.5 49.2 34.3 34.3 58.3 30.4 28.6 38.9 34.1

3C.2A1b N 347 68 2 25 96 23 105 28 31 24 7 378

% 9.6 18.1 - 7.8 3.6 1.5 9.8 8.3 18.6 22.6 - 10.5

3C.2A1a N 97 47 - 8 7 1 30 4 19 19 - 116 not missing data missing not

legibility

% 1.1 0.8 - 1.0 2.1 6.0 - - 3.9 4.8 - 1.4

3C.2A1 N 11 2 - 1 4 4 - - 4 4 - 15

Phylogenetic characterization of influenza A/H3N2 clades circulating in Australia and New Zealand by New Zealand and state, circulating in Australia Phylogenetic influenza A/H3N2 of characterization clades

4. indicateszero to improve

) NSW NT QLD SA TAS VIC WA NI SI - Table Table isolation. of island territory major and Location Australia New Zealand Total ( Dash

Figure 4.1. Phylogenetic characterised of A/H3N2 clades circulating within Australia and New Zealand in 2017 by month of isolation. Absolute number of sequences isolated by clade in Australia (Panel A) and New Zealand (Panel B) by month of isolation. Total proportion of sequences isolated by clade in Australia (Panel C) and New Zealand (Panel D) by month of isolation. Note x- axes in plot Panel B and Panel D is not consistently linear due to the lack of sequences available from February, November and December.

4.4.2 Time-Calibrated Phylogenetic Analysis

In Supplementary Figures S4.2 and S4.3, we show fixed time-scaled MCC trees of influenza A/H3N2 sampled in Australia and New Zealand in 2017 by

Dataset and coloured by their respective sampling locations. Wide genetic diversity among isolates circulating during a single season can be observed among the sampled taxa in both Australia and New Zealand across all Datasets.

Furthermore, significant heterogeneity by sampled location is likewise observed

across full range of genetic diversity, however some clustering by location can be observed. Using the complete sequence dataset (Dataset 1; N=1,110), the estimated median time to most recent common ancestor (tMRCA) of was 3.71 years (95% HPD = 3.18 – 4.30) roughly corresponding to mid-April 2014

(decimal year = 2014.28; 95% HPD = 2013.70 – 2014.82). There were some differences between the three sequence datasets (Supplementary Table S4.6).

We estimated a significantly shorter median (x ͂ = 2.88; 95% HPD = 2.51 - 3.24) tMRCA for Dataset 2 (N=735) while Dataset 3 (N=437) was mostly similar to

Dataset 1, (x ͂ = 3.68; 95% HPD = 3.09 – 4.28) however this can be explained by the random exclusion of all five 3C.3A isolates from Dataset 2 which branch directly from the older MRCA in both Dataset 1 and 3 (Supplementary Figures S4.2 and

S4.3). For Dataset 1, we estimated a mean evolutionary rate of 3.74 x 10-3 substitutions per site per year s/s/y (95% HPD = 3.43–4.06 × 10-3 s/s/y), with little variation between the datasets (Supplementary Table S4.6).

4.4.3 Phylogeography within and between Australia and New Zealand

Between the nine States, Territories and Islands characterised as discrete traits in our phylogeography model, transmission from New South Wales to the

North Island of New Zealand, South Australia and Victoria ranked as the most supported in all three datasets (Table 4.3). In Dataset 1 (N=1,110), 25 statistically supported (BF > 3) routes were identified out of a possible 72 unique paths, 14 of which were definitive (Figure 4.2; BF > 100). In contrast, 21 routes were supported in Dataset 2, and 17 were supported in Dataset 3, of which 13 and 9 were considered definitive (Supplementary Tables S4.7-S4.9 & Supplementary

Figure S4.4). New South Wales was the most frequently implicated origin site for transmission to other locations across all three datasets, and the most supported, occupying three of the top equally supported origin routes. There

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were some differences however between the original and subsampled datasets.

For example, transmission between the islands of New Zealand (North to South) was among the top-supported routes in Dataset 1 (BF = 117982), compared to rank 16th and 14th in Dataset 2 & 3 respectively (BF = 45.7 & 10.9 respectively).

In contrast, transmission from South Australia to New South Wales was among the top supported routes (equal first) in Dataset 2 (BF = 117982) but ranked 10th and 7th in Dataset 1 & 3 (BF = 8420.5 & 721.1 respectively). Queensland was the most frequently supported destination for transmission from other locations in

Dataset 1 (N = 5/25; 20%) and Dataset 2 (N = 6/21; 29%) while in Dataset 3, New

South Wales and South Australia were the most frequently supported destinations (N = 3/17; 18% each).

Figure 4.2. Phylogeographic projection of 1,110 A/H3N2 taxa (Dataset 1) sequenced for HA and NA in 2017 from Australia and New Zealand. We only show routes with definitive support (BF > 100). In Supplementary Table S4.7, we show the line weight equivalent to log-standardized BF values.

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Table 4.3: A/H3N2 transmission in 2017 between and within Australian and New Zealand by subsampling method and ranked by statistical support. Dataset 1 Dataset 2 Dataset 3 Route (N=1,110) (N=735) (N=437) Origin (O) Destination (D) Rank BF Rank BF Rank BF New South Wales North Island 1 117982 1 117982 1 104874 New South Wales South Australia 1 117982 1 117982 1 104874 New South Wales Victoria 1 117982 1 117982 1 104874 New South Wales Queensland 1 117982 1 117982 2 26213 North Island South Island 1 117982 12 45.7 10 10.9 South Australia Victoria 1 117982 7 505.7 NS 0.3 Victoria Western Australia 1 117982 5 2138 11 6.7 Victoria New South Wales 1 117982 6 879.9 13 3.1 Victoria Queensland 2 10719 11 57.6 7 19.4 South Australia New South Wales 3 8420.5 1 117982 3 721.1 New South Wales Tasmania 4 2557.7 2 58987.3 1 104874 New South Wales Northern Territory 5 1678.3 8 284.8 5 157.6 New South Wales Western Australia 6 1221.8 3 4908.9 1 104874 Victoria Tasmania 7 284 NS 0.2 NS 0.8 South Australia Tasmania 8 82.6 10 82 NS 0.5 New South Wales South Island 9 24.9 9 140.4 4 234.9 Queensland South Australia 10 23.8 4 2450.8 6 20.4 Northern Territory New South Wales 11 18.5 NS 0.6 NS 0.5 Queensland Victoria 12 12.3 NS 0.5 8 19.2 Western Australia New South Wales 13 9.7 14 25.9 12 5.4 South Australia Queensland 14 8.2 15 21.9 NS 1.7 Western Australia Victoria 15 6.8 NS 0.5 NS 1.7 Victoria South Australia 16 6.6 NS 0.6 NS 0.5 Northern Territory Queensland 17 6.3 13 35.7 NS 0.5 Western Australia Queensland 18 4.5 16 16.9 NS 1.9

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4.4.4 Predictors of A/H3N2 Transmission

Of the 15 factors included as predictors in the GLM of Dataset 1, six were measured as either significant promoters or suppressors of A/H3N2 transmission within and between Australia and New Zealand (Table 4.4).

State/Territory (in Australia) or Island (in New Zealand) capital city population density by destination, mean annual temperature by destination, and great circle distance between any two pair-wise locations was equally the most significant factor (BF = ∞) identified in the model. Increasing distance between locations definitively (BF >100) suppressed transmission (β| δ = -0.79), whereas increasing population density (β| δ = 0.66) and temperature (β| δ = 0.69) by destination were definitive promotors. Temperature and population density by origin were also definitive promotors of transmission (β| δ = 1.58 and 1.117 respectively). Sample size by origin (β| δ = 0.66) was a definitively supported

(BF = 3985) promotor of transmission between pair-wise locations. The top four significant factors identified in the complete dataset (Dataset 1) were also the top four in each subsampled dataset (Dataset 2 & 3) with matching conditional effects, in order: distance, sample size by origin, and temperature and population density by destination (Table 4.3). In contrast to Dataset 1, temperature by origin was not a significant (BF < 3) promotor of transmission in the two subsampled Datasets 2 & 3, while sample size by destination was, and mean annual precipitation by origin was a minimally significant promotor in

Dataset 2 only (Table 4.3). There however remains some uncertainty in estimates of effect in Datasets 2 & 3: the wide 95% BCI raw effect coefficients for temperature, population density, sample size (Datasets 2 & 3), and precipitation

(Dataset 2 only) might also indicate suppressor effects, in contrast to the promotive conditional effect. In Supplementary Tables S4.10 – S4.15, we show the complete raw and conditional GLM results for each dataset demonstrating

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airline passenger flux was not supported as either a promotor or suppressor of transmission in any dataset.

Table 4: Comparison of conditional effects (β| δ) of factors on A/H3N2 transmission within and between nine Australian and New Zealand States, Territories and Island in 2017 by subsampled dataset. Predictors are ranked by decreasing significance (BF) compared to the complete sequence dataset (Dataset 1). Dataset 1 Dataset 2 Dataset 3

Factors (β| δ) BFs Rank (β| δ) BFs Rank (β| δ) BFs Rank

Pop Dense (D) 0.66 ∞ 1 0.28 39.08 4 0.33 36.05 4

Temperature (D) 0.69 ∞ 1 0.40 59.64 3 0.37 36.16 3

Distance -0.79 ∞ 1 -0.60 342579.56 1 -0.65 20131.84 1

Sample Size (O) 0.74 3985.21 2 0.93 7765.24 2 1.96 21391.40 2

Temperature (O) 1.58 840.38 3 0.02 0.43 11 0.00 0.16 11

Pop Dense (O) 1.17 761.28 4 0.06 1.58 7 0.00 0.11 7 Precipitation (O) 0.01 0.61 5 0.06 4.15 6 0.00 0.12 6 Sample Size (D) 0.00 0.11 9 0.09 6.32 5 0.22 12.36 5

4.5 Discussion

We have used a large dataset of HA and NA sequences and methods in phylogenetic and phylogeographic analysis to characterise the spatial and temporal circulation and transmission dynamics of the 2017 Australian and

New Zealand A/H3N2 influenza season. Most viruses in Australia, and all viruses in New Zealand fell into clade 3C.2A or its known subclades (Table 4.2).

Clade circulation between Australia and New Zealand demonstrated similarities in relative composition over time (Figure 4.1) despite the differences in observed seasonal activity and severity, and very strong evidence of transmission from New South Wales to the two major islands of New Zealand

(Figure 4.2 and Table 4.3). There are two primary explanations for these competing observations, (i) viral factors such as mutations, and (ii) epidemiological factors.

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The accumulation of mutations within the antigenic domains of HA are known to drive the dynamics and severity of influenza epidemics worldwide

(Russell, et al. 2008; Koel et al. 2013). Novel subclades bearing such mutations such as 135K and 150K within clade A3 (designated A3a) and T135K/N within

A1b (Bedford and Neher 2018) have recently been associated with severe disease and reduced neutralization by vaccine serum (Tsou et al. 2017; Jorquera et al. 2019). Of the 161 clade A3 taxa sampled from Australia, over half possessed these characteristic residues (51.0%; N=82/161), 73.2% (N=60/82) of which the majority were concentrated in New South Wales and Victoria (66.0%; N=33/50 and 84.4%; N=27/32 respectively) which both experienced high seasonal activity.

In contrast, only two A3a taxa were isolated in Western Australia and one in

New Zealand which both experienced mild seasons. In New Zealand, residues

135K/N accounted for 32.3% of taxa (N=10/31) within A1b, while in Australia, it accounted for 51.0% (N=177/347) and represented the primary clade in circulation (Table 4.2). The increased circulation of 135K/N residues in clades

A1b and novel A3a in Australia may explain the increased activity and associated severity of the 2017 Australian season however some discrepancies remain: similarly high proportions of clade A1b viruses possessing T135K/N mutants were identified in low activity states such as Western Australia (53.6%;

N=15/28), and high activity states such as Victoria (63.8%; N=67/105) and

Queensland (84.0%; N=21/25). In other high activity states such as South

Australia and New South Wales, low proportions of T135K/N mutants were identified: 29.2% (N=28/96) and 36.8% (N=25/68) respectively. Alternative explanations include an exhaustive number of epidemiological factors including but not limited to comorbidities, age and access to healthcare, which are known to impact the morbidity and mortality of influenza epidemics

(Morales et al. 2017) but were not accounted for in our study. Local influenza

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circulation and social mixing patterns during preceding seasons also play a significant role in shaping population immunity (Mossong et al. 2008; Fonville et al. 2014), and therefore the morbidity and mortality of subsequent influenza seasons. Without conclusive evidence such as associated clinical data, the role of 135K/N or other mutants on A/H3N2 severity remains speculative and suggests that differences in seasonal severity observed between Australia and

New Zealand in 2017 likely involves the complex interaction between epidemiological factors such population susceptibility rather than unique viral factors.

Previous studies have investigated the inter-seasonal dynamics of influenza in Australia using phylogenetic methods (Ross et al. 2015; Barr et al.

2019), however this is the first study to examine the specific regional dynamics of influenza A/H3N2 within and between Australia and New Zealand using phylogeography and GLM methods. Recent studies from Australia have demonstrated that simultaneous global introductions of diverse influenza lineages concurrent with strong transport connections between major

Australian cities likely drives epidemic onset across the country (Geoghegan et al. 2018). Our results add to this evidence and identifies the significant role of

New South Wales in disseminating seasonal A/H3N2 to other states and territories in Australia and onwards to New Zealand, particularly the Northern

Island (Figure 4.2). Of note, significant reciprocal transmission can be observed within the highly populated south-eastern states of Australia (Figure 4.2), with outward transmission to other less populated and/or geographically distant states and territories, which is consistent with our GLM results identifying capital city population density and increasing distance as positive and negative predictors of transmission respectively (Table 4.4). The wide diversity of

A/H3N2 isolates (Table 4.2) observed in our study provides further evidence of

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multiple global introductions, however, the role of domestic airline travel as a predictor of spread was not supported. This suggests that the ongoing importation of global A/H3N2 lineages is a significant factor influencing seasonal composition overtime, while the domestic spread of A/H3N2 may be less than initially expected (Brownstein et al. 2006). As a result, Australia’s role in the region is therefore unclear. Despite evidence of transmission from New

South Wales to New Zealand’s North Island (Figure 4.2), and similarities in clade composition (Table 4.2), we cannot conclusively determine whether these results suggest export from Australia, similar simultaneous and ongoing global introductions into New Zealand, or both. Future phylogeography studies might look to incorporate global strains into subsequent regional analyses to better understand the role of importation, regional spread, and potential export of

A/H3N2 from Australia to New Zealand.

The regional role of A/H3N2 circulation in Australia and New Zealand has important public health implications, particularly on global dynamics, vaccine composition recommendations and epidemic preparedness. Firstly, compared to other seasonal influenza A viruses, A/H3N2 is typically characterised by rapid antigenic turnover from season to season (Nelson and

Holmes 2007), however, in recent years increasing diversity among circulating

A/H3N2 has been observed globally (Bedford and Neher 2017; Bedford and

Neher 2018). This has had considerable implications on recent A/H3N2 VE in the US (Flannery et al. 2019). The wide diversity of A/H3N2 isolates circulating in Australia and New Zealand in 2017 observed in our study (Table 4.2) might similarly explain the low VE observed in the two countries (Huang, et al. 2017;

Sullivan, et al. 2017). Should this trend continue, the incorporation of multiple antigenically distinct A/H3N2 strains into a single vaccine might be considered to overcome the growing diversity of A/H3N2 in the region and globally.

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Second, previous studies have shown that A/H3N2 lineages from Southeast

Asia typically seed terminal epidemics in Oceania, North America and Europe

(Rambaut et al. 2008; Bedford, et al. 2015), however lineages can sometimes persist for years outside of Southeast Asia emigrating at different rates between temperate regions in opposing hemispheres (Bedford et al. 2010). During the

2017-18 northern hemisphere season in the USA, A/H3N2 predominated, with clades A1b, A2 and A3 cocirculating (Garten et al. 2018). The prevalence of clade

A1 and its subclades A1a and A1b in 2017, accounting for the majority (45.9%;

N=509/1110) of all circulating A/H3N2 in Australia and New Zealand, could have therefore preceded the 2017-18 US season, which was also abnormally severe. In contrast, influenza B/Yamagata predominated during the 2017-18 season in Europe, yet the preceding 2016-17 season saw a predominance of

A/H3N2 and the emergence of clade A1 subclades A1a and A1b, and thus might have also preceded the 2017 season in Australia and New Zealand. This suggests the potential spread of A/H3N2, particularly clade A1 and its evolving subclades from Europe to Australia and New Zealand, and onwards to the USA over the course of three seasons from 2016 to 2018. Whether A/H3N2 was seeded in this proposed direction or originated from previously hypothesised source locations in Southeast Asia cannot be conclusively determined without further study but has implications for future vaccine composition recommendations.

Routine clade characterisation in Australia and New Zealand, and ongoing comparison to preceding and subsequent northern hemisphere seasons in the

USA and Europe could help further our understanding of A/H3N2 transmission between temperate regions, including importation, domestic circulation and export, and could support future vaccine composition recommendations and seasonal preparedness activities.

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Overall, this study has some limitations. Firstly, in each of the three datasets, sample size was identified as a significant promotor of transmission (Table 4.3) indicating varying degrees of potential sampling bias despite efforts to control this in the study design. Separating the effects of sampling bias from these analyses however is difficult. For example, locations that experienced large outbreaks are more likely to transmit to other locations and have increased sample sizes. In this case, transmission from New Zealand to Australia was not supported, despite similar levels of travel between the two countries, yet New

Zealand experienced low seasonal activity relative to Australia. As such, minimal transmission from New Zealand to Australia in 2017 might be expected, excluding sampling bias as an overriding factor. Furthermore, we found that often highly supported routes of transmission identified in Dataset 1 were similarly identified in both subsampled Datasets 2 & 3, lending credence to the validity of our primary results. Secondly, due to the large sample size of the primary dataset, global A/H3N2 sequences were not included in our analysis due to computational limitations. Omitting such sequences, however, eliminates any ability to resolve whether other countries may act as intermediate sources for transmission. This could also include evidence of multiple exportations and introductions that supersedes the transmission identified within and between Australia and New Zealand, therefore potentially biasing our results. However, as before, future phylogeography studies incorporating global A/H3N2 sequence data into subsequent regional analyses, could help to better resolve the dynamics of A/H3N2 spread within the region, but also reveal any such bias associated with our analyses.

Lastly, only HA and NA sequences were included in our analysis. Other genetic factors among the six other genes of A/H3N2 could explain the differences observed between Australia and New Zealand in 2017 (Gao et al.

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2009), although considering the similarities in relative clade composition, this conclusion has limited support. Future studies utilizing whole genomes however could exclude this hypothesis.

4.6 Conclusion

We have shown that during the 2017 influenza season, the composition of known clades between Australia and New Zealand was relatively similar, likely excluding hypotheses that viral factors were responsible for the differences in observed seasonal activity and severity. We also find evidence of significant reciprocal transmission between Australian states and territories, particularly within the south-east states, and transmission from New South

Wales to New Zealand using phylogeography for the first time. Generalized

Linear Modelling provides further evidence supporting this regional dynamic, identifying positive associations between capital city population densities, and negative associations with distance among other climactic factors. There was insufficient evidence to indicate domestic airline travel between states and territories in Australia was associated with significant transmission, suggesting that domestic circulation of A/H3N2 may be a less significant factor influencing seasonal influenza epidemics than ongoing global introductions. This hypothesis however warrants further study. Overall these results have important implications in the understanding of A/H3N2 dynamics in the region which could assist with future risk assessment, seasonal control strategies and vaccine composition recommendations in Australia and New Zealand, but also globally. Including routine subtyping, sequencing, clade characterisation and the collection of associated severity data in influenza testing procedures in

Australia and New Zealand should be a priority, first to improve surveillance in the region, and second, estimates of disease burden and severity.

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Chapter Five

Phylodynamics of Influenza A/H1N1pdm09 in

India Reveals Circulation Patterns and Increased

Selection for High Mortality Mutants

This chapter has been published in the journal Viruses with the following citation:

Adam DC, Scotch M, MacIntyre CR. Phylodynamics of Influenza

A/H1N1pdm09 in India Reveals Circulation Patterns and Increased Selection for Clade 6b Residues and Other High Mortality Mutants. Viruses. 2019

Sep;11(9):791.

This publication was a direct result of my research towards this PhD, and reproduction in this thesis does not breach copyright.

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5.1 Chapter Abstract

The clinical severity and observed case fatality ratio of influenza

A/H1N1pdm09 in India, particularly in 2015 and 2017 far exceeds current global estimates. Reasons for these frequent and severe epidemic waves remain unclear. We used Bayesian phylodynamic methods to uncover possible genetic explanations for this while also identifying the spatial transmission of

A/H1N1pdm09 between 2009 and 2017 to inform future public health interventions. We reveal disproportionate selection at haemagglutinin residue positions associated with increased morbidity and mortality in India such as site

222 and clade 6B characteristic residues relative to equivalent isolates circulating globally. We also identify for the first-time increased selection at site 186 as potentially explaining the severity of recent AH1N1pdm09 epidemics in India.

We reveal national routes of A/H1N1pdm09 transmission identifying

Maharashtra as the most important state for spread throughout India while quantifying climactic, ecological and transport factors as drivers of within- country transmission. Together these results have important implications for future A/H1N1pdm09 surveillance and control within India but also for epidemic and pandemic risk prediction around the world.

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5.2 Introduction

In early 2009, a novel influenza A H1N1 (A/H1N1pdm09) virus emerged in Veracruz, Mexico and California, USA and was responsible for the first influenza pandemic of the 21st century (Girard et al. 2010). As a triple-reassortant influenza virus antigenically distinct from the former seasonal A/H1N1(Peiris et al. 2009), the virus quickly spread around the world causing severe perturbations to health and surveillance systems (Webb et al. 2009; Mitchell et al. 2012). During the pandemic, estimated case fatality ratios (CFR) ranged from less than 0.001% to 10% due in part to significant case under-ascertainment and the heterogeneity of case definitions between countries (Wong et al. 2013).

However, a true geographic variation in CFR could not be excluded (Dawood et al. 2012). Furthermore, as diagnostic capacity was overwhelmed, laboratory confirmation of infection was largely restricted to severe and fatal cases leaving estimates of total morbidity unknown. Studies have since estimated approximately 24% (95% Confidence interval: 20 - 27%) of the global population was infected during the pandemic (Kerkhove et al. 2013), whilst mortality was similar to that of a severe seasonal epidemic (~ 0.01% CFR) (Simonsen et al.

2013). Consistent with past pandemics, A/H1N1pdm09 has continued to circulate around the world seasonally every year since 2009, replacing pre- pandemic A/H1N1 strains (WHO 2010; Broor et al. 2012), and co-circulating with influenza A/H3N2 and influenza B (Russell, et al. 2008; Bedford, et al.

2015).

The first case of A/H1N1pdm09 in India was reported in May 2009 in the city of Hyderabad, Telangana (Choudhry et al. 2012). By December, widespread human-to-human transmission led to substantial morbidity and mortality within the country (Gurav et al. 2010; Choudhry, et al. 2012). Following a second

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epidemic wave in early 2010, approximately 50,000 cases had been reported in

India during the pandemic period with a reported CFR of 6.1%. In subsequent seasons, the annual incidence of H1N1pdm09 in India was low, with approximately 5,000 cases or less reported nationally each year. However, in

2015 and 2017, widespread epidemics occurred with an estimated 43,000 and

39,000 confirmed cases reported, as well as approximately 3,000 and 2,300 deaths respectively. States particularly affected included Maharashtra, Gujarat,

Rajasthan and Madhya Pradesh, accounting for 75.6% (N=2261/2991), and 72.1%

(N=1634/2266) of all deaths nationally in 2015 and 2017. In some states, CFR of up 20% in 2015 and 30% in 2017 have been reported. Reasons for these frequent and severe epidemic waves of A/H1N1pdm09 in India with apparent high mortality remain unclear. Resource-constrained lower-middle income countries such as India where access to quality health care might be limited have been associated with excess influenza mortality (Murray et al. 2006; Dawood, et al.

2012; Simonsen, et al. 2013), however ongoing reports of A/H1N1pdm09 associated morality among otherwise healthy adults aged under 65 years in

India remains particularly unusual (Malhotra et al. 2016; Kumar et al. 2017;

Kulkarni et al. 2019).

Emerging methods and models of data integration in Bayesian phylogenetics have provided new insights into the evolution and dynamics of influenza A viruses (Baillie et al. 2012; Hedge et al. 2013; Lemey, et al. 2014), however the use of these methods have yet to be applied to A/H1N1pdm09 in

India. In this study, we aim to utilise these methods to explore possible genetic explanations for the high severity and mortality of A/H1N1pdm09 in India. We also aim to understand the evolutionary and transmission dynamics of

A/H1N1pdm09 in India to estimate potential case under-ascertainment and opportunities for outbreak control. Our results have potential implications for

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predicting the future risk of influenza A/H1N1pdm09 severity and spread both within India and around the world.

5.3 Materials & Methods

5.3.1 Compilation of Sequence Datasets

We searched the Global Initiative for the Sharing All Influenza Data

(GISAID) for all available haemagglutinin (HA) gene sequences sampled in

India between 2009 and 2017 inclusive (Shu and McCauley 2017). We identified

930 out of 1,025 openly available sequences with collection date and location metadata publicly available or available upon request from the uploading authors. Of those, we considered only 625 to be of sufficient length for analysis

(>1,600bp). We removed 12 sequences across five State & Union Territories

(S/UT) of India due to low sampling frequencies defined as less than two sequences per 10 million population within the study period. This cut-off was selected through trial and error with the purpose of maintaining sufficient sampling across the study period without excessively removing valuable data- points (sequences and locations). We aligned the final dataset of 613 sequences using MUSCLE (Edgar 2004) in Geneious v10.1.2 (Kearse, et al. 2012) and manually inspected and trimmed the HA coding regions for further analysis.

Table 5.1 and Supplementary Figure S5.1 shows the spatial and temporal distribution of the Indian sequence dataset. Appendix C acknowledges the source of these 613 sequences. For comparative analyses with globally circulating sequences we searched GISAID for all full length (>1,600bp)

A/H1N1pdm09 HA sequences sampled during the same period with complete region and date of sampling metadata. Excluding India, we identified 23,144 records. We removed 1,935 records with duplicate isolate sources resulting in a final global dataset of 21,209 sequences aggregated to one of ten regions roughly

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similar to a previous study (Bedford, et al. 2015): Northern Asia (Mongolia &

Russia), China, Japan/Korea, Southern Asia/South-East Asia, Middle

East/Western Asia, Africa, Europe, North America (Central America and

USA/Canada), South America, and Oceania. The spatial and temporal distribution of the complete global A/H1N1pdm09 sequence dataset can be seen in Supplementary Table S5.1. To reduce computational burden and limit the impact of sampling bias we created two independent sequence subsets (S1 & S2) randomly sampling up to 50 sequences per region-year as per previous studies

(Russell, et al. 2008; Bedford, et al. 2015). Each subset was aligned using MAFFT v1.3.7 (Katoh and Standley 2013) in Geneious v10.1.2 (Kearse, et al. 2012) and manually inspected and trimmed as before.

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% 3.3 1.6 2.0 9.5 3.6 7.2 1.1 2.9 2.6 1.0 5.5 10.3 10.0 39.5 100.0

7 6 20 63 10 12 58 61 22 44 18 16 34 242 613 Total

6 2 1 9 included for analysis. for included

2017

8 11 19 of India

2016

1 2 3 6 6 1 20 11 18 53 26 s (S/UT) 147 2015 torie

2 3 6 2 13 2014

6 3 7 2 5 5 2 66 96 Union Terri

2013

ar and

3 3 9 5 3 2 1 5 Ye 14 29 15 54 143 State 2012

7 3 1 8 5 4 5 4 37 2011

4 4 3 2 2 2 8 8 2 1 1 16 53 2010

4 1 1 1 1 4 1 3 5 1 3 10 19 42 96 2009 sequence sequence dataset by year and

) 6

aemagglutinin (HA) Kashmir (12.5)

27.7) 1. and 5. Table S/UT (Population 10 Assam (31.2) Delhi (16.8) Goa (1.5) Haryana (25.4) Jammu Karnataka (61.1) Kerala (33.4) Madhya Pradesh (72.6) Maharashtra (112.4) Punjab ( Rajasthan (68.6) Tamil Nadu (72.15) Uttarakhand (10.1) West Bengal (91.3) Year Total

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5.3.2 A/H1N1pdm09 Transmission within India

We used BEAUti v1.10 (Suchard, et al. 2018) to specify a discrete-trait phylogeographic model to estimate all possible transitions between the 14

Indian S/UT included in our Indian dataset (N = 613). We selected a GTR+ Γ4 substitution model with a relaxed clock based on preliminary path sampling and stone-stepping sampling results (Baele, et al. 2012; Baele, et al. 2012)

(Supplementary Table S5.2) and correlation (R2=0.81) between the sampling time and root-to-tip divergence of HA (Rambaut, et al. 2016) (Supplementary

Figure S5.2). We ran four models independently with 50 million Markov Chain

Monte Carlo (MCMC) generations sampling every 5,000 steps and combined independent runs using LogCombiner v1.10 (Suchard, et al. 2018) after inspecting for similar convergence around the posterior using Tracer v1.6

(Rambaut, et al. 2014). Each model specified a nonparametric Bayesian Skygrid tree prior (Minin et al. 2008; Gill et al. 2013) with 50 intervals as default to reconstruct past population demographics. We produced maximum clade credibility (MCC) trees from the combined posterior tree distribution using

TreeAnnotator v1.10 specifying median node heights after removing 10% burn- in (Suchard, et al. 2018). We used SpreaD3 v0.9.6 (Bielejec, et al. 2016) to calculate

Bayes factors (BF) for each pairwise transition between the 14 S/UT as well as to geospatially render the phylogeographic projections. We defined sufficient support for transmission as BF > 3 as per convention (Lemey, et al. 2009). Higher levels of statistical support were defined according to Supplementary Table S5.3

(Jeffreys 1961).

A single state, Maharashtra, accounted for 39.5% (N=242/613) of the complete India dataset. To compare and control for potential sampling bias in

India, we subset the Indian sequence dataset into five unique datasets,

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randomly sampling up to five sequences per location-year in location-years with greater than five sequences, while leaving those with less than five sequences per location-year untouched. This created a more equitable spatiotemporal distribution of sequences without removing under-represented location-years (Supplementary Table S5.4). For each of the five random subsets, we specified an identical discrete-trait phylogeographic model as above each with 50 million MCMC generations. We calculated BF for each pairwise transition as the average between all five subsets using SpreaD3 v0.9.6 (Bielejec, et al. 2016).

5.3.3 Predictors of Transmission within India

We extended the phylogeographic model above using a generalised linear modelling (GLM) framework to investigate the contribution of, climactic, ecological, and demographic factors as potential predictors of transmission

(Lemey, et al. 2014). Based on previously published studies (Scotch et al. 2013;

Dudas, et al. 2017), preliminary ecological factors of interest included longitude, latitude, average temperature and average precipitation. Preliminary demographic predictors included average population growth, population density, gross domestic state product (GDSP) and percentage of S/UT population living in urban environments. We also calculated aviation passenger flux as the average number of domestic passengers recorded in all S/UT between

2009 and 2017 using data publicly available from the Indian Directorate General of Civil Aviation. After assessing for collinearity, the final GLM included S/UT population density, average temperature, longitude and passenger flux. We also included great-circle-distance between each pair-wise location and sample size by S/UT to assess for potential sampling biases. We estimated the mean

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posterior probability of each predictor’s inclusion in the model, BF, and the effect coefficient using R as per (Lemey, et al. 2009; Beard et al. 2014).

5.3.4 Positive Selection Analysis

For the complete Indian sequence dataset (n=613) we generated rate ratios of non-synonymous to synonymous (dN/dS) mutations using Bayesian renaissance counting (BRC) implemented in BEAST 1.10 (Suchard, et al. 2018) assuming three independent (1, 2, 3) codon partitions (Yang 1994). At each site, positive selection was determined where the lower 95% Bayesian Credible

Interval (BCI) was greater than one (dN/dS > 1). Potential sites under selection were validated using both two-rate fixed effects likelihood (FEL) and single- likelihood ancestor counting (SLAC) tests in HyPhy assuming a p-value threshold of 0.1 as convention (Kosakovsky Pond and Frost 2005; Pond and

Muse 2005). For this, fixed MCC trees were generated with TreeAnnotator v1.10

(Suchard, et al. 2018) from previous BEAST outputs. We also used BRC methods in BEAST 1.10 to compare dN/dS ratios at significant sites in both global

A/H1N1pdm09 datasets previously generated (S1 & S2). Due to the significant number of taxa in both global datasets (2 x N=4,063), we first generated a posterior distribution of 10,000 trees from 100 million MCMC generations each before inferring dN/dS ratios on the a fixed posterior of 500 trees after removing

50% burn-in similar to Bedford et al 2015 (Bedford, et al. 2015). For all three datasets, relative H1 and H3 site numbering was determined using the influenza research database’s (FluDB) HA Subtype Numbering algorithm (Burke and

Smith 2014) and residue frequencies at selected sites was determined using

Geneious v10.1.2 (Kearse, et al. 2012). Selected sites within the HA structure

(RCSB PDB ID: 4LXV (Yang et al. 2014)) were visualized with YASARA View v19.7.20 (Krieger and Vriend 2014).

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5.4 Results

5.4.1 CFR & Viral Population Demographics

(a) (b)

Figure 5.1. (a) Bayesian Skygrid estimation of effective viral population size

(Ne) of A/H1N1pdm09 in India between 2009 and 2017. Here, we show the

mean Ne and respective 95% Bayesian credible interval (BCI) plotted in blue. (b) Official A/H1N1pdm09 case and death counts (left axis) reported by the National Centre for Disease Control in Delhi (NCDC). Calculated yearly case fatality ratios (CFR) with corresponding percentages shown (right axis). The arrow in Figure 1a points to the inferred seasonal epidemic in 2013/14, in contrast to reported cases in Figure 1b.

In Figure 5.1a we show the inferred past population demographics of

A/H1N1pdm09 in India between 2009 and 2017 alongside the observed cases and deaths recorded each year and calculated CFR (Figure 5.1b). The largest epidemic peak inferred as the effective population size (Ne) of A/H1N1pdm09

(Figure 5.1a) can be seen in 2009 (Median Ne = 36.24; 95 BCI = 11.93 to 152.25) concurrent with the global pandemic at the time. The second largest peak Ne can be seen in early 2015 (Median Ne = 25.58; 95 BCI = 13.74 to 54.90) with epidemic activity estimated to have begun in late 2014 (Figure 5.1a). A low Ne is seen in mid-2011 (Median Ne = 12.60; 95 BCI = 4.20 to 45.10) consistent with the observed

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case data (Figure 5.1b) before consecutive Ne waves of similar height can be observed in years 2012 to 2014. Less than 1,000 cases of A/H1N1pmd09 were reported nationally in 2014 (Figure 5.1b), however results of the demographic reconstruction suggest a typical seasonal epidemic beginning in late 2013 before declining in mid-2014 (Figure 5.1a Arrow). A widening 95% BCI can be seen in mid-2016 onward likely due to the reduction in taxa available for accurate demographic inference (N=28).

5.4.2 dN/dS Selection Analysis & Amino Acid Variations

Table 5.2. dN/dS ratios of codon sites identified in India under pervasive positive selection relative to dN/dS ratios of two distinct international samples S1 and S2. India Taxa (n = 613) International (S1) International (S2) Site (H3 a) Ag b dN/dS 95% BCI dN/dS 95% BCI dN/dS 95% BCI 84 (92) n/a 8.14 (5.68–10.92) 2.53 (1.69–3.45) 3.51 (2.33–4.79) 163 (166) Sa 3.83 (2.68–5.35) 4.54 (2.98–6.38) 2.62 (1.79–3.68) 185 (188) Sb 4.50 (3.18–6.21) 2.14 (1.41–2.94) 1.13 (0.76–1.54) 186 (189) Sb 3.35 (2.26–4.51) 1.44 (0.97–1.94) 1.47 (0.93–1.97) 222 (225) Ca 13.42 (9.43–18.42) 3.42 (2.30–4.81) 4.32 (2.87–5.85) 256 (259) n/a 4.39 (3.12–6.09) 1.16 (0.74–1.58) 1.08 (0.75–1.48) a Relative H3 numbering determined by FluDB HA Subtype Numbering algorithm. b Antigenic Domain (Ag).

Bayesian renaissance counting (BRC) identified 40 codon sites under positive selection given the uncertainty of the posterior phylogeny (95% BCI >

1.0). Six of these sites were also detected by both FEL and SLAC procedures assuming a fixed MCC tree (Table 5.2). Site 222 (H1 numbering onwards) had the highest dN/dS ratio (13.42) followed by site 84 (8.14), 185 (4.50), and 256 (4.39).

All validated sites detected in the complete Indian dataset excluding 163 exhibited significantly higher dN/dS ratios given the 95% BCI determined by BRC compared to both international samples (Supplementary Table S5.5). Among the six selected sites, four (163, 185, 186 and 222) are located within the antigenic sites of HA (Ca, Cb, Sa, Sb). The relative structural positions of these six selected sites are visualized in Figure 5.2. The complete dN/dS results for both Indian and

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International sequence dataset can be seen in Supplementary Table S5.5 and

S5.6.

Figure 5.2. Relative structural locations of A/H1N1pdm09 residues within the HA monomer under positive selection in India. Sites under selection are highlighted in red and numbered not including the signal peptide (H1 numbering). Letters in parentheses indicate sites located within the known antigenic domains of HA1. (RCSB PDB ID: 4LXV).

The frequency of residue changes at each detected site in India varied.

Among the detected sites, S185T was the most frequently observed residue change (N = 473/613; 77.2%), followed by K163Q (N = 240/613; 39.2%), A256T

(N=231/613; 37.7%), and S84N (N = 160/613; 26.1%). Two residue changes,

D222G and D222N, were observed in 3.1% (N=19/613) and 1.6% (N=10/613) of

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the Indian sample respectively. Four residue changes could be observed at site

84; in descending order of frequency: S84N (N = 160/613; 26.1%), S84G (N =

66/613; 10.8%), S84I (N = 6/613; 1.0%) and S84D (N = 2/613; 0.3%). Eight discrete changes were observed at selected site 186; in descending order of frequency:

A186T (N=5/613; 0.82%), A186G (N=2/613; 0.33%), and A186V (N=1/613; 0.16%).

5.4.3 Phylogeography of A/H1N1pdm09

In Figure 5.3 we show the spatio-temporal projection of definitive

A/H1N1pdm09 transmission (BF > 100) in India from 2009 to 2017 based on the most complete sequence dataset available (N=613). The corresponding phylogeographic MCC tree can be seen in Supplementary Figure S5.3. We observe consistent historic transmission over time from Maharashtra to most

S/UT included in our model, spanning the entire latitude of India. Results from each of the five randomly down-sampled datasets showed similar routes of transmission demonstrating connections from Maharashtra to most S/UT

(Supplementary Figure S5.4 and Table S5.7). Between the 14 S/UT modelled as discrete-traits, we identified 30 supported (BF > 3) routes of asymmetric transmission out of a possible 182 unique paths (Supplementary Table S5.8).

Maharashtra was the most frequently implicated origin site for transmission (N

= 9/30; 30%) and the most supported (221,244 > BF > 199.23) followed by

Karnataka (N=8/30; 27%), and Jammu & Kashmir (N=4/30; 13%). Kerala was the most frequently implicated destination for transmission (N=5/30; 17%), followed by Jammu & Kashmir, Punjab, and Karnataka (N = 3/30; 10% each).

Assam, Punjab and Rajasthan were not supported sources for transmission (BF

< 1) but were variably implicated as destinations (N = 4/30; 13% and 1/30; 3% respectively). Maharashtra was the only state implicated as a frequent source of transmission (N = 6/30;20%) but never as a destination.

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Figure 5.3: Phylogeography of definitive A/H1N1pdm09 transmission between S/UT in India since 2009. Paths between S/UT are coloured by inferred transmission time and directions indicated by adjacent arrows. All routes shown are characterised by definitive statistical support (Bayes factor (BF) > 100). Supported to very strongly supported routes (100 > BF > 3) are not shown but can be seen in Supplementary Table S5.8.

5.4.4 Generalised Linear Modelling

Of the 11 demographic, ecological, and climactic factors included as predictors in the GLM, seven were supported as either promoters or protectors of A/H1N1pdm09 transmission between the 14 S/UT modelled (Table 5.3). The most supported factor identified in the model was great-circle-distance between

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any two pair-wise locations (BF = 269.3). As indicated by the negative model coefficient (β| δ = -0.53), increasing distance between states was definitively protective against viral transmission. Population density and average temperature by origin were also both protective against viral transmission between S/UT, however with reduced support (BF = 8.93 and 4.69 respectively).

In contrast, sample size by origin was the most supported factor (BF = 237.2) and promoting viral transmission as indicated by the positive model coefficient (β|

δ = 1.46). Sample size by destination was also implicated as a strong promotor of viral transmission (BF = 10.3). Other apparent promotors of A/H1N1pdm09 transmission included aviation passenger flux both by origin and destination

S/UT and sample size by destination. Passenger flux by origin had the highest median effect size coefficient (β| δ = 1.86) of all factors however the wide uncertainty of the 95% BCI means it might also be protective (-0.46 to 2.95).

Table 5.3: Predictors of A/H1N1pdm09 transmission in India between 14 S/UT from 2009 to 2017. Predictor E(δ) Probability (β| δ) Coefficient 95% BCI BF a Distance 0.88 −0.53 −0.79 to −0.28 269.3 SS origin 0.87 1.46 0.96 to 2.12 237.2 P Flux destination 0.31 0.35 0.15 to 0.56 15.67 SS destination 0.22 0.26 0.11 to 0.42 10.03 Pop Dense origin 0.2 −1.43 −2.82 to −0.28 8.93 P Flux origin 0.14 1.86 −0.46 to 2.95 5.84 Average temp origin 0.12 −0.49 −0.8 to −0.18 4.69 a Predictors are ordered by decreasing significance (BF) and probability of inclusion (E(δ)) in the model as a measure of the likelihood of impact on transmission.

5.5 Discussion

This study provides new insights into the high CFR and dynamics of

A/H1N1pdm09 in India. Using the most comprehensive A/H1N1pdm09 HA sequence dataset annotated with associated temporal and spatial metadata available from India our analysis has uncovered possible genetic explanations

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for the apparent high CFR of A/H1N1pdm09 observed there. Case fatality ratios aim to measure the individual risk of death among infected cases and are frequently used as a proxy for disease severity within populations (Nishiura

2010). In India, yearly CFR for A/H1N1pdm09 have ranged from 3.6% to 23.3%, which is orders of magnitude higher than observed in other countries (Figure

5.1b). The true CFR is certainly lower due to case under-ascertainment (Nishiura

2010). For example, in states with more than 100 reported deaths, the upper CFR range from 2009 to 2017 drops to 18.2% (Kulkarni, et al. 2019). In order to roughly determine the degree of possible case ascertainment issues affecting

CFR estimates in India, we used demographic reconstruction methods to infer the effective viral population size (Ne) of A/H1N1pdm09 through time. In contrast to official case counts which show little activity in the three years from

2012 to 2014 (Figure 5.1b) we infer sizable A/H1N1pdm09 epidemics in India occurring almost yearly (Figure 5.1a). This suggests a high degree of case under- ascertainment during those years. Likely explanations for this include the widespread circulation of mild or subclinical strains or increasing population immunity to drifted but antigenically indistinguishable strains. In the absence of any significant changes to surveillance and reporting guidelines however, the magnitude of cases and deaths therefore detected in 2015 and 2017 suggests the emergence of antigenically novel A/H1N1pdm09 viruses in the population. The acute magnitude and severity of the 2015 season in particular was reflected extensively in the Indian media at the time (Cousins 2015), and largely affected younger and middle aged persons under 65 years and involved deaths of healthy people (Malhotra, et al. 2016; Kumar, et al. 2017; Kulkarni, et al. 2019).

Clinicians working in intensive care reported severe clinical manifestations and extensive ulceration of the trachea-bronchial tree on bronchoscopy (personal correspondence, clinician round table, Apollo Health). This is in contrast to

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other countries where post-pandemic A/H1N1pdm09-predominate seasons have been relatively mild.

In our study we provide possible genetic explanations for the circulation of a severe A/H1N1pdm09 virus in India which has yet to be articulated in the literature. We provide evidence of increased positive selection within the antigenic sites of HA (Figure 5.2) among Indian A/H1N1pdm09 strains relative to globally circulating strains that could explain the severity of recent epidemics observed there (Table 5.2). Progressive selection at HA antigenic sites (i.e. antigenic drift) is principally responsible for the seasonal emergence of influenza A epidemics worldwide (Petrova and Russell 2018), and higher rates of selection at antigenic sites are known to drive the increased frequency and severity of A/H3N2 epidemics (Hay et al. 2001). Residue changes within the four antigenic sites of A/H1N1pdm09 HA (Sa, Sb, Ca and Cb) continue to be detected worldwide (Yasuhara et al. 2017) including S185T (Horm et al. 2014; Nguyen et al. 2015; Pandey et al. 2018) detected in our study (Table 5.2; Sb domain).

Residue change S185T was observed in the majority of our extant Indian sample

(N = 473/643; 77.2%). Increased selection at position 186 however has not previously been reported. Residue changes at this position have been observed overseas among cases with severe disease concurrent with other residue changes also detected in our study (Table 5.2) such as S185T and D222N/G

(Houng et al. 2012; Ledesma et al. 2012; Ramos et al. 2013; Ruggiero et al. 2013).

Outside of typical determinates associated with excess influenza mortality such as reduced access to health care in India (Murray, et al. 2006; Dawood, et al.

2012; Simonsen, et al. 2013), selection at these sites together provide possible genetic explanations for the unusual severity of recent epidemics in India. At site 186 particularly, the equivalent position in A/H3N2 viruses (position 189) is one of seven key positions within HA capable of generating antigenically novel

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variants from a single residue change (Koel, et al. 2013). Therefore, residue changes at this site, such as A186T, may constitute new antigenic determinates of A/H1N1pdm09 severity. This could have substantial public health implications for future epidemic prevention, control and response within India, but also for risk prediction around the world. However, the low frequency of residue changes at position 186 in our sample (N=8/613; 1.31%) means these mutations might also be sporadic. Yet, the robust evidence for increased selection relative to internationally circulating A/H1N1pdm09 viruses (Table

5.2) suggests an unusual situation could be occurring in India which warrants further investigation. Future studies such as haemagglutinin inhibition (HI) assays will be necessary to provide empirical evidence of the potential antigenic implications of residue changes at position 186 in A/H1N1pdm09 rather than position 189 in A/H3N2.

Our analysis also detected significantly increased selection at positions

84 and 256, relative to overseas viruses (Table 5.2). Along with position 163 which we found to be under increased selection in India (dN/dS = 3.83; BCI = 2.68

– 5.35) but not significantly more than overseas (Table 5.2: BCI = 1.79 – 6.38), residue changes at these positions such as S84N, K163Q, and A256T (along with

S185T mentioned previously) were frequently observed in our sample and are known to be characteristic of clade 6B and 6B.1 (S84N) A/H1N1pdm09 strains

(Arellano-Llamas et al. 2017). Clade 6B viruses are further defined by residue changes D97N and K283E in HA1, yet only position 97 and not 283 observed increased selection (dN/dS = 3.14; BCI = 2.06 – 4.33) relative to overseas

H1N1pdm09 viruses (Supplementary Table S5.5). Overall 36.1% (N=221/613) of our sample comprised clade 6B H1N1pdm09 viruses, the majority of which were collected in 2015 (Supplementary Table S9). Clade 6B strains have been detected globally since 2012 (Arellano-Llamas, et al. 2017) and our results show

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sporadic detection of 6B resides since then (Supplementary Table S5.9) contrary to previous analyses (Parida et al. 2016; Nakamura et al. 2017) which claimed emergence in India in 2015. Notably, a previous study from 2014 found an association between clade 6B viruses with reduced immune responses among younger and middle-aged persons born after 1985 (Linderman et al. 2014). As the majority of sequences isolated during the severe 2015 season (96.1%;

N=141/147) comprised of 6B viruses, the increased selection at these sites relative to overseas viruses (excluding K163Q and K283E) provides additional explanation concerning the increased morbidity and mortality observed in India during that season. Following the widespread global circulation of clade 6B and subclade 6B.1 (S84N, S162N, and I216T) residues, the A/H1N1pdm09 vaccine strain was updated for the first time since 2009 (from A/California/7/2009 to

A/Michigan/45/2015) during the 2017–2019 Southern Hemisphere influenza seasons. The 2019–2020 Northern Hemisphere recommendation includes another updated A/H1N1pdm09 vaccine strain, A/Brisbane/02/2018 from clade

6B.1A, which includes additional residue changes. Since the uptake of the influenza vaccine however remains low in India, the increased selection and significant emergence of clade 6B in 2015 and 6B.1 in 2017 could have contributed to the high morbidity and mortality observed there. Increasing influenza vaccination rates should help to reduce the impact of future

A/H1N1pdm09 epidemics in India.

Finally, we calculated selection ratios at position 222 up to eight times greater than the equivalent sample of globally circulating A/H1N1pdm09 (Table

5.2), the highest of all sites detected. Variants at this position, particularly

D222G/N are known to preferentially bind to α2,3-linked sialic acid (α2,3-SA) receptors necessary for lower respiratory tract colonisation in humans (Abed et al. 2011; Belser et al. 2011) and have demonstrated an associated 11% increase in

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morbidity and 23% increase in mortality in cases infected with D222G/N mutants (Vazquez-Perez et al. 2013; Goka et al. 2014). Earlier studies have shown A/H1N1pdm09 strains circulating in India in 2014 had acquired residue changes D222N in HA (Tharakaraman and Sasisekharan 2015; Mukherjee et al.

2016) although this was contested by the National Institute of Virology in Pune.

In our sample of 613 Indian taxa we identified both G222 and N222 residues, albeit at low frequencies, 3.1% (N=19/613) and 1.6% (N=10/613) respectively, and only once in 2015. Like position 186, the sporadic detection of residue changes at position 222 means the impact on the observed morbidity and morbidity in

India is not clear. However, the disproportionate increase in selection at position

222 (Table 5.2) further exemplifies the unusual epidemiological situation in the country. Because short-term selection is a stochastic process (Nelson et al. 2006) but long-term is known to involve complex interactions between

A/H1N1pdm09 evolution, host response and human behaviour (Petrova and

Russell 2018), the observed increase in pervasive positive selection in India may therefore be the result of between-host transmission characteristics unique to the country and warrants further investigation (Petrova and Russell 2018).

Overall the combination of increased selection for and circulation of clade

6B genogroups, increased selection of known determinates of A/H1N1pdm09 severity such as D222N/G, and the identification of potentially new antigenic determinates of severity such as A186T, together provides credible genomic evidence that might explain the increased frequency and severity of

A/H1N1pdm09 epidemics in India. Seasonal surveillance of clade 6B residues

(and related subclades), D222G/N, and residue changes at site 186 may assist with the early detection of severe epidemics in the future.

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This study is also the first to simultaneously integrate GLM and Bayesian phylogeography analysis methods to empirically quantify the transmission dynamics of A/H1N1pdm09 in India revealing supported routes of

A/H1N1pdm09 transmission. We have identified Maharashtra state as a key location disseminating A/H1N1pdm09 to many S/UT including Rajasthan,

Tamil Nadu, Delhi, Jammu & Kashmir, Karnataka, Madhya Pradesh, West

Bengal, Assam and Kerala (Figure 5.3). These results could have important implications for surveillance, risk assessment and epidemic control strategies including vaccination in the country, as they could also be generalisable for other influenza A viruses such as A/H3N2 or other potentially pandemic variants that could emerge in the future. For example, India, along with East and Southeast Asia has been shown to be a significant source for globally circulating influenza A/H3N2 viruses (Bedford, et al. 2015). The emergence of a novel pandemic influenza A strain in India therefore represents a significant risk to global health. Understanding the transmission patterns of influenza A within

India allows for rapid risk assessment not only within the country, but also risk for subsequent spread around the world.

The identification of key climactic, demographic and ecological factors associated with A/H1N1pdm09 transmission within India similarly indicates opportunities for targeted interventions during outbreaks that could also be generalisable among S/UT not included in our model. For example, increasing distance between S/UT was the most significant (BF = 269.3) factor identified as contributing to the model and was associated with the prevention of transmission (Table 5.3). Other preventative factors while immutable included increasing population density and average temperature by the originating S/UT.

This suggests that S/UT with lower average temperatures are more likely to transmit A/H1N1pdm09 to other S/UT in India. This appears consistent with

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previous biological evidence suggesting cooler temperatures are favourable for influenza transmission (Lowen et al. 2007; Lowen and Steel 2014) and the known seasonality of influenza in India where large temperature variations are observed between Northern India and Southern India during winter (Koul et al.

2014; Chadha et al. 2015). High passenger flux by both origin and destination was another factor shown to be on average predictive of transmission between

S/UT in India, but the evidence was inconclusive (Table 5.3). Taken together, public health decision makers might choose to prioritise efforts restricting human movement between neighbouring S/UT, and potentially restricting key domestic flights with high passenger numbers.

Overall this study has some key limitations. First, sample representativeness is a key limitation of any epidemiological study, including phylogeography. Only 14 S/UT were included in our model due to limited sample availability or unrepresentative sampling (5 states with samples less than two sequences per 10 million population were removed). This includes less than half of the 36 S/UT in India. Ancestral state reconstruction methods like those used by discrete-trait phylogeography can only generate inferences from traits assigned to the extant sequences. For example, no sequences were available from Gujarat, which has been affected by particularly large outbreaks

(approximately 7,100 cases in 2015 and 7,700 in 2017). This means that transmission to and from Gujarat while perhaps expected, remains unresolved in our model. Limited sample availability may also have the effect of exaggerating the evidence of transmission between any two pair-wise locations in the model, for example where Gujarat or another unsampled state may have acted as intermediate locations for transmission. Sample size by origin was also implicated as a predictor of transmission in the complete India dataset (N=613), which suggests sampling bias may be affecting our primary results. As an

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example, Maharashtra was the most supported location for transmission to other S/UT, but also had the most sequences available (N=242/613; 39.5%).

Discrete-trait Bayesian phylogeography is known to be susceptible to sampling bias and, in some cases, overestimate origin support (De Maio et al. 2015; Magee et al. 2017). It is reassuring however that the results from each of the five randomly down-sampled datasets showed not dissimilar evidence for transmission from Maharashtra and other S/UT (Supplementary Figure S4,

Table S7). Furthermore, Maharashtra also endured the largest recorded outbreaks (approximately 8,500 cases in 2015 and 6,100 cases in 2017) and had the highest average number of domestic passengers between 2009 and 2017 suggesting our primary results may in fact reflect the truth. However, measures of transit between states in our GLM model only extended to domestic aviation.

Other significant means of interstate travel in India such as rail (nationally 8.1 billion passengers in 2017) was not considered in our model due to the lack of data by S/UT. We hypothesise that rail-travel would have a significant effect on

A/H1N1pdm09 transmission in India, particularly over short distances, however the absence of this data does not affect the overall interpretation of our results. Rather, any effect of rail-travel remains simply unresolved. Future studies could investigate the effect of rail-travel if state-based data were to become available.

Lastly, only HA sequences were included in our analysis due to the limited availability of whole A/H1N1pdm09 genomes from India. Residue variations among other genes such as PB2 have been shown to affect viral replication in other influenza A viruses such as A/H5N1 which are associated with increased morbidity and mortality (Gao et al. 2009). We cannot know if other residue changes may be driving the frequency and severity of

A/H1N1pdm09 outbreaks in India, rather than selection within HA. Future

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studies however could investigate selection pressures and residue variations within PB2, among the other gene segments of influenza A.

5.6 Conclusion

Our findings have important implications in understanding the dynamics of influenza A/H1N1pdm09 transmission and evolution in India which could inform future public health prevention and control efforts in the country. We have identified increased selection pressure at multiple meaningful

HA residue positions including site 222 and clade 6B characteristic residues relative to internationally circulating viruses that may explain the high CFR observed there, particularly the abnormally severe 2015 and 2017 seasons, and signifies a link to the unique public health burden of the virus in India. We believe this is the first study to observe increased selection at site 186 and believe this site could be a potential determinate of A/H1N1pdm09 severity. Future investigations however are warranted to confirm the antigenic potential of residue changes at this position and the associated impact on morbidity and mortality. We have revealed national routes of A/H1N1pdm09 transmission in

India identifying Maharashtra as the most supported state for spread throughout the country while quantifying climactic, ecological and transport factors as drivers of within-country transmission. Together these results have important implications for future A/H1N1pdm09 surveillance and control within India but also for epidemic & pandemic risk prediction around the world. Strengthening influenza surveillance capacity in the country should remain a priority, first to improve estimates of severity, and second, to prepare for future epidemics & pandemics. Continuous monitoring of haemagglutinin changes remains vital for public health surveillance in India.

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Chapter Six

Thesis Conclusion

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6.1 Thesis Overview, Key Findings & Strengths

This thesis aimed to utilise methodologies and models in Bayesian phylogenetic analysis to characterise the molecular epidemiology, evolution and phylogeography of various pathogens of public health significance and uncover clinical and spatial patterns of disease in order to inform future outbreak surveillance, prevention and control activities. Part A of Chapter One introduces these pathogens and outlines their significance to public health, and in Part B, the methods utilised in their analysis are introduced and described in detail.

In Chapter Two, statistical analysis and Bayesian phylogenetic trait- association methods were used to answer persistent clinical questions regarding variations in disease severity among paediatric cases between rhinovirus species using a unique linked dataset of symptomatic features and corresponding genetic data. Chi-square tests were used to show that RV-C was significantly more likely to be isolated from paediatric cases under two years of age compared to RV-A while Bayesian phylogenetic trait-association provided some evidence of this association by age within the VP4/VP2 capsid coding protein region. This suggested potential age-specific variations in infectivity among rhinovirus species but also subtypes between species. However, no significant associations between rhinovirus species and symptom presentation was observed from either statistical or phylogenetic analysis. This study demonstrated the novel application of Bayesian phylogenetic trait-association methods to investigate the relationship between genotype and phenotype that can be hidden in traditional epidemiology studies. As sequencing is increasingly being used for routine surveillance and diagnostics of rhinovirus infection, the demonstrated utility of this is method is of particular benefit to

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future public health and clinical research. This chapter established the use of basic Bayesian phylogenetic reconstruction methods and the BEAST software application in this thesis which was the foundation of all subsequent chapters’ methodologies.

In Chapter Three, temporal and spatial sampling meta-data was integrated with publicly available genetic data using discrete-trait phylogeography modelling to uncover the recent history of smallpox transmission world-wide. This study is the first to investigate historic smallpox transmission using phylogeography models and the first describe two discrete clades of variola major circulating between East Asia and Europe at the end of

WWII. This study also demonstrated that in-lieu of whole genome sequencing, multi-locus models combining commonly sequenced gene targets HA

(hemagglutinin), ATI (A-type inclusion protein) and CrmB (cytokine response modifier B) can differentiate between high, intermediate and low case-fatality clades within variola major and variola minor. These results can be used to inform rapid real-time outbreak response and control activities such as directing resources and interventions in the field should smallpox re-emerge in the future potentially via accidental or deliberate release. A strength of this study was the systematic collection and combination of all publicly available smallpox genomes with additional isolates of smallpox partially sequenced for HA. By supplementing whole genome analyses with additional isolates sequenced for

HA only, we achieved an improvement in sample representativeness without significantly increasing uncertainty of the phylogeny and corresponding phylogeographic inference. This chapter also established the use of discrete-trait phylogeography modelling in this thesis for use in subsequent chapters.

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In Chapter Four, discrete-trait phylogeography and generalised linear modelling was used to investigate the regional dynamics and spread of influenza A/H3N2 within and between Australia and New Zealand using the

2017 season as a case study. This was the first time phylogeography and GLM methods have been used to investigate the spread of influenza in the region and provided empirical evidence of frequent reciprocal transmission along the southeast coast of Australia between New South Wales, Victoria, and South

Australia. There was strong evidence of transmission from Australia to New

Zealand via New South Wales but no evidence supporting transmission from

New Zealand to Australia. Results from the GLM analysis identified positive associations between capital city population densities and negative associations with distance which supports our hypothesis of intense transmission between highly populated and adjacent capital cities in Australia i.e. New South Wales,

Victoria and South Australia. The role of covariates such as humidity, temperature and airline travel on transmission however was inconclusive in this study. Molecular characterisation results demonstrated overall similarities in clade composition overtime within and between Australian and New Zealand despite differences in observed seasonal severity. This indicates that epidemiological factors such population immunity most likely explain the differences in seasonal activity and severity observed between the two countries during 2017 but warrants further investigation. Potential viral severity factors are also suggested, such as the frequency of T135K/N antigenic mutants. The results of this study can be used to inform regional surveillance of seasonal

A/H3N2 including future risk assessment, as well as seasonal control strategies and vaccine composition recommendations. Strengths of this study include the use of both HA and NA genes in multi-locus models, as well as the systematic and robust subsampling strategy used to limit and evaluate the effects of over-

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representative sampling. Additional strengths include extensive model testing with path sampling and stone stepping sampling methods identifying demographic, evolutionary and molecular clock models to fit the given multiple sequence alignment. This chapter established the use of extensive model testing and extended Bayesian GLM methods in this thesis.

In Chapter Five, the complete evolution, phylodynamics and phylogeography of pandemic influenza A/H1N1pdm09 in India from emergence in 2009 and subsequent seasonal spread through 2017 was investigated. This was the first study to investigate the evolution of

A/H1N1pdm09 in India across multiple years and uncovered the hidden demographic history of seasonal A/H1N1pdm09 epidemics in the country using coalescent Bayesian Skygrid modelling. This indicated case under- ascertainment was significantly affecting case-fatality estimates which was orders of magnitude higher than observed in other countries. However, this also suggested the likely emergence of an antigenically novel A/H1N1pdm09 strain in the population in 2015 and 2017 where unprecedented seasonal spread overwhelmed the healthcare systems. Selection analysis within Bayesian and maximum likelihood phylogenetic frameworks identified a number of unique sites under significant positive selection compared to internationally circulating strains. Of those, many were characteristic markers of 6B and 6B.1 clades which could have contributed to the high morbidity and mortality observed in 2015 and 2017 respectively. This has implications for future surveillance and vaccination policy in the country but also epidemic & pandemic risk prediction around the world. This study also uncovered national routes of A/H1N1pdm09 transmission while quantifying climactic, ecological and transport factors as drivers of transmission. Maharashtra state was identified as the most important location for the spread throughout India, while distance was negatively

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associated with spread. Population density and temperature however was positively associated with spread. There was some evidence that domestic airline travel was also predictive of transmission. In the event of future pandemics or epidemics, these results could be used to inform decision makers who might choose to prioritise efforts restricting human movement between neighbouring regions, and potentially restricting key domestic flights with high passenger flux. Strengths of this study include extensive model testing, robust comparison to internationally circulating A/H1N1pdm09 strains and a subsampled phylogeography analysis performed to account for potential biased sampling. This study also used the most comprehensive sequence dataset of

A/H1N1pdm09 annotated with temporal and spatial metadata available from

India. The cumulation of this thesis is represented by this chapter where the use and implications from numerous Bayesian phylogenetic models including phylodynamic, phylogeographic and GLM methods is evident.

6.2 Limitations of Methods

As demonstrated by this thesis, Bayesian phylogenetics, the BEAST software framework and its associated models, are increasingly positioned to supplement traditional approaches to research and practice in public health, particularly in the field of infectious disease epidemiology from routine surveillance, to outbreak investigation, response and control. However, key limitations remain. Most critical to this conclusion chapter is over and under representative sampling and related biases in phylogeography.

In Chapter Three, phylogeography models that only considered a subset of all available whole genome sequences concealed transmission events that were otherwise resolved when supplemented with additional partially sequenced isolates. This approach was primarily utilised to evaluate the utility

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of such supplemented phylogeography analyses for the purposes of rapid outbreak response in the event of potential smallpox re-emergence. However, as a secondary aim, revealed the importance of under-representative sampling and its potential to limit inferences of transmission. A similar effect of under- representative sampling is evident and discussed as a significant limitation in

Chapter Five, where sequence data was only available from 14 out of 36 states and union territories (S/UT) in India. In this case, potential transmission to and from S/UT not included in the model could not be resolved. Accounting for these limitations however is particularly difficult because discrete-trait phylogeography models can only generate transmission inferences between locations with associated sequence data. This loss of inference however can in some instances be accounted for through the use of continuous-trait phylogeography models. By defining locations within a continuous two- dimensional landscape, e.g. geospatial coordinates, transmission within intermediate locations between those included in the model can be roughly inferred. However, the application of such models is necessarily restricted by the availability of sufficiently detailed spatial metadata at the level of cities or towns for example. In most instances, these data do not exist and therefore represents a significant limitation impacting this thesis e.g. choices had to be made prior to either analysis regarding the viability of each approach given the availability of both sequence and spatial metadata. Furthermore, the GLM extension of the discrete-trait phylogeography model used in Chapters Four and

Five of this thesis has yet to be implemented for the continuous-trait variant.

Until such a time, these limitations concerning the loss of inference due to under representative sampling and lack of sufficient spatial metadata must be acknowledged. Otherwise, supplementing datasets with additional isolates sequenced for alternative genome regions, as in Chapter Three, may be an

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appropriate interim strategy. The likely increase in the uncertainty of the posterior topology however also needs to be considered in such instances.

Loss of transmission inference is not the only limitation associated with unrepresentative sampling in Bayesian phylogeography. In Chapter Four, where sequence availability was not limited (N=1,110) and was available from all necessary locations, over representative sampling however was a concern.

Discrete-trait phylogeography models have been shown to be susceptible to overconfident estimates of transmission origin when sampling is unrepresentative (De Maio, et al. 2015; Frost et al. 2015; Magee, et al. 2017). This can bias evidence of transmission from locations with disproportionately high sampling, while concealing evidence of transmission between others.

Subsampling is one approach that can be implemented to partially account for this effect and can also help satisfy prior assumptions of the coalescent model that the data represents a random sample from the population (Rambaut and

Holmes 2009). An example of subsampling can be seen in Chapters Four and

Five. In Chapter Five, Maharashtra state was shown to be a significant and persistent source of influenza A/H1N1pdm09 transmission to most S/UT in

India (excluding those that lacked sequence data as mentioned in the previous paragraph). Maharashtra however also represented the state with the greatest number of sequences, and the largest outbreaks. A subsampling procedure was employed to investigate the potential that over-representative sampling could have biased these primary results. Fortunately, little difference could be seen between the phylogeography results of the original dataset and the subsampled dataset (Figure 5.3 & Supplementary Figure S5.4). A similar example can be seen in Chapter Four, where phylogeography and GLM analyses was replicated and the results compared across each subsample. Despite these efforts however, an indication of potential bias could be seen after the inclusion of sample size by

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location as a covariate in the GLM analysis. This method of incorporating sample size by location is often used in GLM analyses to measure the presence and degree of bias in the data (Scotch, et al. 2013; Magee, et al. 2017; Bui, et al.

2018). In this case, sample size was shown to be a positive predictor of transmission by origin in both the original and subset analyses. Again, continuous-trait phylogeography models have been proposed help to reduce overconfidence in estimates of transmission, however they still remain susceptible to any sampling bias present in the data (Lemey et al. 2010).

Alternative methodologies, such as structured coalescent approximations (De

Maio, et al. 2015; Müller, et al. 2017), have been shown to effectively account for biased sampling effects to a degree, however have only very recently seen a similar covariate extension of the discrete-trait GLM implemented in BEAST

(Müller et al. 2019), and thus was not available for most of this thesis. Future studies could aim to utilise these new phylogeography and GLM models to account for sampling biases. However, they should be implemented alongside various subsampling strategies, as well as the inclusion of sample size by location as covariates in the GLM to indicate any remaining biases.

Lastly, the potential for model misspecification is inherently an issue in

Bayesian phylogenetics. Model misspecification occurs when the models and priors selected are not appropriately suited to explain the data provided

(Nascimento, et al. 2017). If the model is inappropriate, the resulting phylogeny can be flawed (Kelsey et al. 1999), along with any subsequent phylodynamic and phylogeographic inference dependant on the accuracy of dated phylogenetic trees. In lieu of testing all available potential model variations independently, for which there are hundreds, there is little that can be done account for these limitations beyond the model evaluation procedure implemented in both

Chapters Four and Five. It is also otherwise accepted, that even the best

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available model is only an approximation of the true underlying evolutionary and demographic process. Improving the accuracy of available models and the development of new models is as an entire field of research in and of itself and is beyond the scope of this thesis. However, recognition and acknowledgement of these inherent limitations and uncertainty is essential.

6.3 Implications & Recommendations

Through the application of Bayesian phylogenetic methods and models, this thesis has contributed to the overall body of knowledge concerning the clinical and spatial epidemiology of rhinoviruses, variola virus (smallpox) and influenza A H3N2 and H1N1pdm09. This thesis has demonstrated the utility of

Bayesian phylogenetic methods and models available in BEAST in characterising the molecular epidemiology, evolution and phylogeography of these pathogens, and provides results and methodologies that could be used to inform future surveillance, outbreak prevention and control activities. This could include rapid sequencing and analysis strategies, the prioritisation and direction of resources during outbreaks, and other targeted interventions such as border closures. Real-world examples include the current COVID-19 pandemic which has as of 31 March 2020 has resulted in 785,979 confirmed cases and 37,686 confirmed deaths world-wide (Dong et al. 2020). Through the application of similar Bayesian phylogeography methods utilised in this thesis, the global spread of SARS-CoV-2 (the viral agent of COVID-19) has recently been visualised, revealing critical international travel routes that has seeded sustained transmission around the world (Bedford et al. 2020). Various phylodynamic models have been implemented to uncover additional epidemiological and virological parameters such as evolutionary growth rates

(Rambaut 2020) and estimates of the true background incidence of unreported

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infection (Volz et al. 2020) likely the result of mild or asymptomatic SARS-CoV-

2 infection (Bai et al. 2020). Our results also include a critical evaluation of the effects of sample size and sample representativeness primarily in phylogeography studies, while demonstrating potential solutions and their outcomes. This thesis has highlighted the value of publicly available and well annotated pathogen sequence data for research in public health. Based on the results of this thesis, and points raised in this conclusion chapter, we have prepared the following recommendations:

1. Increase pathogen sequencing during the routine surveillance

of high priority or understudied pathogens such as rhinovirus.

This also primarily concerns seasonal influenza A viruses

within India, which are widely tested for but not equivalently

sequenced. This will also allow for better estimates of subtype

prevalence in the community and more accurate inferences of

transmission. Additional sequencing and subtyping of

influenza A in Australia and New Zealand is also similarly

desirable but not a priority.

2. If sequencing is performed, the collection and record of the

complete date of sampling is highly recommended. Without

temporal sampling metadata, calibration of the molecular

clock and generation of phylodynamic inference is not

possible. Often valuable sequence data is available but

excluded from already limited datasets due to the lack of this

information. Improving the collection and record of temporal

sampling metadata where sequence is performed should be a

priority.

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3. Where sequence data is annotated with date of sampling

metadata, a third priority is the record of spatial information

for the purposes of phylogeography modelling. This should

aim to be at least below the country level. As before with

recommendation 2, valuable temporally annotated sequence

data will be excluded from studies that aim to investigate

detailed transmission patterns. Often country of sampling is

recorded, which allows global phylogeography analysis,

however, increasing the resolution of the location data permits

finer regional and local analyses. Increasingly precise location

information, e.g. at the level of towns also permits the

application of continuous-trait phylogeography models which

are less susceptible to intrinsic sampling biases.

4. After this, the collection and record of associated case data is

similarly of great value in Bayesian phylogenetics. These can

include host symptom features, hospital admission and

disease severity indicators. With this information, any

potential associations between traits and phylogenetic clusters

or clades could be readily considered, providing greater

insights into patterns of disease. This would be very useful for

studies of influenza A, which otherwise relies on population-

level estimates to indicate overall seasonal severity.

5. Lastly, given all this data is collected, making it publicly

available to the global community via online repositories such

as GISAID and GenBank would accelerate impactful

discoveries in pathogen dynamics. These discoveries could be

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used to inform routine surveillance practice, vaccine

composition recommendations, and outbreak response,

prevention and control activates

As the availability of pathogen sequence data is expected to increase, the appeal of the methods and models implemented in this thesis and related insights will likely grow. The ongoing development of new methods and models in Bayesian phylogenetics is required to enhance our understanding of significant pathogens such as those under investigation in this thesis. However, the overall utility and impact of these and future models will necessarily require adherence to the five recommendations above. Doing so will help support discoveries in the field of pathogen dynamics and through their application, reduce the burden of infectious diseases globally.

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Appendix A: Supplementary Results

Table S2.1: RV-specific primers used for 5’UTR and VP4/VP2 amplification

PCR Primer For 5′ GARCAAGYACTYCTGTYWCCCCGG—EV140 Sequencing 5′UTR Rev 5′ ACACGGACACCCAAAGTAGTCGGTTCC—EV170 For 5′ GCCCCTGAATGYGGCTAA—Rhi3A Sequencing VP4-VP2 Rev 5′ GGTAAYTTCCACCACCANCC—EVVP4

Figure S2.1: Regression of root-to-tip divergence against sampling time for 5’UTR (left) and VP4/VP2 (right) of RV demonstrating sufficient temporal signal for time-scaled phylogenetic analysis.

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Table S2.1: Complete RV sequence characterization by species and subtype including study metadata and GenBank accession Accession Study ID Collection Date Species Subtype Gene Segment Identify KF543874 MS06-2 2006-09-18 A RV-A89 5’UTR and VP4/VP2 JQ837719 (98.4%) KF543875 MS06-3 2006-09-21 A RV-A12 5’UTR and VP4/VP2 JF781511 (99.3%) KF543876 MS06-4 2006-09-29 A RV-A61 5’UTR and VP4/VP2 MH648013 (97.3%) KF543878 MS06-7 2006-10-11 C RV-C11 5’UTR and VP4/VP2 KY369877 (96.2%) KF543879 MS06-8 2006-10-20 C RV-C02 5’UTR and VP4/VP2 KY369881 (97.8%) KF543880 MS07-1 2007-03-21 A RV-A49 5’UTR and VP4/VP2 MH648031 (99.6%) KF543881 MS07-2 2007-03-29 A RV-A28 5’UTR and VP4/VP2 JN798580 (99.1%) KF543884 MS07-5 2007-04-17 C RV-C11 5’UTR and VP4/VP2 EU840952 (98.7%) KF543885 MS07-6 2007-04-24 C RV-C28 5’UTR and VP4/VP2 JX025557 (98.8%) KF543886 MS07-7 2007-05-22 C RV-C32 5’UTR and VP4/VP2 JQ994498 (98.6%) KF543887 MS07-9 2007-06-04 C RV-C00 5’UTR and VP4/VP2 JF317015 (98.2%) KF543888 MS07-13 2007-08-01 C RV-C11 5’UTR and VP4/VP2 EU840952 (98.7%) KF543889 MS07-14 2007-08-26 C RV-C26 5’UTR and VP4/VP2 KP890664 (98.8%) KF543891 MS07-16 2007-09-04 A RV-A31 5’UTR and VP4/VP2 MH648060 (99.4%) KF543892 MS07-17 2007-08-04 C RV-C32 5’UTR and VP4/VP2 JQ994498 (98.8%) KF543894 MS07-19 2007-09-19 C RV-C11 5’UTR and VP4/VP2 EU840952 (98.7%) KF543895 MS07-20 2007-09-24 A RV-A57 5’UTR and VP4/VP2 KM109994 (97.6%) KF543897 MS07-23 2007-10-18 A RV-A20 5’UTR and VP4/VP2 JQ994494 (99.4%) KF543898 CS07-1 2007-05-01 C RV-C02 5’UTR and VP4/VP2 KY369881 (95.4%) KF543899 CS07-2 2007-05-01 A RV-A20 5’UTR and VP4/VP2 JN541270 (98.1%) KF543900 CS07-4 2007-05-03 A RV-A22 5’UTR and VP4/VP2 KY342346 (98.5%) KF543901 CS07-6 2007-05-06 A RV-A15 5’UTR and VP4/VP2 JN541268 (98.9%) KF543902 CS07-7 2007-05-28 A RV-A24 5’UTR and VP4/VP2 JF285328 (97.8%) KF543903 CS07-8 2007-05-11 C RV-C16 5’UTR and VP4/VP2 KR997882 (96.7%) KF543904 CS07-9 2007-05-12 A RV-A01 5’UTR and VP4/VP2 MF973194 (98.7%) KF543908 CS07-18 2007-05-16 A RV-A01 5’UTR and VP4/VP2 JN815255 (98.7%) KF543909 CS07-24 2007-05-24 A RV-A56 5’UTR and VP4/VP2 EU840727 (93.5%) KF543910 MS08-1 2008-05-14 C RV-C02 5’UTR and VP4/VP2 KY369881 (96.8%) KF543914 MS08-5 2008-09-16 C RV-C37 5’UTR and VP4/VP2 KP737048 (98.8%) KF543915 MS08-6 2008-09-17 A RV-A43 5’UTR and VP4/VP2 JX129400 (99.0%) KF543918 CS09-2 2009-05-27 A RV-A67 5’UTR and VP4/VP2 JN621245 (99.8%) KF543919 CS09-3 2009-05-27 A RV-A33 5’UTR and VP4/VP2 JX129418 (97.6%) KF543922 CS09-6 2009-05-27 A RV-A21 5’UTR and VP4/VP2 JN837693 (99.0%) KF543929 CS09-17 2009-05-28 A RV-A21 5’UTR and VP4/VP2 JX129450 (99.3%) KF543937 CS09-27 2009-06-05 A RV-A15 5’UTR and VP4/VP2 JN541268 (98.9%) KF555323 MS06-1 2006-09-12 C RV-C00 5'UTR LC420535 (99.4%) KF555324 MS06-5 2006-09-29 C RV-C00 5'UTR LC420535 (99.4%) KF555326 MS07-8 2007-05-30 C RV-C00 5'UTR JX129430 (99.6%) KF555327 MS07-10 2007-06-12 C RV-C00 5'UTR HM581877 (99.4%) KF555329 MS07-12 2007-07-11 B RV-B91 5'UTR MK859163 (99.8%) KF555330 MS07-22 2007-10-18 A RV-A49 5'UTR FJ445134 (99.4%) KF555332 CS07-5 2007-05-05 B RV-B17 5'UTR JX074054 (99.2%) KF555335 CS07-15 2007-05-16 C RV-C00 5'UTR MF978824 (98.9%) KF555336 CS07-16 2007-05-16 A RV-A01 5'UTR MH648026 (98.7%) KF555337 CS07-17 2007-05-17 C RV-C06 5'UTR JN990702 (98.5%) KF555338 CS07-19 2007-05-18 C RV-C00 5'UTR KP890662 (98.7%) KF555340 CS07-21 2007-05-19 C RV-C40 5'UTR JQ245963 (99.7%) KF555341 CS07-22 2007-05-19 C RV-C00 5'UTR MF978815 (99.4%) KF555344 MS08-7 2008-10-01 A RV-A71 5'UTR KM109974 (99.6%) KF555348 CS09-16 2009-05-28 B RV-B06 5'UTR MH648100 (98.9%) KF555349 CS09-18 2009-05-28 A RV-A00 5'UTR MK859104 (98.5%) KF555350 CS09-19 2009-05-28 B RV-B48 5'UTR KY348863 (98.9%)

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Table S3.1: Compiled records of VARV isolates sequenced as full genomes and used for Bayesian phylogeography.

# Accession Isolate name Isolation Geographic origin Description

1 DQ437580 AFG70_vlt4 1970-03-18 Afghanistan Variolator-4 2 DQ437581a BSH75_banu 1975-11-24 Bangladesh V75-550 3 DQ437582 CHN48_horn 1948 China China Horn Sabin lab 4 DQ437583 CNG70_46 1970 Congo region V70-46 Kinshasa 5 DQ437584 GER58_hdlg 1958 Germany Heidelberg from India 6 DQ437585 IND64_vel4 1964 India 7124 Vellore 7 DQ437586 IND64_vel5 1964 India 7125 Vellore 8 DQ437587 IRN72_tbrz 1972 Iran Iran 2602 Tabriz 9 DQ437588 NEP73_175 1973-07-26 Nepal V73-175 10 DQ437589 PAK69_lah 1969-03-03 Pakistan Rafiq Lahore 11 DQ437590 SOM77_ali 1977-11-10 Somalia V77-2479 last case 12 DQ437591 SUM70_222 1970-10-17 Sumatra V70-222 13 DQ437592 SYR72_119 1972-04-06 Syria V72-119 14 DQ441416 BEN68_59 1968-04-10 Benin V68-59, Dahomey 15 DQ441417 BOT72_143 1972-04-26 Botswana V72-143 16 DQ441418 BOT73_225 1973-10-08 Botswana V73-225 17 DQ441419 BRZ66_39 1966-06-05 Brazil V66-39 alastrim 18 DQ441420 BSH74_nur 1974 Bangladesh Nur Islam 19 DQ441421 BSH74_shz 1974 Bangladesh Shahzaman 20 DQ441422 BSH74_sol 1974 Bangladesh Solaiman 21 DQ441423 CNG70_227 1970-03-12 Congo region V74-227 Gispen Congo 9 22 DQ441424 ETH72_16 1972-08-29 Ethiopia Eth16 Addis 23 DQ441425 ETH72_17 1972-08-29 Ethiopia ETH72_17 Eth17 Addis 24 DQ441426 GUI69_005 1969 Guinea V69-005 Guinea 25 DQ441427 IND53_mad 1953-09-06 India Kali-Muthu-Madras 26 DQ441428 IND53_ndel 1953 India New Delhi 27 DQ441429 JAP46_yam 1946 Japan Yamada MS-2A Tokyo 28 DQ441430 JAP51_hrpr 1951 Japan Harper Masterseed 29 DQ441431 JAP51_stwl 1951 Japan Stillwell Masterseed 30 DQ441432 KOR47_lee 1947 Korea Lee Masterseed 31 DQ441433 KUW67_1629 1967-05-07 Kuwait K1629 32 DQ441434 NIG69_001 1969 Nigeria import from Nigeria 33 DQ441435 SAF65_102 1965-04-12 South Africa 102 Natal, Ingwavuma 34 DQ441436 SAF65_103 1965-04-14 South Africa 103 T'vaal, Nelspruit 35 DQ441437 SLN68_258 1968-01-02 Sierra Leone V68-258 36 DQ441438 SOM77_1252 1977-05-19 Somalia V77-1252 37 DQ441439 SOM77_1605 1977-08-09 Somalia V77-1605 38 DQ441440 SUD47_jub 1947-10-07 Sudan Juba (alastrim-like) 39 DQ441441 SUD47_rum 1947 Sudan Rumbec 40 DQ441442 SUM70_228 1970-10-26 Sumatra V70-228 41 DQ441443 TAN65_kem 1965-09 Tanzania Kembula 42 DQ441444 UNK44_harv 1944 UK Harvey Middlesex 43 DQ441445 UNK46_hind 1946 UK Hinden 44 DQ441446 UNK47_hig 1947-04-06 UK Higgins Staffordshire 45 DQ441447 UNK52_but 1952 UK Butler alastrim 46 DQ441448 YUG72_164 1972 Yugosalvia Yugoslavia from Iraq 47 X69198b IND67_mah 1967 Brazil Vector Maharastra E6 48 Y16780 BRZ66_gar 1966 Brazil Garcia alastrim

# Accession Isolate name Isolation Geographic origin Description

1 BK010317c VD21 1654 Lithuania KY358055 2 LT706528 V563 1929 Czech N/A 3 LT706529 V1588 1925 Czech N/A

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Table S3.2: Compiled data of VARV isolates sequenced for HA and uploaded to GenBank. Isolates are cross referenced with WG VARV used for phylogeography. Likely unique and unique isolates (n=22) are used for analyses. Likely unique isolates share country and year of sampling with other WG isolates but have unique isolate names. Accession Isolate name Isolation Geographic origin Match to S3.1 1 AY944055 BEN68_59 1968 Benin DQ441416 2 AY944056 GUI69_005 1969 Guinea DQ441426 3 AY944057 UNK52_butler 1952 UK DQ441447 4 AY944058 BRZ66_39 1966 Brazil DQ441419 5 AY944033 NEP73_175 1973 Nepal DQ437588 6 AY944034 JAP46_yamada 1946 Japan DQ441429 7 AY944035 JAP51_harper 1951 Japan DQ441430 8 AY944036 IND53_New_Delhi 1953 India DQ441428 9 AY944037 JAP51_stillwl 1951 Japan DQ441431 10 AY944038 SAF65_102 1965 South Africa DQ441435 11 AY944039 GER_heidel 1958 Germany DQ437584 12 AY944040 UNK47_higgins 1947 UK DQ441446 13 AY944041 UNK46_hinden 1946 UK DQ441445 14 AY944042 CHN48_horn 1948 China DQ437582 15 AY944043 IND53_mad 1953 India DQ441427 16 AY944044 TAN65_kem 1965 Tanzania DQ441443 17 AY944045 KOR47_lee 1947 Korea DQ441432 18 AY944046 SUD47_jub 1947 Sudan DQ441440 19 AY944047 SUM70_222 1970 Indonesia DQ437591 20 AY944048 AFG70_vlt4 1970 Afghanistan DQ437580 21 AY944049 IND64_vel4/7124 1964 India DQ437585 22 AY944050 IRN72_tbrz 1972 Iran DQ437587 23 AY944051 KUW67_1629 1967 Kuwait DQ441433 24 AY944052 PAK69_lah 1696 Pakistan DQ437589 25 AY944053 SYR72_119 1972 Syria DQ437592 26 AY944054 YUG72_164 1972 Yugoslavia DQ441448 27 AF375129 var-but 1952 UK DQ441447 28 AF375141 var-raf 1969 Pakistan DQ437589 29 AF375132 var-et16 1972 Ethiopia DQ441424 30 AF375133 var-et17 1972 Ethiopia DQ441425 31 AF375139 var-nur 1974 Bangladesh DQ441420 32 AF375125 var-aba 1974 Bangladesh Likely unique 33 AF375131 car-cng 1970 Congo Likely unique 34 AF375134 var-hawa 1974 Bangladesh Likely unique 35 AF375135 var-ilm 1965 Tanzania Likely unique 36 AF375136 var-jal 1974 Bangladesh Likely unique 37 AF375140 var-par 1974 Bangladesh Likely unique 38 AF375143 var-som 1977 Somalia Likely unique 39 AF375144 var-tlv 1946 UK Likely unique 40 AF375130 var-cm6 1968 Brazil Unique isolate 41 AF375138 var-nig 1961 Nigeria Unique isolate 42 AF375142 var-sln 1968 Sierra Leone Unique isolate 43 AF375126 var-af2 1972 Afghanistan Unique isolate 44 AF375127 var-af3 1971 Afghanistan Unique isolate 45 AF375128 var-bom 1958 India Unique isolate 46 AF375137 var-mad 1962 India Unique isolate 47 AF377887 Skin Lesion WWII 1944 Japan Unique isolate 48 AF377888 Skin Lesion 1946 Japan Unique isolate 49 AF377889 Skin Lesion 1945 China Unique isolate 50 AF377890 Skin Lesion 1947 Belgium Unique isolate 51 AF377891 Skin Lesion 1927 USAa Unique isolate 52 AF377892 Skin Lesion 1946 Korea Unique isolate 53 AF377893 Skin Lesion 1940 USAa Unique isolate

187

Table S3.3: Summary of geographic location of unique isolates by country, aggregate region and genetic content. Region & Country of isolation WG HA Total Asia Pacific 2 0 2 Indonesia 2 0 2 Eastern Asia 5 4 9 Japan 3 2 5 South Korea 1 1 2 China 1 1 2 East Africa & Middle East 11 2 13 Kuwait 1 0 1 Iran 1 0 1 Syria 1 0 1 Somalia 3 1 4 Ethiopia 2 0 2 Sudan 2 0 2 Tanzania 1 1 2 Europe 9 2 11 Belgium 0 1 1 United Kingdom 4 1 5 Yugoslavia 1 0 1 Germany 1 0 1 Latin America 3 1 4 Brazil 3 1 4 Southern Africa 6 1 7 Congo 2 1 3 South Africa 2 0 2 Botswana 2 0 2 Southern Asia 11 8 19 Afghanistan 1 2 3 Bangladesh 4 4 8 Nepal 1 0 1 India 4 2 6 Pakistan 1 0 1 Western Africa 4 2 6 Nigeria 1 1 2 Sierra Leone 1 1 2 Guinea 1 0 1 Benin 1 0 1 Total 51 20 71

188

05 07 05 07 - - - -

0.673 0.8204 0.4556 0.2075 1.50E 2.25E 2.59E 4.08E 1663.6102 1841.7691 HA+ATI+crmb HA+ATI+crmb

05 07 05 07 - - - - 0.8584 0.7368 0.3218 0.1035 1.67E 2.07E 1649.352 1.96E 5.32E 1816.926 ATI+crmB ATI+crmB

05 07

05 07 - - - - 0.749 0.561 0.186 0.4313 1.75E 4.94E 1671.623 2.29E 3.67E 1840.494 HA+crmB HA+crmB

05 07 05 07 - - - - 0.6697 0.4485 0.5222 0.2727 HA+ATI 1.17E 3.46E HA+ATI 2.51E 2.69E 1664.769 1863.555

05 06 05 02 06 - - - - - crmB crmB 0.6835 0.4672 0.2152 2.28E 1.22E 3.07E 4.63E 3.09E 1849.0064 1662.0872

05 07 05 07 - - - - ATI ATI 0.3772 0.1422 0.7979 0.6367 1.41E 1.92E 1.16E 1.58E 1647.296 1847.7916

05 06 - - 05 07 HA - - HA 0.4055 0.1644 0.5062 0.2562 1.19E 1.48E 4.26E 8.42E 1691.4635 1871.4638

05 07 - - 05 07 tip by genetic year distance sampling VARV of (51 Taxa) WG - - - tip by genetic year distance sampling VARV of (50 Taxon) afterBK010317 removing WG - to 0.8451 0.7143 - to 0.4907 0.2408 - 1.18E 1.15E 1.42E 1.01E 1660.5226 1833.1281

Squared Summary of root of Summary

Summary of root of Summary

Intercept Intercept - - Table S3.4: Table Statistic (rate)Slope X Correlation Coefficient R squared Mean Residual Table S Table Statistic (rate)Slope X Correlation Coefficient R squared Squared Mean Residual

189

0.2 DQ437581/BGD75 0.1 DQ441418/BWA73 0.1 DQ441422/BGD74 0 AF375131/COG70 0.8 0.2 A 0.2 DQ441421/BGD74 B 0.2 DQ441423/COG70 DQ441420/BGD74 0.4 DQ437583/COG70 0.1 AF375136/BGD74 DQ441417/BWA72 0.20.1 AF375140/BGD74 0.40.2 DQ441435/ZAF65 1 AF375125/BGD74 DQ441436/ZAF65 0 0.9 Region 0.6 AF375134/BGD74 DQ441442/SUM70 DQ437588/NPL73 DQ437591/SUM70 0.7 DQ441446/UNK47 DQ441441/SDN47 Asia Pacifc DQ437584/GER58 0.3 DQ441439/SOM77 0.3 1 0 0.2 0.7 DQ441417/BWA72 1 DQ437590/SOM77 0.6 DQ441418/BWA73 AF375143/SOM77 Southern Asia DQ441436/ZAF65 0.3 DQ441438/SOM77 0.1 0.20.5 0.2 0.5 DQ441435/ZAF65 DQ441425/ETH72 0.7 0.5 Southern Africa 0.4 DQ437583/COG70 DQ441424/ETH72 0.3 DQ441423/COG70 DQ441443/TZA65 WesternAfrica AF375131/COG70 AF375135/TZA65 0.4 DQ441443/TZA65 DQ441440/SDN47 0.1 DQ441427/IND53 0.6 DQ441433/KWT67 Europe 0.7 AF375127/AFG71 DQ437587/IRN72 0.2 AF375126/AFG72 0 DQ437592/SYR72 0.4 1 0.2 0 0.2 DQ441442/SUM70 0.1 DQ437585/IND64 Latin American DQ437591/SUM70 DQ441448/YUG72 AF375128/IND58 0.10.5 DQ437580/AFG70 East Asia 0.4 AF375144/UNK46 1 DQ437589/PAK69 DQ441445/UNK46 AF375137/IND62 1 0 0.6 DQ437592/SYR72 DQ437586/IND64 East Africa / Middle East 0.9 DQ437587/IRN72 0.1 AF375128/IND58 1 0.2 0.20.1 1 DQ441448/YUG72 X69198/IND67 1 DQ437589/PAK69 DQ441427/IND53 DQ437580/AFG70 0.8 AF375126/AFG72 0.6 DQ441433/KWT67 AF375127/AFG71 11 R 0.5 R DQ437585/IND64 0.5 DQ441430/JPN51 0.6 DQ437586/IND64 1 DQ441431/JPN51 AF375137/IND62 DQ441428/IND53

High CF 0.5 0.2 High CF 1 DQ441428/IND53 Major DQ441429/JPN46 Major 1 DQ441429/JPN46 0.6 DQ437582/CHN48 1 0.8 DQ441431/JPN51 DQ441432/KOR47 DQ441430/JPN51 0.2 DQ437584/GER58 0.80.9 0.1 DQ441432/KOR47 0.2 DQ441446/UNK47 0.7 0.7 DQ437582/CHN48 0.2 AF375144/UNK46 LT706528/CZE25 0.2 DQ441445/UNK46 DQ441444/UNK44 DQ441444/UNK44 0.3 DQ437590/SOM77 LT706528/CZE25 1 AF375143/SOM77 P-I 1 0.1 DQ441422/BGD74 0.8 0 DQ441438/SOM77 0 DQ437588/NPL73 0.7 0.8 DQ441439/SOM77 AF375125/BGD74 1 DQ441424/ETH72 0 DQ441420/BGD74 0.8 0.4 DQ441425/ETH72 0.10 AF375134/BGD74 AF375135/TZA65 DQ441421/BGD74 P-I 1 DQ441441/SDN47 10.1 AF375140/BGD74 1 DQ441440/SDN47 0.9 AF375136/BGD74 X69198/IND67 DQ437581/BGD75 0.2 AF377892/KOR46 1 AF377888/JPN46 0.4 1 0.2 1 AF377889/CHN45 0.2 AF377889/CHN45 0.8 AF377887/JPN44 1 AF377892/KOR46 AF377890/BEL47 AF377890/BEL47 1 AF377888/JPN46 AF377887/JPN44 0.5 0.3 1 AF375142/SLE68 e 1 AF375142/SLE68 e 0.8 DQ441437/SLE68 DQ441426/GIN69

R 0.2 R 0.8 DQ441426/GIN69 DQ441437/SLE68

CF 0.3 CF 1 DQ441416/BEN68 1 1 DQ441434/NER69 Minor Minor

AF375138/NIG61 Intermediat DQ441416/BEN68 Intermediat 1 DQ441434/NER69 AF375138/NIG61 1 0.2 AF375130/BRZ68 1 0.6 AF375130/BRZ68

1 m 1 m 1 Y16780/BRZ66 R 1 DQ441419/BRZ66 R 1 DQ441419/BRZ66 P-II 0.5 Y16780/BRZ66 P-II DQ441447/UNK52 DQ441447/UNK52 LT706529/CZE29 Low CF LT706529/CZE29 Low CF BK010317/LTH54 Alastri BK010317/LTH54 Alastri

1700 1800 1900 2000 1700 1800 1900 2000 Year (ACE) Year (ACE)

Figure S3.1: Time-rooted phylogenetic characterisation of 71 VARV isolates. Values on ancestral nodes represent posterior probabilities. Tip names are coloured by sampling region and edges coloured by inferred origin using a BSSVS framework. A: Fifty-one whole genome VARV isolates aligned with 20 additional ha sequences. B: Fifty-one ha sequences extracted from whole

190

dataset

HA+ATI+CrmB 61.65 24.17 19.21 34.06 320.42 7.59 6.59 1.53 12.37 1.57

ATI+CrmB 5.35 5.36 12.95 21.19 340.42 5.72 25.19 1.43 16.61 1.58 ns from 1654 to 1977 by from ns

HA+CrmB 7.68 92.01 24.54 25.96 302.57 7.41 11.21 1.68 21.17 1.64

Dataset

HA+ATI 39.63 34.92 7.88 33.36 5.99 7.33 0.96 1.51 16.09 1.52

CrmB 5.19 11.11 22.69 8.72 397.44 6.52 24.15 1.51 18.66 1.53

ATI 22.21 3.55 7.28 32.83 3.50 6.09 2.52 1.46 13.09 1.53

HA 50.18 90.83 9.12 39.04 3.48 8.59 1.27 1.81 18.24 1.68 for VARV transmission between eightregio VARVfor transmission discrete

WG 4703.65 542.45 330.14 107.21 82.31 11.84 8.37 4.11 4.07 3.48 support

Supported routes in bold (Bayes Factor >3) (Bayes in bold Supported routes

Asia

To East Africa/ Middle East Southern Asia Europe Latin America Southern Africa Western Africa Asia Pacific East East Asia East Asia

Absolute Bayes Factor Bayes Absolute

From : 6 Europe Europe Europe 3. East Asia Transmission Route East Africa/ Middle East Southern Asia Southern Asia Southern Asia Latin America Western Africa Table S Table phylogeographyusing Bayesian

191

Table S3.7: Representativeness by absolute support of VARV transmission between eight discrete regions from 1654 to 1977 by dataset using Bayesian phylogeography. Supported routes in bold (Bayes Factor >3) Dataset From To WG WG+20HA HA+20HA Southern Asia East Africa/ 4703.65 2207.14 55.90 Middle East East Asia Southern Asia 542.45 118.42 507.53

Southern Asia Europe 330.14 38.44 10.06

Europe Latin America 107.21 60.79 44.64 East Africa/ Southern Africa 82.31 5.29 4.18 Middle East Europe Western Africa 11.84 9.45 5.00

Southern Asia Asia Pacific 8.37 10.78 2.25

Latin America East Asia 4.11 1.42 2.10

Europe East Asia 4.07 68.75 10.50

Western Africa East Asia 3.48 1.56 2.25

Table S3.8: Interpretation of computed Bayes Factor values Bayes Factor (BF) Interpretation >100 Decisive 30-100 Very strong 10-30 Strong 3-10 Supported

192

0 0 31 58 57 48 85 68 17 65 154 10 1 102 735 Total

------3 3 8 14 14 Dec

------3 8 1 2 14 14 Nov

- - 3 9 2 9 9 8 3 3 40 43 Oct

- 7 5 3 2 89 20 20 12 20 10 94 Sep

1 5 6 4 3 1 83 20 20 11 20 87 Aug

1 9 8 2 94 20 16 20 20 22 20 Jul 115 A sequences by location. and sequences month A

Month

- - 1 3 3 9 64 20 20 20 12 76 Jun

- - 3 2 6 2 4 4 4 37 20 41 May

- -

1 9 9 2 61 20 20 19 19 80 Apr

- 8 1 3 1 61 20 11 18 14 13 75 Mar

- - - - 9 3 1 1 Distribution of subsampled HA and N and HA Distribution subsampled of 39 12 13 39 Feb . 1

S4.

4 - 6 4 1 1 3 3 5 13 14 15 57 Jan able able T

NSW NT QLD SA TAS VIC WA NI SI pplementary Includes Australia Capital Australia Territory (ACT) Includes u S Country/State Australia New Zealand Total a:

193

6 77 72 27 33 44 33 11 464 128 121 508 Total

------8 2 1 5 8 Dec

------1 7 1 2 11 11 Nov

- - - 2 8 5 5 7 3 3 27 30 Oct

- 3 8 3 5 2 1 1 69 16 34 71 Sep

1 3 4 4 2 1 1 common between HA and NA datasets by datasets betweenNA month. and HA common 69 16 15 26 71

Aug

- - 9 6 5 5 73 10 38 14 14 8 Jul h location

per

Mont - - - 1 2 5 5 42 16 11 12 47 Jun

- - 3 2 4 4 2 2 2 2 sequences 17 19

May

unique

- - 1 7 8 2 1 6 6 36 17 42 Apr

- - 9 8 1 3 7 6 1 49 28 56 Mar Distribution of

- - - - - eb 6 3 8 1 1 19 19 F 4.2:

- 2 4 1 1 3 3 able S able 44 12 13 11 47 Jan y T

ntar

NSW NT QLD SA TAS VIC WA NI SI Includes Australia Capital Australia Territory (ACT) Includes uppleme S Country/State Australia New Zealand Total a:

194

77 72 27 68 33 44 33 11 393 110 437 Total

------8 2 1 5 8 Dec

------1 7 1 2 11 11 Nov

- - - 2 8 5 5 7 3 3 27 30 Oct

- 3 8 3 5 2 1 1 49 15 15 51 Sep

1 3 4 4 2 1 1 57 15 15 15 59 Aug common between HA and NA datasets by datasets betweenNA month. and HA common

- - 9 6 5 5 50 10 15 14 14 64 Jul location

Month per - - - 1 2 5 5

41 15 11 12 46 Jun

- - 3 2 4 4 2 2 2 2 17 19 May sequences

- - 1 7 8 2 1 6 6 34 15 40 unique Apr of of

- - 9 8 1 3 7 6 1 36 15 43 Mar

Subsample

- - - - - 6 3 8 1 1 19 19 Feb S4.3:

- 2 4 1 1 3 3 44 12 13 11 47 able able Jan T

entary a

NSW NT QLD SA TAS VIC WA NI SI Includes Australia Capital Australia Territory (ACT) Includes

Supplem Country/State Australia New Zealand Total a:

195

Supplementary Table S4.4. Results of various combinations of substitution and tree model testing using path sampling (PS) and stepping-stone sampling (SSS) methods. The results and average of two independent runs (MLE 1 and MLE 2) are shown. Dataset Tree Prior Nucleotide Clock Statistic MLE1 MLE2 Average PS -12070 -12138 -12104 Strict SSS -12120 -12173 -12147 HKY PS -12914 -12858 -12886 Relaxed SSS -12960 -12912 -12936 Exponential PS -12063 -12115 -12089 Strict SSS -12130 -12179 -12155 GTR PS -12894 -12906 -12900 Relaxed SSS -12950 -12975 -12963 PS -12122 -12063 -12093 Strict SSS -12164 -12100 -12132 HKY PS -12802 -12826 -12814 Relaxed SSS -12861 -12901 -12881 HA Constant PS -12055 -12039 -12047 Strict SSS -12100 12093 -12097 GTR PS -12811 -12794 -12803 Relaxed SSS -12835 -12833 -12834 PS -12075 -12090 -12083 Strict SSS -12142 -12142 -12142 HKY PS -12830 -12817 -12824 Relaxed SSS -12860 -12865 -12863 Skygrid PS -12074 -12071 -12073 Strict SSS -12113 -12127 -12120 GTR PS -12797 -12803 -12800 Relaxed SSS -12849 -12849 -12849 PS -10134 -10133 -10134 Strict SSS -10190 -10168 -10179 HKY PS -10989 -10973 -10981 Relaxed SSS -11030 -11022 -11026 Exponential PS -10106 -10157 -10132 Strict SSS -10168 -10194 -10181 GTR PS -10989 -10943 -10966 Relaxed SSS -11041 -10983 -11012 PS -10142 -10142 -10142 Strict SSS -10184 -10191 -10188 HKY PS -10907 -10908 -10908 Relaxed SSS -10961 -10973 -10967 NA Constant PS -10150 -10163 -10157 Strict SSS -10198 -10208 -10203 GTR PS -10864 -10902 -10883 Relaxed SSS -10913 -10944 -10929 PS -10251 -10169 -10210 Strict SSS -10306 -10225 -10266 HKY PS -10906 -10913 -10910 Relaxed SSS -10955 -10979 -10967 Skygrid PS -10135 -10174 -10155 Strict SSS -10182 -10214 -10198 GTR PS -10879 -10873 -10876 Relaxed SSS -10940 -10923 -10932

196

Supplementary Table S4.5: Interpretation of computed Bayes Factor values Interpretation of Evidence Bayes Factor (BF) Decisive >100 Very strong 30-100 Strong 10-30 Substantial 3-10

Supplementary Figure S4.1: Root-to-tip regression over time of influenza A/H3N2 haemagglutinin (Left – Blue) and neuraminidase (Right – Green) isolates sampled from Australia and New Zealand in 2017. Sample demonstrates sufficient temporal signal (R2 = 0.11 and 0.08) suitable for time- scaled phylogenetic analysis and relaxed molecular clock model assumptions.

197

nferred influenza influenza Dataset3)

C: Tips coloured by State and . Dataset 2), 437 (

B: Dataset 1), 735 (

1,110 ( of

s ancestral state location. Credibility (MCC) tree scaled Maximum Clade Clade scaled Maximum - Time

: 2 S4.

on in Australia or major island of Zealand.New In each tree, internal branches and nodes are coloured by the statistically i Figure Territory of isolati A/H3N2 taxa sequenced for Hemagglutinin (HA) and Neuraminidase (NA) genes in 2017 in Australia and New Zealand Supplementary

198

d. Tips coloured by country of scaled Maximum Clade Credibility (MCC) trees Credibility of (MCC) Clade 1,110 (A: 1),Dataset (B: 735 2),Dataset (C: 437 influenza scaled Maximum Dataset3) - Time

: 3 S4. isolation. In each tree, internal branches and nodes are coloured by the statistically inferred ancestral state location. state ancestral inferred statistically the by coloured are nodes and branches internal tree, each In isolation. A/H3N2 taxa sequenced for Hemagglutinin (HA) and Neuraminidase (NA) genes in 2017 in Australia and New Zealan Supplementary Figure

199

Supplementary Table S4.6: Comparison of time-scaled phylogenetic statistics between three unique sequence datasets Dataset Taxa Root Height (95% HPD) Age Root (95% HPD) Clock Rate (95% HPD) 1 1,110 3.71 (3.18 - 4.30) 2014.28 (2013.70 - 2014.82) 3.74 x 10-3 (3.43 - 4.06) 2 735 2.88 (2.51 - 3.24) 2015.11 (2014.75 - 2015.49) 3.71 x 10-3 (3.37 - 4.06) 3 437 3.68 (3.09 – 4.28) 2014.31 (2013.71 – 2014.89) 3.72 x 10-3 (3.36 - 4.13)

Supplementary Figure S4.4. Phylogeographic projection of 735 (Top) and 437 (Bottom) A/H3N2 samples sequenced for haemagglutinin (HA) and Neuraminidase (NA) in 2017 from Australia and New Zealand. Only routes with definitive support (BF > 100) are shown. Line weight equivalent to log- standardised BF values shown in Supplementary Tables S4.8 and S4.9.

200

Supplementary Table S4.7: Bayes Factors and Posterior inclusion probabilities of supported A/H3N2 transmission within and between Australia and New Zealand during the 2017 season (1,110 taxa; Dataset 1). Origin Destination Bayes Factor (BF) Posterior Probability New South Wales North Island 117981.93 1.00 New South Wales Queensland 117981.93 1.00 New South Wales South Australia 117981.93 1.00 New South Wales Victoria 117981.93 1.00 North Island South Island 117981.93 1.00 South Australia Victoria 117981.93 1.00 Victoria Western Australia 117981.93 1.00 Victoria New South Wales 117981.93 1.00 Victoria Queensland 10719.01 1.00 South Australia New South Wales 8420.52 1.00 New South Wales Tasmania 2557.70 1.00 New South Wales Northern Territory 1678.28 1.00 New South Wales Western Australia 1221.77 0.99 Victoria Tasmania 284.05 0.98 South Australia Tasmania 82.58 0.92 New South Wales South Island 24.95 0.77 Queensland South Australia 23.75 0.77 Northern Territory New South Wales 18.47 0.72 Queensland Victoria 12.27 0.63 Western Australia New South Wales 9.67 0.57 South Australia Queensland 8.23 0.53 Western Australia Victoria 6.77 0.48 Victoria South Australia 6.59 0.48 Northern Territory Queensland 6.25 0.46 Western Australia Queensland 4.53 0.38 South Australia Western Australia 2.80 0.28 Tasmania South Australia 2.13 0.23 Tasmania New South Wales 1.99 0.21 Tasmania Queensland 1.74 0.19 Northern Territory South Island 1.48 0.17 South Island North Island 1.29 0.15 Tasmania Western Australia 1.27 0.15 South Island Queensland 1.21 0.14 South Island New South Wales 1.20 0.14 South Island Victoria 1.18 0.14 South Island South Australia 1.13 0.13 South Island Western Australia 1.12 0.13 Queensland South Island 1.11 0.13 South Island Tasmania 1.08 0.13 South Island Northern Territory 1.07 0.13 Northern Territory Western Australia 1.06 0.13 Tasmania Victoria 1.05 0.13 Tasmania North Island 0.79 0.10 Tasmania South Island 0.69 0.09 Northern Territory South Australia 0.69 0.09

201

Tasmania Northern Territory 0.62 0.08 Queensland New South Wales 0.61 0.08 Queensland Northern Territory 0.56 0.07 North Island Tasmania 0.54 0.07 Queensland Western Australia 0.52 0.07 Western Australia South Australia 0.48 0.06 Western Australia Northern Territory 0.40 0.05 Victoria Northern Territory 0.37 0.05 Victoria North Island 0.34 0.04 Northern Territory Victoria 0.32 0.04 Victoria South Island 0.32 0.04 Western Australia Tasmania 0.32 0.04 North Island Victoria 0.30 0.04 North Island New South Wales 0.26 0.03 South Australia South Island 0.25 0.03 Western Australia South Island 0.25 0.03 Northern Territory North Island 0.24 0.03 Western Australia North Island 0.24 0.03 Queensland Tasmania 0.23 0.03 Queensland North Island 0.22 0.03 Northern Territory Tasmania 0.21 0.03 North Island Western Australia 0.20 0.03 North Island South Australia 0.19 0.03 North Island Queensland 0.17 0.02 North Island Northern Territory 0.17 0.02 South Australia North Island 0.12 0.02 South Australia Northern Territory 0.11 0.02

202

Supplementary Table S4.8: Bayes Factors and Posterior inclusion probabilities of supported A/H3N2 transmission within and between Australia and New Zealand during the 2017 season (735 taxa; Dataset 3). Origin Destination Bayes Factor (BF) Posterior Probability New South Wales Queensland 117981.93 1.00 New South Wales South Australia 117981.93 1.00 New South Wales Victoria 117981.93 1.00 New South Wales North Island 117981.93 1.00 South Australia New South Wales 117981.93 1.00 New South Wales Tasmania 58987.33 1.00 New South Wales Western Australia 4908.93 1.00 Queensland South Australia 2450.83 1.00 Victoria Western Australia 2137.98 1.00 Victoria New South Wales 879.85 0.99 South Australia Victoria 505.71 0.99 New South Wales Northern Territory 284.77 0.98 New South Wales South Island 140.39 0.95 South Australia Tasmania 81.97 0.92 Victoria Queensland 57.58 0.89 North Island South Island 45.7 0.86 Northern Territory Queensland 35.73 0.83 Western Australia New South Wales 25.85 0.78 South Australia Queensland 21.85 0.75 Western Australia Queensland 16.86 0.7 Tasmania Queensland 4.04 0.36 South Island North Island 2.84 0.28 South Australia Western Australia 2.27 0.24 North Island New South Wales 1.79 0.2 Northern Territory Western Australia 1.61 0.18 Northern Territory South Island 1.6 0.18 Tasmania South Australia 1.53 0.17 Tasmania New South Wales 1.49 0.17 Tasmania Western Australia 1.48 0.17 North Island Victoria 1.37 0.16 Queensland Western Australia 1.15 0.14 South Island New South Wales 1.11 0.13 South Island South Australia 1.08 0.13 Northern Territory South Australia 1.06 0.13 South Island Western Australia 1.02 0.12 Victoria North Island 0.95 0.12 North Island Tasmania 0.95 0.12 South Island Queensland 0.93 0.11 South Island Victoria 0.9 0.11 South Island Northern Territory 0.88 0.11 South Island Tasmania 0.86 0.11 Tasmania Victoria 0.85 0.1 Tasmania North Island 0.76 0.09 Western Australia South Australia 0.7 0.09 Queensland New South Wales 0.65 0.08

203

North Island Western Australia 0.61 0.08 Victoria South Australia 0.6 0.08 Northern Territory New South Wales 0.58 0.07 North Island South Australia 0.55 0.07 Queensland South Island 0.54 0.07 Tasmania South Island 0.54 0.07 Western Australia Victoria 0.52 0.07 Tasmania Northern Territory 0.49 0.06 Queensland Victoria 0.47 0.06 Queensland Northern Territory 0.43 0.06 Western Australia Northern Territory 0.36 0.05 North Island Queensland 0.3 0.04 Northern Territory Victoria 0.3 0.04 Queensland Tasmania 0.29 0.04 Western Australia Tasmania 0.28 0.04 Queensland North Island 0.26 0.03 Western Australia North Island 0.26 0.03 Northern Territory Tasmania 0.24 0.03 Northern Territory North Island 0.24 0.03 Western Australia South Island 0.24 0.03 Victoria Tasmania 0.23 0.03 North Island Northern Territory 0.22 0.03 Victoria South Island 0.19 0.03 South Australia North Island 0.19 0.03 South Australia South Island 0.15 0.02 Victoria Northern Territory 0.14 0.02 South Australia Northern Territory 0.09 0.01

204

Supplementary Table S4.9: Bayes Factors and Posterior inclusion probabilities of supported A/H3N2 transmission within and between Australia and New Zealand during the 2017 season (437 taxa; Dataset 2). Origin Destination Bayes Factor (BF) Posterior Probability New South Wales South Australia 104873.64 1.00 New South Wales Tasmania 104873.64 1.00 New South Wales Victoria 104873.64 1.00 New South Wales Western Australia 104873.64 1.00 New South Wales North Island 104873.64 1.00 New South Wales Queensland 26212.95 1.00 South Australia New South Wales 721.06 0.99 New South Wales South Island 234.94 0.97 New South Wales Northern Territory 157.62 0.96 Queensland South Australia 20.38 0.74 Victoria Queensland 19.37 0.73 Queensland Victoria 19.22 0.73 Tasmania South Australia 15.57 0.68 North Island South Island 10.91 0.60 Victoria Western Australia 6.67 0.48 Western Australia New South Wales 5.36 0.42 Victoria New South Wales 3.09 0.30 North Island New South Wales 2.24 0.24 Western Australia Queensland 1.91 0.21 South Australia Queensland 1.70 0.19 Western Australia Victoria 1.68 0.19 Queensland Western Australia 1.49 0.17 South Island North Island 1.35 0.16 South Island New South Wales 1.33 0.15 Queensland South Island 1.24 0.15 South Island Queensland 1.17 0.14 South Island Tasmania 1.13 0.13 South Island Western Australia 1.08 0.13 South Island Victoria 1.02 0.12 North Island Victoria 1.01 0.12 South Island South Australia 1.00 0.12 Queensland New South Wales 0.94 0.11 South Island Northern Territory 0.90 0.11 Tasmania Western Australia 0.85 0.10 Western Australia South Australia 0.81 0.10 Tasmania Victoria 0.79 0.10 Victoria Tasmania 0.77 0.10 Western Australia Tasmania 0.77 0.10 Western Australia South Island 0.74 0.09 Queensland Northern Territory 0.70 0.09 Tasmania New South Wales 0.66 0.08 Tasmania North Island 0.65 0.08 Northern Territory Western Australia 0.63 0.08 Western Australia Northern Territory 0.63 0.08 North Island Tasmania 0.62 0.08

205

Northern Territory South Island 0.60 0.08 Northern Territory South Australia 0.58 0.07 Western Australia North Island 0.55 0.07 South Australia Tasmania 0.54 0.07 Northern Territory New South Wales 0.52 0.07 Tasmania Queensland 0.52 0.07 Northern Territory Queensland 0.50 0.06 Victoria South Australia 0.50 0.06 Northern Territory Tasmania 0.47 0.06 Northern Territory North Island 0.45 0.06 Northern Territory Victoria 0.45 0.06 North Island South Australia 0.41 0.05 South Australia Western Australia 0.40 0.05 Tasmania South Island 0.39 0.05 Tasmania Northern Territory 0.34 0.04 South Australia Victoria 0.31 0.04 North Island Queensland 0.27 0.04 Queensland North Island 0.27 0.04 Victoria South Island 0.26 0.03 Victoria North Island 0.25 0.03 South Australia North Island 0.22 0.03 North Island Western Australia 0.22 0.03 North Island Northern Territory 0.21 0.03 Victoria Northern Territory 0.20 0.03 Queensland Tasmania 0.19 0.03 South Australia Northern Territory 0.09 0.01 South Australia South Island 0.08 0.01

206

Supplementary Table S4.10: Raw effect and 95% Bayesian credible interval of A/H3N2 transmission within and between Australia and New Zealand in 2017 based on Dataset 1 (N=1110). Factors are ordered by decreasing significance (BF) and probability of inclusion (E(δ)) in the model as a measure of the likelihood of impact on transmission. Factor (Origin/Destination) E(δ) Probability (δ) Coefficient 95% BCI BF Population Density (D) 1.00 0.65 0.44 to 0.89 ∞ Temperature (D) 1.00 0.68 0.43 to 0.98 ∞ Distance 1.00 -0.78 -0.94 to -0.63 ∞ Sample Size (O) 0.99 0.74 0.4 to 1.13 3985.21 Temperature (O) 0.97 1.58 0.9 to 2.21 840.38 Population Density (O) 0.97 1.17 0.59 to 1.71 761.27 Precipitation (O) 0.02 0.00 -3.88 to 3.88 0.60 Population Growth (O) <0.01 0.00 -3.89 to 3.92 0.20 Passenger Flux (O) <0.01 0.00 -3.89 to 3.95 0.19 Gross Product (O) <0.01 -0.01 -3.89 to 3.89 0.13 Passenger Flux (D) <0.01 0.00 -3.86 to 3.9 0.13 Sample Size (D) <0.01 0.00 -3.93 to 3.89 0.11 Population Growth (D) <0.01 0.00 -3.92 to 3.9 0.06 Precipitation (D) <0.01 0.00 -3.91 to 3.91 0.06 Gross Product (D) <0.01 0.00 -3.91 to 3.92 0.05

207

Supplementary Table S4.11: Raw effect and 95% Bayesian credible interval of A/H3N2 transmission within and between Australia and New Zealand in 2017 based on Dataset 2 (N=735). Factors are ordered by decreasing significance (BF) and probability of inclusion (E(δ)) in the model as a measure of the likelihood of impact on transmission. Factor E(δ) Probability (δ) Coefficient 95% BCI BF (Origin/Destination) Distance 0.99 -0.60 -0.79 to -0.35 342579.56 Sample Size (O) 0.99 0.93 0.52 to 1.47 7765.23 Temperature (D) 0.73 0.38 -2.67 to 2.56 59.63 Population Density (D) 0.64 0.28 -2.86 to 2.93 39.07 Sample Size (D) 0.23 0.10 -3.61 to 3.67 6.31 Precipitation (O) 0.16 0.04 -3.77 to 3.76 4.14 Population Density (O) 0.06 0.06 -3.85 to 3.86 1.58 Population Growth (O) 0.02 0.02 -3.87 to 3.97 0.61 Gross Product (O) 0.02 0.01 -3.86 to 3.88 0.60 Gross Product (D) 0.02 0.01 -3.9 to 3.92 0.45 Temperature (O) 0.01 0.00 -3.81 to 3.89 0.42 Precipitation (D) 0.01 0.00 -3.91 to 3.93 0.39 Passenger Flux (O) 0.01 0.02 -3.85 to 3.93 0.29 Passenger Flux (D) 0.01 0.00 -3.85 to 3.87 0.22 Population Growth (D) 0.00 0.02 -3.82 to 3.92 0.09

208

Supplementary Table S4.12: Raw effect and 95% Bayesian credible interval of A/H3N2 transmission within and between Australia and New Zealand in 2017 based on Dataset 3 (N=437). Factors are ordered by decreasing significance (BF) and probability of inclusion (E(δ)) in the model as a measure of the likelihood of impact on transmission. Factor (Origin/Destination) E(δ) Probability (δ) Coefficient 95% BCI BF Sample Size (O) 0.99 1.96 1.13 to 3.19 21391.40 Distance 0.99 -0.64 -0.95 to -0.33 20131.83 Temperature (D) 0.63 0.38 -2.99 to 3.06 36.15 Population Density (D) 0.63 0.35 -2.97 to 3.08 36.05 Sample Size (D) 0.36 0.20 -3.59 to 3.42 12.35 Gross Product (D) 0.07 0.09 -3.88 to 3.89 1.59 Population Growth (D) 0.06 -0.06 -3.79 to 3.81 1.56 Passenger Flux (D) 0.04 -0.01 -3.87 to 3.92 0.96 Precipitation (D) <0.01 0.00 -3.86 to 3.92 0.21 Temperature (O) <0.01 0.00 -3.85 to 3.83 0.16 Gross Product (O) <0.01 0.01 -3.94 to 3.98 0.14 Precipitation (O) <0.01 -0.01 -3.89 to 3.94 0.12 Population Growth (O) <0.01 0.00 -3.92 to 3.89 0.12 Passenger Flux (O) <0.01 0.01 -4.04 to 3.89 0.11 Population Density (O) <0.01 -0.03 -3.94 to 3.85 0.10

209

Supplementary Table S4.13: Conditional effect (β| δ) of all potential predictors of A/H3N2 transmission within and between nine Australia and New Zealand States, Territories, and Island in 2017 of 1,110 taxa (Dataset 1). Predictors are ordered by decreasing Bayes Factor (BF) and of posterior probability (E(δ)) of inclusion in the model as a measure of the likelihood of impact on transmission. Predictors Rank (β| δ) E(δ) BFs Distance 1 -1.03 1.00 60972.88 Sample Size (origin) 2 1.31 1.00 22850.71 Mean Temperature (destination) 3 1.95 0.87 148.35 Population Density (destination) 4 0.57 0.81 94.49 Mean Precipitation (origin) 5 0.21 0.26 7.87 Sample Size (destination) 6 0.07 0.12 2.97 Population Density (origin) 7 0.06 0.07 1.58 Mean Precipitation (destination) 8 0.04 0.04 1.02 Gross State Product (origin) 9 0.07 0.04 0.97 Airline Passenger Flux (destination) 10 -0.03 0.04 0.88 Percentage Growth (origin) 11 0.02 0.03 0.75 Gross State Product (destination) 12 0.02 0.03 0.67 Mean Temperature (origin) 13 0.04 0.03 0.62 Airline Passenger Flux (origin) 14 0.00 0.01 0.30 Percentage Growth (destination) 15 0.00 0.01 0.13

210

Supplementary Table S4.14: Conditional effect (β| δ) of all potential predictors of A/H3N2 transmission within and between nine Australia and New Zealand States, Territories, and Island in 2017 using a down sampled dataset of 735 taxa (Dataset 2). Predictors are ordered by decreasing Bayes Factor (BF) and of posterior probability (E(δ)) of inclusion in the model as a measure of the likelihood of impact on transmission. Predictors Rank (β| δ) E(δ) BFs Distance 1 -1.03 1.00 60972.88 Sample Size (origin) 2 1.31 1.00 22850.71 Mean Temperature (destination) 3 1.95 0.87 148.35 Population Density (destination) 4 0.57 0.81 94.49 Mean Precipitation (origin) 5 0.21 0.26 7.87 Sample Size (destination) 6 0.07 0.12 2.97 Population Density (origin) 7 0.06 0.07 1.58 Mean Precipitation (destination) 8 0.04 0.04 1.02 Gross State Product (origin) 9 0.07 0.04 0.97 Airline Passenger Flux (destination) 10 -0.03 0.04 0.88 Percentage Growth (origin) 11 0.02 0.03 0.75 Gross State Product (destination) 12 0.02 0.03 0.67 Mean Temperature (origin) 13 0.04 0.03 0.62 Airline Passenger Flux (origin) 14 0.00 0.01 0.30 Percentage Growth (destination) 15 0.00 0.01 0.13

211

Supplementary Table S4.15: Conditional effect (β| δ) of all potential predictors of A/H3N2 transmission within and between nine Australia and New Zealand States, Territories, and Island in 2017 using a down sampled dataset of 437 taxa (Dataset 3). Predictors are ordered by decreasing Bayes Factor (BF) and of posterior probability (E(δ)) of inclusion in the model as a measure of the likelihood of impact on transmission. Predictors Rank (β| δ) E(δ) BFs Distance 1 -1.03 1.00 60972.88 Sample Size (origin) 2 1.31 1.00 22850.71 Mean Temperature (destination) 3 1.95 0.87 148.35 Population Density (destination) 4 0.57 0.81 94.49 Mean Precipitation (origin) 5 0.21 0.26 7.87 Sample Size (destination) 6 0.07 0.12 2.97 Population Density (origin) 7 0.06 0.07 1.58 Mean Precipitation (destination) 8 0.04 0.04 1.02 Gross State Product (origin) 9 0.07 0.04 0.97 Airline Passenger Flux (destination) 10 -0.03 0.04 0.88 Percentage Growth (origin) 11 0.02 0.03 0.75 Gross State Product (destination) 12 0.02 0.03 0.67 Mean Temperature (origin) 13 0.04 0.03 0.62 Airline Passenger Flux (origin) 14 0.00 0.01 0.30 Percentage Growth (destination) 15 0.00 0.01 0.13

212

Supplementary Figure S5.1: Spatial distribution of 613 taxa included in the final dataset sampled in India between 2009 and 2017 by S/UT.

213

5.96 7.99 7.04 5.00 4.60 3.92 6.43 8.13 % 24.27 26.65 100.00

976 831 1,265 1,695 5,147 1,494 1,061 5,652 1,363 1,725 Total 21,209

17 47

348 208 438 178 305 657 150 374 2017 2,722

287 162 268 235 402 152 553 366 2016 1,291 1,457 5,173

87 38 216 148 617 255 320 162 110 383 2015 2,336

40 60 53 66 79 97 413 193 248 149 2014 1,398

79 78 52 95 354 104 351 119 143 131

2013 1,506

Year 5 47 18 15 37 24 47 128 245 120 686 2012

60 23 23 65 12 57 22 100 250 204 816 2011

89 76 28 14 14 59 42 Global dataset of HA sequences by year and region for comparative analysis.

259 200 275 2010 1,056

99 73 662 550 150 173 195 263 2009 1,456 1,895 5,516

Year Total S/UT Africa China Europe Japan/Korea Middle East / Western Asia North America Northern Asia Oceania South America Southern Asia / South East Asia Supplementary Table S5.1:

214

SSS

18,930 18,897 18,914 - - -

Strict

PS 18,897 18,865 18,881 - - -

GTR

SSS 18,836 18,840 18,838 - - -

Relaxed PS 18,796 18,803 18,800 - - -

SSS

19,006 18,967 18,986 - - -

Strict

PS 18,977 18,935 18,956

- - -

HKY

SSS 18,893 18,894 18,894 - - -

Substitution model and clock prior testing results using path sampling (PS) and

Relaxed 2: SSS) of log marginal likelihoods. 5. PS 18,850 18,857 18,854 - - -

Table S

stone sampling ( stone sampling - Run 1 Run 2

Statistic Average Sites Model Clock Model Supplementary stepping

215

Supplementary Figure S5.2: Root-to-tip regression over time of 613 A/H1N1pdm09 isolates sampled from India between 2009 and 2017. Demonstrating temporal signal suitable for time-scaled analysis and suggestive of relaxed molecular clock model assumptions.

Supplementary Table S5.3: Interpretation of computed Bayes Factor values Bayes Factor (BF) Interpretation of Evidence >100 Decisive 30-100 Very strong 10-30 Strong 3-10 Substantial Adapted from Liang F (2013) Table 1 and Jeffreys H (1961)

216

% 5.4 3.9 4.6 8.9 9.7 6.9 2.7 6.6 5.8 2.3 5.0 12.0 10.8 15.4 100.0

7 6 14 31 10 12 23 25 18 28 40 17 15 13 259 Total

5 2 1 8 or replicate analysis 2017

5 5 10 2016

1 5 2 5 3 5 5 5 5 1 5 42 2015

2 3 5 2 12 2014

5 3 5 2 5 5 5 2 32

2013

Year 5 3 3 5 5 5 5 5 3 2 1 5 47 2012 sequences by year and S/UT included f included S/UT by year and sequences

5 3 1 5 5 4 5 4 32 2011

4 4 3 2 2 5 2 5 5 2 1 1 36 2010

4 5 1 1 1 5 1 4 5 1 3 5 1 3 40 Subsample of HA 2009

5.4:

) 6

)

Table S

10.1 (

arakhand arakhand jasthan (68.6) jasthan

S/UT (Population 10 Assam (31.2) Delhi (16.8) Goa (1.5) Haryana (25.4) (12.5) Kashmir & Jammu Karnataka (61.1) Kerala (33.4) Madhya Pradesh (72.6) Maharashtra (112.4) Punjab (27.7) Ra Tamil Nadu (72.15) Utt West Bengal (91.3) Year Total Supplementary

217

Supplementary Table S5.5: dN/dS rate ratios and 95% BCI of all positively selected HA sites among Indian / International taxa of A/H1N1pdm09 detected using renaissance counting (REN). India Taxa International Taxa International Taxa Site (N = 613) (S1) (S2) (H3#) dN/dS 95% BCI P dN/dS 95% BCI P dN/dS 95% BCI P 3 (-) 0.60 (0.25 - 0.98) 0.99 (0.59 - 1.37) 2.13 (1.46 - 2.92) + 4 (-) 2.18 (0.78 - 3.46) 2.92 (2.01 - 3.94) + 3.37 (2.14 - 4.58) + 6 (-) 2.92 (0.70 - 4.56) 2.67 (1.85 - 3.68) + 1.82 (1.17 - 2.55) + 12 (-) 0.83 (0.23 - 1.52) 1.52 (1.05 - 2.09) + 1.31 (0.89 - 1.79) 13 (-) 0.36 (0.15 - 0.63) 3.27 (2.31 - 4.57) + 3.02 (2.07 - 4.18) + 15 (-) 3.91 (1.11 - 5.94) + 0.78 (0.52 - 1.06) 1.94 (1.29 - 2.72) + 16 (-) 0.39 (0.20 - 0.61) 3.23 (2.19 - 4.51) + 1.09 (0.69 - 1.52) 35 (45) 2.14 (1.46 - 2.93) + 3.26 (2.10 - 4.50) + 6.97 (4.51 - 9.79) + 38 (48) 2.62 (1.79 - 3.65) + 0.27 (0.18 - 0.37) 0.24 (0.15 - 0.32) 45 (-) 1.11 (0.76 - 1.53) 4.37 (2.99 - 6.17) + 2.84 (1.84 - 3.90) + 48 (57) 1.42 (0.98 - 1.96) 2.49 (1.59 - 3.38) + 1.95 (1.28 - 2.66) + 69 (78) 0.22 (0.15 - 0.30) 1.19 (0.81 - 1.62) 1.67 (1.15 - 2.42) + 73 (82) 0.02 (0.01 - 0.03) 1.72 (1.16 - 2.35) + 1.63 (1.12 - 2.23) + 74 (-) 0.13 (0.09 - 0.18) 2.59 (1.66 - 3.63) + 1.56 (1.06 - 2.13) + 84 (92) 8.14 (6.58 – 10.92) + 3.51 (2.33 - 4.79) + 2.53 (1.70 - 3.45) + 86 (93) 8.18 (5.44-11.29) + 0.53 (0.35 - 0.72) 0.42 (0.28 - 0.56) 97 (104) 3.14 (2.06 - 4.33) + 1.25 (0.82 - 1.73) 1.24 (0.78 - 1.70) 120 (-) 0.22 (0.15 - 0.30) 1.70 (1.10 - 2.38) + 3.39 (2.28 - 4.64) + 125 (129) 2.21 (1.38 - 3.24) + 1.20 (0.81 - 1.65) 0.80 (0.53 - 1.08) 129 (133) 2.02 (1.35 - 2.85) + 2.85 (1.83 - 3.95) + 2.41 (1.48 - 3.29) + 137 (140) 0.20 (0.14 - 0.28) 1.16 (0.79 - 1.57) 1.74 (1.13 - 2.33) + 139 (142) 0.51 (0.35 - 0.71) 1.95 (1.27 - 2.66) + 4.34 (2.93 - 6.10) + 141 (144) 0.16 (0.11 - 0.22) 1.69 (1.11 - 2.31) + 2.32 (1.54 - 3.31) + 143 (146) 2.02 (1.34 - 2.74) + 1.32 (0.88 - 1.83) 1.12 (0.71 - 1.51) 156 (159) 0.05 (0.03 - 0.08) 1.67 (1.11 - 2.31) + 1.04 (0.73 - 1.42) 162 (165) 2.66 (1.81 - 3.68) + 0.84 (0.56 - 1.12) 1.34 (0.91 - 1.84) 163 (166) 3.83 (2.68 – 5.35) + 2.62 (1.79 - 3.68) + 4.54 (2.98 - 6.38) + 185 (188) 4.50 (3.18 – 6.21) + 1.13 (0.76 - 1.54) 2.14 (1.41 - 2.94) + 186 (189) 3.35 (2.23 – 4.51) + 1.47 (0.93 - 1.97) 1.44 (0.97 - 1.94) 191 (194) 0.60 (0.40 - 0.82) 1.63 (1.05 - 2.24) + 0.89 (0.60 - 1.21) 203 (206) 1.63 (1.12 - 2.30) + 0.28 (0.18 - 0.39) 0.36 (0.23 - 0.51) 205 (208) 2.02 (1.36 - 2.75) + 2.37 (1.59 - 3.25) + 2.06 (1.32 - 2.85) + 215 (218) 1.42 (0.97 - 1.94) 3.84 (2.51 - 5.20) + 2.09 (1.33 - 2.82) + 216 (219) 2.03 (1.37 - 2.80) + 2.28 (1.57 - 3.15) + 3.64 (2.44 - 5.07) + 222 (225) 13.42 (9.43 - 18.42) + 4.32 (2.87 - 5.85) + 3.42 (2.30 - 4.81) + 223 (226) 0.41 (0.29 - 0.57) 4.07 (2.58 - 5.53) + 5.14 (3.36 - 7.24) + 227 (230) 0.21 (0.14 - 0.28) 1.65 (1.12 - 2.30) + 1.45 (0.98 - 1.98) 249 (252) 3.23 (2.14 - 4.38) + 0.22 (0.14 - 0.30) 0.24 (0.17 - 0.34) 256 (259) 4.39 (3.12 – 6.09) + 1.08 (0.75 - 1.48) 1.16 (0.74 - 1.58) 257 (260) 0.81 (0.56 - 1.10) 3.73 (2.55 - 5.07) + 5.06 (3.61 - 7.12) + 260 (-) 0.54 (0.34 - 0.74) 2.00 (1.29 - 2.72) + 1.78 (1.15 - 2.42) + 261 (263) 2.04 (1.33 - 2.79) + 1.07 (0.70 - 1.45) 1.22 (0.84 - 1.70) 286 (288) 2.66 (1.74 - 3.60) + 2.43 (1.70 - 3.32) + 5.51 (3.69 - 7.31) + 295 (297) 3.02 (1.93 - 4.31) + 1.95 (1.29 - 2.66) + 2.95 (2.04 - 4.02) + 365 (367) 2.64 (1.80 - 3.57) + 0.13 (0.08 - 0.17) 0.25 (0.17 - 0.34) 370 (372) 0.07 (0.05 - 0.10) 1.87 (1.24 - 2.56) + 2.05 (1.37 - 2.95) + 372 (374) 2.06 (1.32 - 2.90) + 0.41 (0.26 - 0.58) 0.66 (0.44 - 0.91) 408 (410) 2.03 (1.35 - 2.74) + 0.09 (0.06 - 0.12) 0.06 (0.04 - 0.09) 451 (453) 4.28 (2.76 - 6.03) + 1.22 (0.84 - 1.66) 0.84 (0.56 - 1.13) 460 (462) 0.82 (0.56 - 1.17) 3.14 (2.13 - 4.44) + 2.96 (1.86 - 4.13) + 499 (501) 3.90 (2.53 - 5.32) + 1.93 (1.32 - 2.62) + 1.91 (1.29 - 2.61) + 520 (521) 1.89 (1.28 - 2.61) + 2.21 (1.53 - 3.09) + 1.67 (1.13 - 2.28) + 527 (528) 2.03 (1.41 - 2.84) + 0.81 (0.56 - 1.15) 1.32 (0.85 - 1.83) 533 (534) 2.03 (1.36 - 2.80) + 1.89 (1.30 - 2.66) + 2.24 (1.46 - 3.07) + 547 (548) 0.35 (0.22 - 0.50) 2.61 (1.79 - 3.59) + 2.15 (1.40 - 2.89) +

218

)’ - 0.03 0.05 0.06 0.09 0.04 value < 0.01 - P rate fixed fixed rate -

SLAC S ∞ ∞ ∞ ∞ ∞ /d 5.40 N d oth samples. The dash ‘(

0.02 0.05 0.03 0.05 0.02 value < 0.01 - P

FEL

S ∞ ∞ ∞ ∞ ∞ /d 5.05 N d

) ) ) ) 3 6 2 10 5.3 6.21) 4.5 6.

10.9 18.42) - - - -

- -

9 9 7 95% BCI 95% 5 (2.6 (3.1 (2.2 (3.12 (5.68 (5.68 (9.4

S /d 8.14 3.83 4.50 3.35 4.40 N 13.43 d Renaissance Counting likelihood ancestor counting (SLAC) in India. India. in (SLAC) counting ancestor likelihood - Codon sites under pervasive positive selection as identified using Bayesian Renaissance Counting (BRC), two Renaissance(BRC),identifiedBayesianCountingpervasivepositiveselectionusing asunder sites Codon

5. Table S Table

likelihood (FEL) and single and (FEL) likelihood ects Relative H3 numbering determined by FluDB HA Subtype Numbering algorithm; Results presented as the mean dN/dS and range of b 185 (188)

H1 Site (H3#) S84 (92) K163 (166) S A186 (189) D222 (225) A256 (259) a (BCI). Interval Credible Bayesian genome. reference H3 the in position equivalent no indicates Supplementary eff

219

sequenced for HA between HA for sequenced

taxa Tips are coloured by sampled location state location and by sampled are coloured Tips A/H1N1pdm09 A/H1N1pdm09

613 of of

in India. in India.

oured by origin. oured statisticallyancestral inferred s and union territory’s union (S/UT) and s state

4 Maximum clade credibility tree clade (MCC) Maximum : 3 5. internal edges and nodes col nodes internal and edges S Figure Figure Supplementary Supplementary 2009 and 2017 inclusive from 1 from 2017 inclusive 2009 and

220

Supplementary Figure S5.4: Definitively supported (BF > 100) routes of A/H1N1pdm09 transmission between S/UT in India from 2009 to 2017 based on five randomly subsampled datasets. Direction of spread is indicated by arrowheads. Paths between S/UT are weighted according to their corresponding significance in Supplementary Table S7 on a log scale. Less supported routes (100 > BF > 3) are not shown but can be seen in Supplementary Table S7.

221

Supplementary Table S5.7: Statistically supported routes for A/H1N1pdm09 transmission in India between 14 S/UT from 2009 to 2017 based on the average of five randomly subsampled sequence datasets (N=259) Origin Destination Bayes Factor (BF) Posterior Probability Maharashtra Karnataka 88654.23 1.00 Maharashtra Madhya Pradesh 88653.09 1.00 Maharashtra Rajasthan 88504.03 0.95 Maharashtra Delhi 67205.70 0.97 Maharashtra Jammu Kashmir 45396.62 0.98 Goa Uttarakhand 3732.35 0.99 Delhi Goa 1390.98 0.98 Haryana Uttarakhand 564.42 0.94 Jammu Kashmir Kerala 394.76 0.77 Delhi Haryana 362.22 0.88 Maharashtra Tamil Nadu 301.75 0.89 Jammu Kashmir Assam 205.30 0.66 Maharashtra West Bengal 181.67 0.76 Uttarakhand Jammu Kashmir 177.89 0.85 Maharashtra Kerala 73.90 0.72 Delhi Punjab 62.11 0.48 Karnataka Kerala 46.67 0.74 Maharashtra Assam 38.60 0.52 Jammu Kashmir Punjab 20.31 0.48 Karnataka Tamil Nadu 16.23 0.53 Jammu Kashmir Delhi 9.79 0.23 Tamil Nadu Maharashtra 8.18 0.34 Karnataka West Bengal 5.67 0.22 Madhya Pradesh Kerala 5.35 0.23 Jammu Kashmir Rajasthan 4.95 0.24 West Bengal Karnataka 4.80 0.23 Rajasthan Madhya Pradesh 4.19 0.25 Punjab Haryana 3.81 0.22

222

Supplementary Table S5.8: Statistically supported routes for A/H1N1pdm09 transmission in India between 14 S/UT discrete locations from 2009 to 2017. Origin Destination Bayes Factor (BF) Posterior Probability Maharashtra Rajasthan 221243.76 1.00 Maharashtra Tamil Nadu 221243.76 1.00 Maharashtra Delhi 221243.76 1.00 Maharashtra Jammu & Kashmir 221243.76 1.00 Maharashtra Karnataka 221243.76 1.00 Maharashtra Madhya Pradesh 221243.76 1.00 Maharashtra West Bengal 27644.71 1.00 Delhi Goa 656.16 0.98 Delhi Haryana 340.59 0.97 Jammu & Kashmir Punjab 258.86 0.95 Haryana Uttarakhand 252.69 0.95 Maharashtra Assam 239.42 0.95 Goa Uttarakhand 227.68 0.95 Maharashtra Kerala 199.23 0.94 Karnataka Kerala 132.99 0.92 Uttarakhand Jammu & Kashmir 126.78 0.91 Madhya Pradesh Kerala 114.94 0.90 Karnataka Tamil Nadu 28.35 0.70 Karnataka Madhya Pradesh 19.70 0.62 West Bengal Karnataka 8.30 0.40 Jammu & Kashmir Kerala 6.24 0.34 Karnataka Haryana 5.59 0.31 Karnataka Goa 5.21 0.30 Karnataka Punjab 4.50 0.27 Karnataka Jammu & Kashmir 4.40 0.26 Jammu & Kashmir Assam 4.40 0.26 Karnataka Maharashtra 3.70 0.23 Tamil Nadu Kerala 3.45 0.22 Kerala Punjab 3.20 0.21 Jammu & Kashmir Karnataka 3.00 0.20

223

17 62 .71 0.45 9.05 0.00 0.45 6.33 2 3. 8.14 0.00 3. 4.98 0.45 % 48.87 11.76 100.00

1 1 6 7 8 1 20 14 18 11 26 108 221 Total

6 2 9

017 2

8 1 8 11 2016

1 1 3 5 6 1 17 17 53 26 141 2015

2 3 2 6 2 12 2014

1 1 3 39 47

2013

Year 2 2 4 2012

2011

2010

Distribution of A/H1N1pdm09 viruses belonging to clade 6B by year

2009

) 6

(12.5)

(91.3)

Supplementary Table S5.9: and S/UT in India between 2009 and 2011. S/UT (Population 10 Assam (31.2) Delhi (16.8) Goa (1.5) Haryana (25.4) Kashmir & Jammu Karnataka (61.1) Kerala (33.4) Madhya Pradesh (72.6) Maharashtra (112.4) Punjab (27.7) Rajasthan (68.6) Tamil Nadu (72.15) Uttarakhand (10.1) West Bengal Year Total

224

Appendix B: A/H3N2 Acknowledgment Table

We gratefully acknowledge the authors and originating and submitting laboratories of the sequences from GISAID’s EpiFlu™ Database on which Chapter Four’s research is based. The list is detailed below:

ID Name Submitting Authors / Submitting Laboratory 246677 A/Brisbane/3/2017 WHO Collaborating Centre for Reference and Research on Influenza 249157 A/Brisbane/2/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 249168 A/South Australia/15/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 249169 A/South Australia/19/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 249173 A/South Australia/13/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 249174 A/South Australia/4/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 249175 A/South Australia/22/2017 Deng,Y-M.; Iannello,P.; Spirason,N.; Lau,H.; Kaye,M.; Komadina,N. 261878 A/Darwin/1004/2017 Deng, Y-M.; Iannello, P.; Spirason, N.; Lau, H.; Kaye, M.; Komadina, N. 261879 A/South Australia/32/2017 Deng, Y-M.; Iannello, P.; Spirason, N.; Lau, H.; Kaye, M.; Komadina, N. 261880 A/Darwin/1002/2017 Deng, Y-M.; Iannello, P.; Spirason, N.; Lau, H.; Kaye, M.; Komadina, N. 261881 A/South Australia/41/2017 Deng, Y-M.; Iannello, P.; Spirason, N.; Lau, H.; Kaye, M.; Komadina, N. 261885 A/Darwin/1001/2017 Deng, Y-M.; Iannello, P.; Spirason, N.; Lau, H.; Kaye, M.; Komadina, N. 269255 A/Newcastle/23/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269256 A/Newcastle/30/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269257 A/Newcastle/25/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269258 A/Newcastle/31/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269266 A/Brisbane/19/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269267 A/Newcastle/22/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269268 A/Sydney/12/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269269 A/South Australia/54/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269270 A/South Australia/59/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269271 A/Darwin/1005/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269272 A/South Australia/58/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269273 A/Sydney/51/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269276 A/Sydney/17/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269279 A/Townsville/5/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269280 A/South Australia/62/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269281 A/Sydney/56/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269282 A/Brisbane/35/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269283 A/Sydney/60/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269284 A/Sydney/42/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269296 A/South Australia/14/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269297 A/South Australia/12/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269298 A/South Australia/20/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269299 A/South Australia/2/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269300 A/South Australia/3/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269301 A/South Australia/5/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269302 A/South Australia/7/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269303 A/South Australia/8/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269304 A/South Australia/17/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269305 A/South Australia/18/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269306 A/Newcastle/14/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269307 A/Newcastle/2/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269308 A/Newcastle/4/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269309 A/Newcastle/5/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269310 A/Newcastle/7/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269311 A/Newcastle/8/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269312 A/Newcastle/9/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269313 A/Newcastle/11/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269314 A/Newcastle/12/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269315 A/Newcastle/13/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269316 A/Newcastle/15/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269317 A/Perth/4/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269318 A/Perth/41/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269319 A/Perth/48/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269320 A/Perth/32/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269321 A/Perth/45/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269322 A/Perth/43/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269323 A/Perth/47/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269324 A/Perth/51/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269325 A/Perth/2/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269326 A/Perth/14/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269327 A/Perth/11/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269328 A/Perth/20/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269329 A/Perth/27/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269330 A/Perth/35/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N.

225

269331 A/Perth/40/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269332 A/Perth/34/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269333 A/Perth/42/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269334 A/Perth/46/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269335 A/Victoria/1000/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269336 A/Darwin/1003/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269337 A/South Australia/23/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269338 A/South Australia/24/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269339 A/South Australia/25/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269340 A/South Australia/28/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269341 A/South Australia/30/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269342 A/South Australia/31/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269343 A/South Australia/33/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269344 A/South Australia/34/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269345 A/South Australia/35/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269346 A/South Australia/39/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269349 A/Brisbane/8/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269350 A/Brisbane/9/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269351 A/Brisbane/10/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269352 A/Brisbane/11/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269353 A/Brisbane/14/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269354 A/Brisbane/15/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269355 A/Brisbane/21/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269356 A/Brisbane/22/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269357 A/Brisbane/23/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269358 A/Brisbane/24/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269359 A/Brisbane/26/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269360 A/Brisbane/72/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269361 A/Townsville/1/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269362 A/Newcastle/20/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269363 A/Newcastle/29/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269364 A/Newcastle/32/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269365 A/Newcastle/18/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269366 A/Newcastle/19/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269367 A/Newcastle/21/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269368 A/Newcastle/24/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269369 A/Newcastle/27/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269370 A/Newcastle/28/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269371 A/Tasmania/1/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269372 A/Tasmania/2/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269373 A/Tasmania/6/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269374 A/Tasmania/7/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269375 A/Tasmania/9/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269376 A/Victoria/1/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269377 A/Perth/53/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269378 A/Perth/54/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269379 A/Perth/55/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269380 A/Perth/56/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269381 A/Perth/57/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269382 A/Perth/58/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269383 A/Perth/60/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269384 A/Perth/61/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269385 A/South Australia/1002/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269386 A/South Australia/63/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269387 A/South Australia/80/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269388 A/Canberra/1/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269390 A/Townsville/1001/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269391 A/South Australia/45/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269392 A/South Australia/46/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269393 A/South Australia/47/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269394 A/South Australia/49/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269395 A/South Australia/50/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269396 A/South Australia/51/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269397 A/South Australia/52/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269398 A/South Australia/53/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269399 A/South Australia/55/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269400 A/South Australia/56/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269401 A/Sydney/1000/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269402 A/South Australia/57/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269403 A/Sydney/49/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269404 A/Sydney/50/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269405 A/Sydney/52/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269406 A/Sydney/55/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269407 A/Sydney/57/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269408 A/Sydney/58/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269409 A/Sydney/61/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269410 A/Sydney/62/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N.

226

269411 A/Sydney/63/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269412 A/Sydney/66/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269413 A/Sydney/67/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269414 A/Sydney/68/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269415 A/Sydney/77/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269416 A/Brisbane/27/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269417 A/Sydney/69/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269418 A/Brisbane/28/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269419 A/Brisbane/29/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269420 A/Brisbane/32/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269421 A/Brisbane/33/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269422 A/Brisbane/34/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269423 A/South Australia/60/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269424 A/South Australia/61/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269425 A/Sydney/64/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269426 A/Sydney/65/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269427 A/Sydney/70/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269428 A/Perth/68/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269429 A/Perth/64/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269430 A/South Australia/64/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269431 A/South Australia/66/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269432 A/South Australia/70/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269433 A/South Australia/79/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269434 A/South Australia/65/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269435 A/South Australia/67/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269436 A/South Australia/68/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269437 A/South Australia/71/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269438 A/South Australia/72/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269439 A/South Australia/73/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269440 A/South Australia/74/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269441 A/South Australia/75/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269442 A/South Australia/76/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269443 A/South Australia/77/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269444 A/South Australia/78/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269445 A/South Australia/81/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269446 A/South Australia/69/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269447 A/Canberra/5/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269448 A/Canberra/6/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269449 A/Sydney/83/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269450 A/Sydney/103/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269451 A/Sydney/108/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269452 A/South Auckland/15/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269453 A/South Auckland/16/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269454 A/South Auckland/18/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269455 A/South Auckland/20/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269456 A/South Auckland/21/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269457 A/South Auckland/22/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269458 A/South Auckland/23/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269459 A/South Auckland/24/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269460 A/South Auckland/26/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269461 A/South Auckland/27/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269462 A/South Auckland/30/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269463 A/South Auckland/31/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269465 A/Newcastle/53/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269466 A/South Auckland/14/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269467 A/South Auckland/17/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269468 A/South Auckland/19/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269469 A/South Auckland/28/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 269470 A/South Auckland/32/2017 Deng,Y-M.; Iannello,P.; Kaye,M.; Lau,H; Todd,A.; Komadina,N. 271229 A/South Auckland/33/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271230 A/South Auckland/35/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271231 A/South Auckland/36/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271232 A/South Auckland/38/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271233 A/South Auckland/39/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271234 A/South Auckland/40/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271235 A/South Auckland/41/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271236 A/South Auckland/42/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271237 A/South Auckland/45/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271238 A/Bay Of Plenty/1/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271239 A/Sydney/78/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271240 A/Sydney/84/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271241 A/Sydney/96/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271242 A/Sydney/97/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271243 A/Sydney/99/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271244 A/Sydney/107/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271245 A/Sydney/110/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271246 A/Sydney/1003/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N.

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271247 A/South Australia/82/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271248 A/South Australia/83/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271249 A/South Australia/84/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271250 A/South Australia/85/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271251 A/South Australia/86/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271252 A/South Australia/87/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271253 A/South Australia/88/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271254 A/South Australia/89/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271255 A/South Australia/91/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271256 A/South Australia/92/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271257 A/South Australia/93/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271258 A/South Australia/94/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271259 A/South Australia/99/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271260 A/South Australia/102/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271261 A/South Australia/103/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271262 A/South Australia/104/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271263 A/South Australia/106/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271264 A/South Australia/108/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271265 A/South Australia/110/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271266 A/Perth/69/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271267 A/Perth/70/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271268 A/Perth/71/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271269 A/Perth/73/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271270 A/Perth/78/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271271 A/Brisbane/39/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271272 A/Brisbane/44/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271273 A/Brisbane/45/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271274 A/Brisbane/46/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271275 A/Brisbane/47/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271276 A/Brisbane/48/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271277 A/Brisbane/50/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271278 A/Brisbane/52/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271279 A/Brisbane/57/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271280 A/Brisbane/58/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271281 A/Brisbane/61/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271282 A/South Australia/112/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271283 A/South Australia/113/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271284 A/South Australia/114/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271285 A/South Australia/115/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271286 A/South Australia/116/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271287 A/South Australia/118/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271288 A/South Australia/119/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271289 A/South Australia/120/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271290 A/South Australia/121/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271291 A/South Australia/122/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271292 A/South Australia/123/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271293 A/South Australia/124/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271294 A/South Australia/125/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271295 A/South Australia/126/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271296 A/South Australia/127/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271297 A/South Australia/128/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271298 A/South Australia/129/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271299 A/South Australia/130/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271300 A/South Australia/131/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271301 A/South Australia/132/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271302 A/South Australia/134/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271303 A/Victoria/9/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271304 A/Victoria/10/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271305 A/Victoria/2010/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271306 A/Victoria/2014/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271307 A/Victoria/2015/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271308 A/Victoria/2017/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271309 A/Victoria/2018/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271318 A/Sydney/119/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271319 A/Sydney/120/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271320 A/Sydney/121/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 271321 A/Sydney/134/2017 Deng, Y-M.; Iannello, P.; Lau,H.; Kaye,M.; Todd,A.; Komadina, N. 274915 A/Sydney/114/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274916 A/Sydney/115/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274917 A/Sydney/131/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274918 A/Sydney/126/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274919 A/Sydney/138/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274920 A/Sydney/123/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274921 A/Brisbane/38/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274922 A/Brisbane/43/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274933 A/Victoria/2008/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274934 A/Victoria/2011/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N.

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274935 A/Victoria/2012/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274936 A/Victoria/2013/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274937 A/Sydney/122/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274938 A/Sydney/124/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274939 A/Sydney/125/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274940 A/Sydney/128/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274941 A/Sydney/132/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274942 A/Sydney/133/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274943 A/Sydney/136/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274944 A/Brisbane/53/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274951 A/Brisbane/91/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274952 A/South Auckland/49/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 274953 A/South Auckland/50/2017 Deng,Y-M; 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275250 A/Victoria/519/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275251 A/Christchurch/503/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275252 A/Christchurch/513/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275253 A/Sydney/154/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275254 A/Victoria/517/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275255 A/Victoria/537/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275256 A/Victoria/2043/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275257 A/Victoria/2044/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275258 A/South Australia/187/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275259 A/South Australia/190/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275260 A/South Australia/191/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 275261 A/South Australia/192/2017 Deng,Y-M; Iannello,P; 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Centre for Reference and Research on Influenza 277319 A/Victoria/2036/2017 WHO Collaborating Centre for Reference and Research on Influenza 277320 A/Victoria/2040/2017 WHO Collaborating Centre for Reference and Research on Influenza 277321 A/Sydney/150/2017 WHO Collaborating Centre for Reference and Research on Influenza 277322 A/Sydney/151/2017 WHO Collaborating Centre for Reference and Research on Influenza 277323 A/Canberra/19/2017 WHO Collaborating Centre for Reference and Research on Influenza 277324 A/Canberra/20/2017 WHO Collaborating Centre for Reference and Research on Influenza 277325 A/Canberra/22/2017 WHO Collaborating Centre for Reference and Research on Influenza 277326 A/Canberra/25/2017 WHO Collaborating Centre for Reference and Research on Influenza 277327 A/Victoria/2027/2017 WHO Collaborating Centre for Reference and Research on Influenza 277328 A/Victoria/2035/2017 WHO Collaborating Centre for Reference and Research on Influenza 277329 A/Victoria/2037/2017 WHO Collaborating Centre for Reference and Research on Influenza 277330 A/Victoria/2038/2017 WHO Collaborating Centre for Reference and Research on Influenza 277331 A/Victoria/2039/2017 WHO Collaborating Centre for Reference and Research on Influenza 277332 A/Victoria/1005/2017 WHO Collaborating Centre for Reference and Research on Influenza 277333 A/South Australia/209/2017 WHO Collaborating Centre for Reference and Research on Influenza 277334 A/Tasmania/3/2017 WHO Collaborating Centre for Reference and Research on Influenza 277335 A/Tasmania/17/2017 WHO Collaborating Centre for Reference and Research on Influenza 277336 A/Tasmania/18/2017 WHO Collaborating Centre for Reference and Research on Influenza 277337 A/Tasmania/21/2017 WHO Collaborating Centre for Reference and Research on Influenza 277338 A/Victoria/51/2017 WHO Collaborating Centre for Reference and Research on Influenza 277339 A/Victoria/53/2017 WHO Collaborating Centre for Reference and Research on Influenza 277340 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and Research on Influenza 277351 A/Tasmania/27/2017 WHO Collaborating Centre for Reference and Research on Influenza 277352 A/Victoria/33/2017 WHO Collaborating Centre for Reference and Research on Influenza 277353 A/Victoria/34/2017 WHO Collaborating Centre for Reference and Research on Influenza 277354 A/Victoria/37/2017 WHO Collaborating Centre for Reference and Research on Influenza 277355 A/Victoria/42/2017 WHO Collaborating Centre for Reference and Research on Influenza 277356 A/Victoria/43/2017 WHO Collaborating Centre for Reference and Research on Influenza 277357 A/Victoria/45/2017 WHO Collaborating Centre for Reference and Research on Influenza 277358 A/Victoria/46/2017 WHO Collaborating Centre for Reference and Research on Influenza 277359 A/Victoria/47/2017 WHO Collaborating Centre for Reference and Research on Influenza 277360 A/Victoria/54/2017 WHO Collaborating Centre for Reference and Research on Influenza 277361 A/Victoria/56/2017 WHO Collaborating Centre for Reference and Research on Influenza 277362 A/Tasmania/38/2017 WHO Collaborating Centre for Reference and Research on Influenza 277363 A/Tasmania/39/2017 WHO Collaborating Centre for Reference and Research on Influenza 277364 A/Perth/110/2017 WHO Collaborating Centre for Reference and Research on Influenza 277365 A/Victoria/596/2017 WHO Collaborating Centre for Reference and Research on Influenza 277366 A/Victoria/612/2017 WHO Collaborating Centre for Reference and Research on Influenza 277541 A/Brisbane/1009/2017 WHO Collaborating Centre for Reference and Research on Influenza 277542 A/Brisbane/1010/2017 WHO Collaborating Centre for Reference and Research on Influenza 277543 A/Brisbane/1011/2017 WHO Collaborating Centre for Reference and Research on Influenza 277544 A/Canberra/28/2017 WHO Collaborating Centre for Reference and Research on Influenza 277545 A/Canberra/32/2017 WHO Collaborating Centre for Reference and Research on Influenza 277546 A/Canberra/33/2017 WHO Collaborating Centre for Reference and Research on Influenza 277547 A/Canberra/34/2017 WHO Collaborating Centre for Reference and Research on Influenza 277548 A/Canberra/35/2017 WHO Collaborating Centre for Reference and Research on Influenza 277549 A/Canberra/36/2017 WHO Collaborating Centre for Reference and Research on Influenza 277550 A/Canberra/37/2017 WHO Collaborating Centre for Reference and Research on Influenza 277551 A/Canberra/38/2017 WHO Collaborating Centre for Reference and Research on Influenza 277552 A/Canberra/40/2017 WHO Collaborating Centre for Reference and Research on Influenza 277553 A/Newcastle/93/2017 WHO Collaborating Centre for Reference and Research on Influenza 277554 A/Perth/106/2017 WHO Collaborating Centre for Reference and Research on Influenza 277555 A/Perth/112/2017 WHO Collaborating Centre for Reference and Research on Influenza 277556 A/Perth/117/2017 WHO Collaborating Centre for Reference and Research on Influenza 277557 A/Perth/94/2017 WHO Collaborating Centre for Reference and Research on Influenza 277558 A/Perth/99/2017 WHO Collaborating Centre for Reference and Research on Influenza

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232

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233

277986 A/Tasmania/35/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277987 A/Tasmania/37/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277988 A/Victoria/566/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277989 A/Victoria/560/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277990 A/Victoria/561/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277991 A/Victoria/60/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277992 A/Victoria/545/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277993 A/Victoria/546/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277994 A/Victoria/547/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277995 A/Victoria/548/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277996 A/Victoria/549/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277997 A/Victoria/554/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277998 A/Victoria/559/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 277999 A/Victoria/563/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278000 A/Victoria/564/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278001 A/Victoria/575/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278002 A/Victoria/579/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278003 A/Victoria/580/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278004 A/Victoria/581/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278005 A/Victoria/582/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278006 A/Victoria/583/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278007 A/Victoria/587/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278008 A/Tasmania/31/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278009 A/Tasmania/30/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278010 A/Tasmania/32/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278011 A/Canberra/31/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278012 A/Sydney/1032/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278013 A/South Australia/1017/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278014 A/Brisbane/1008/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278015 A/Sydney/1048/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278016 A/Brisbane/1026/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278019 A/Tasmania/006/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278020 A/Victoria/586/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278021 A/Victoria/572/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278022 A/Victoria/573/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278023 A/Victoria/576/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 278024 A/Victoria/578/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N 282258 A/South Auckland/4/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282259 A/South Auckland/1/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282268 A/Sydney/81/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282269 A/South Auckland/43/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282270 A/Bay Of Plenty/2/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282272 A/Perth/77/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282273 A/South Australia/109/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282340 A/South Australia/140/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282341 A/Wellington/1/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282342 A/Sydney/147/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282625 A/Victoria/521/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282626 A/Victoria/621/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282627 A/Canberra/41/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282628 A/Sydney/1050/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282629 A/Brisbane/1028/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282633 A/Wellington/44/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282634 A/Canterbury/15/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 282642 A/Canberra/26/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Komadina,N. 283119 A/Victoria/697/2017 WHO Collaborating Centre for Reference and Research on Influenza 283120 A/Victoria/872/2017 WHO Collaborating Centre for Reference and Research on Influenza 283121 A/Darwin/1016/2017 WHO Collaborating Centre for Reference and Research on Influenza 283122 A/Victoria/708/2017 WHO Collaborating Centre for Reference and Research on Influenza 283123 A/Victoria/211/2017 WHO Collaborating Centre for Reference and Research on Influenza 283124 A/Brisbane/140/2017 WHO Collaborating Centre for Reference and Research on Influenza 283125 A/Sydney/1101/2017 WHO Collaborating Centre for Reference and Research on Influenza 284857 A/New Zealand/1218/2017 Centers for Disease Control and Prevention 284860 A/New Zealand/1051/2017 Centers for Disease Control and Prevention 284870 A/New Zealand/669/2017 Centers for Disease Control and Prevention 284871 A/New Zealand/883/2017 Centers for Disease Control and Prevention 284874 A/New Zealand/2269/2017 Centers for Disease Control and Prevention 284876 A/New Zealand/1213/2017 Centers for Disease Control and Prevention 284877 A/New Zealand/1052/2017 Centers for Disease Control and Prevention 284890 A/New Zealand/1102/2017 Centers for Disease Control and Prevention 284891 A/New Zealand/1070/2017 Centers for Disease Control and Prevention 284894 A/New Zealand/882/2017 Centers for Disease Control and Prevention 284895 A/New Zealand/875/2017 Centers for Disease Control and Prevention 284898 A/New Zealand/758/2017 Centers for Disease Control and Prevention 284917 A/New Zealand/1090/2017 Centers for Disease Control and Prevention 284920 A/New Zealand/1419/2017 Centers for Disease Control and Prevention 284921 A/New Zealand/770/2017 Centers for Disease Control and Prevention

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285705 A/New Zealand/755/2017 Centers for Disease Control and Prevention 285706 A/New Zealand/707/2017 Centers for Disease Control and Prevention 285708 A/New Zealand/695/2017 Centers for Disease Control and Prevention 286031 A/New Zealand/2337/2017 Centers for Disease Control and Prevention 286033 A/New Zealand/2267/2017 Centers for Disease Control and Prevention 286064 A/New Zealand/2379/2017 Centers for Disease Control and Prevention 286066 A/New Zealand/2391/2017 Centers for Disease Control and Prevention 286067 A/New Zealand/2380/2017 Centers for Disease Control and Prevention 288868 A/South Australia/111/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 288874 A/Perth/121/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 288889 A/Victoria/729/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 288890 A/Victoria/740/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 288891 A/Victoria/741/2017 Deng,Y-M.; Iannello,P.; 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Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289864 A/Victoria/1033/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289865 A/Newcastle/1004/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289866 A/Sydney/1156/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289867 A/South Australia/1051/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289868 A/South Australia/1052/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289869 A/South Australia/1053/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289870 A/Sydney/1158/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289871 A/Brisbane/1071/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289872 A/Sydney/1160/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289873 A/Brisbane/1074/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289874 A/Victoria/1034/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289875 A/South Australia/1054/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289876 A/South Australia/1060/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289877 A/South Australia/1062/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289878 A/Sydney/1162/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289879 A/Sydney/1163/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289880 A/Sydney/1164/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289881 A/Sydney/1167/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289882 A/Brisbane/1076/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289883 A/Tasmania/1031/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289884 A/Tasmania/1032/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289885 A/Sydney/1172/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289886 A/Sydney/1173/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289887 A/Sydney/1177/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289888 A/Sydney/1179/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289889 A/Brisbane/1082/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289890 A/Victoria/1037/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289891 A/South Australia/1066/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289892 A/South Australia/1067/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289893 A/Sydney/190/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289894 A/Victoria/189/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289895 A/South Australia/237/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289896 A/South Australia/238/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289897 A/South Australia/242/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289898 A/South Australia/244/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289899 A/Sydney/168/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289900 A/Victoria/2156/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289901 A/Victoria/2162/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289902 A/Victoria/2163/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289903 A/Victoria/720/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289904 A/Victoria/723/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N.

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289905 A/Victoria/706/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289906 A/Victoria/707/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289907 A/Victoria/714/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289908 A/Victoria/724/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289909 A/Victoria/725/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289910 A/Victoria/726/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289911 A/Victoria/745/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289913 A/Victoria/732/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289914 A/Victoria/734/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289915 A/Victoria/739/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289916 A/Victoria/738/2017 Deng,Y-M.; Iannello,P.; Lau, H.; Kaye,M.; Todd,A.; Komadina,N. 289921 A/Victoria/748/2017 Deng,Y-M.; 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Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291327 A/Victoria/861/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291328 A/Victoria/862/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291329 A/Victoria/863/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291330 A/Victoria/868/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291331 A/Victoria/216/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291332 A/Canberra/75/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291333 A/Tasmania/61/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291334 A/Tasmania/66/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291335 A/Victoria/882/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291336 A/Victoria/883/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 291337 A/South Australia/246/2017 Deng,Y-M.; Iannello,P.; Lau,H.; 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293755 A/Tasmania/57/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293756 A/Tasmania/58/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293757 A/Tasmania/59/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293758 A/Tasmania/62/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293759 A/Tasmania/63/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293760 A/Tasmania/64/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293761 A/Victoria/204/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293762 A/Victoria/205/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293763 A/Victoria/206/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293764 A/Victoria/212/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293765 A/Perth/225/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293766 A/Victoria/634/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293767 A/Victoria/642/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293768 A/Victoria/193/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293769 A/Victoria/196/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293770 A/Victoria/201/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293771 A/Victoria/222/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293772 A/Victoria/221/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293773 A/Victoria/943/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293774 A/Victoria/233/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293775 A/Victoria/180/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293776 A/Victoria/182/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293777 A/Victoria/184/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293778 A/Victoria/185/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293779 A/Victoria/186/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293780 A/Victoria/190/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293781 A/Victoria/192/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293782 A/South Australia/259/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293783 A/South Australia/261/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293784 A/Christchurch/515/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293785 A/Christchurch/516/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293786 A/Tasmania/67/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293787 A/Tasmania/68/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293788 A/Tasmania/69/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293789 A/Tasmania/71/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293790 A/Tasmania/72/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293791 A/Tasmania/73/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293792 A/Tasmania/74/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293793 A/Tasmania/75/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293794 A/Tasmania/76/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293799 A/Brisbane/185/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293800 A/Brisbane/187/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293801 A/Brisbane/188/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293802 A/Brisbane/189/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293803 A/Brisbane/190/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293804 A/Brisbane/192/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293805 A/Brisbane/1096/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293806 A/Brisbane/1098/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293807 A/Sydney/220/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293808 A/Sydney/221/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293809 A/Perth/317/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293810 A/Perth/322/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293811 A/Perth/333/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293812 A/Perth/325/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293813 A/Perth/331/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293814 A/Perth/285/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293815 A/Victoria/899/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293832 A/Perth/329/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293833 A/Perth/330/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 293834 A/Canberra/74/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299598 A/Brisbane/1094/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299602 A/South Australia/276/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299603 A/South Australia/274/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299604 A/Sydney/1207/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299605 A/South Australia/273/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299606 A/Perth/336/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299607 A/Sydney/224/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299608 A/Sydney/235/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299636 A/Sydney/1199/2017 Deng,Y-M.; Iannello,P.; 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299684 A/South Australia/1087/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299685 A/South Australia/270/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299686 A/South Australia/271/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299687 A/South Australia/275/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299693 A/Sydney/236/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 299694 A/Brisbane/1099/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Komadina,N. 305345 A/Sydney/546/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Spirason,N.; Komadina,N. 305348 A/Sydney/543/2017 Deng,Y-M.; Iannello,P.; Lau,H.; Kaye,M.; Todd,A.; Spirason,N.; Komadina,N. 312144 A/Newcastle/207/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 312146 A/Newcastle/206/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 312171 A/Brisbane/195/2017 Deng,Y-M; Iannello,P; 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Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313351 A/Perth/134/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313352 A/Perth/135/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313353 A/Perth/142/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313354 A/Perth/143/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313355 A/Perth/153/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313356 A/Victoria/74/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313357 A/Victoria/92/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313358 A/Victoria/102/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313359 A/Victoria/106/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313360 A/Victoria/107/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313361 A/Victoria/113/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313362 A/Victoria/114/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313363 A/Victoria/120/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313364 A/Victoria/124/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313365 A/Victoria/126/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313366 A/Victoria/134/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313367 A/Victoria/136/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313368 A/Victoria/137/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313369 A/Victoria/145/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313370 A/Victoria/146/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313371 A/Victoria/147/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313372 A/Victoria/148/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313373 A/Victoria/150/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313374 A/Victoria/151/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313375 A/Victoria/153/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313376 A/Victoria/72/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313377 A/Victoria/73/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313378 A/Victoria/81/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313379 A/Victoria/90/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313380 A/Victoria/94/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313381 A/Victoria/101/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313382 A/Victoria/103/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313383 A/Victoria/118/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313384 A/Victoria/123/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N.

238

313385 A/Victoria/133/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313386 A/Victoria/139/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313387 A/Victoria/143/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313388 A/Victoria/154/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313389 A/Victoria/160/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313390 A/Victoria/163/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313391 A/Victoria/161/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313392 A/Victoria/170/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313393 A/Victoria/172/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313394 A/Victoria/173/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313395 A/Victoria/178/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313396 A/Perth/207/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313397 A/Perth/200/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313398 A/Perth/262/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313399 A/Perth/294/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313400 A/Victoria/234/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313401 A/Victoria/235/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313402 A/Victoria/236/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313403 A/Victoria/237/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313404 A/Brisbane/1089/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313405 A/Sydney/1185/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313406 A/Darwin/1019/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313407 A/South Australia/1071/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313408 A/Sydney/1193/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313409 A/South Australia/1084/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313410 A/Sydney/1186/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313411 A/South Australia/1068/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313412 A/Sydney/1187/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313413 A/Sydney/1189/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313414 A/Tasmania/1036/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313415 A/Brisbane/1088/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313416 A/Victoria/1038/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313417 A/Victoria/1039/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313418 A/Brisbane/1091/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313419 A/South Australia/1069/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313420 A/Tasmania/1038/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313421 A/Sydney/1195/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313422 A/Sydney/1196/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313423 A/Sydney/1197/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313424 A/South Australia/1073/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313425 A/South Australia/1075/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 313426 A/Victoria/164/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N. 322174 A/Canterbury/31/2017 Deng,Y-M; Iannello,P; Lau,H; Kaye,M; Todd,A; Spirason,N; Komadina,N.

239

Appendix C: A/H1N1pdm09 Acknowledgment

Table

We gratefully acknowledge the authors and originating and submitting laboratories of the sequences from GISAID’s EpiFlu™ Database on which Chapter Two’s research is based. The list is detailed below:

ID Name Submitting Authors / Submitting Laboratory 66666 A/India/8489/2009 Garten,R. 70312 A/India/4725/2009 Garten,R. 71311 A/Blore/NIV236/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71312 A/Blore/NIV310/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S.; Mullick,J.; Cherian,S.S.; 71313 A/Delhi/NIV3610/2009 Potdar,V.A.; Chadha,M.S.; Mishra,A.C. 71314 A/Delhi/NIV3704/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71316 A/Mum/NIV5442/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71317 A/Mum/NIV9945/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71318 A/Pune/NIV10278/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71319 A/Pune/NIV10604/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71320 A/Pune/NIV6196/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71321 A/Pune/NIV6447/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71322 A/Pune/NIV8489/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 71323 A/Pune/NIV9355/2009 Mishra,A.; Potdar,V.; Chadha,M.; Jadhav,S.; Mullick,J.; Cherian,S. 75208 A/Blore/NIV1189/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75209 A/Pune/NIV20007/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75210 A/Pune/NIV21115/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75211 A/Pune/NIV21123/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75212 A/Mum/NIV261/2009 Mullick,J.; Potdar,V.A.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75213 A/Delhi/NIV57/2009 Mullick,J.; Potdar,V.A.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75214 A/Assam/RMRC_711/2016 Borkakoty,B.; Jakharia,A.; Sarmah,K.; Hazarika,R.; Biswas,D.; Mahanta,J. 75215 A/Pune/NIV807/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75216 A/Mum/NIV9312/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75217 A/Dhule/NIV9433/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75218 A/Jalna/NIV9436/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75219 A/Ytml/NIV9438/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 75220 A/Mum/NIV968/2009 Mullick,J.; Potdar,V.A.; Chadha,M.; Payyapilly,B.J.; Keng,S.S.; Cherian,S.S.; Jadhav,S.M.; Mishra,A.C. 77718 A/India/3725/2010 Garten,R. 79357 A/India/5107/2010 Garten,R. 79358 A/India/8910/2010 Garten,R. 79359 A/India/007/2010 Garten,R. 79711 A/India/8910/2010 Garten,R. 93611 A/India/4947/2011 Garten,R. 95123 A/Pune/NIV759/2009 Mullick,J.; Potdar,V.A. 95124 A/Pune/NIV652/2009 Mullick,J.; Cherian,S.S.; Potdar,V.A.; Chadha,M.S.; Mishra,A.C. 95125 A/Assam/RMRC_693/2016 Borkakoty,B.; Jakharia,A.; Sarmah,K.; Hazarika,R.; Biswas,D.; Mahanta,J. 95126 A/Mum/NIV398/2009 Mullick,J.; Potdar,V.A. 95127 A/Nanded/NIV29214/2010 Mullick,J.; Potdar,V.A. 95128 A/Pune/NIV26410/2010 Mullick,J.; Potdar,V.A. 95132 A/Kolar/MCVRAF3154/2017 Jagadesh,A.; Arunkumar,G. 95133 A/Assam/RMRC_709/2016 Borkakoty,B.; Jakharia,A.; Sarmah,K.; Hazarika,R.; Biswas,D.; Mahanta,J. 95134 A/Ngp/NIV22704/2010 Mullick,J.; Potdar,V.A. 95135 A/Pune/NIV21259/2009 Mullick,J.; Potdar,V.A. 95136 A/Pune/NIV21218/2009 Mullick,J.; Potdar,V.A. 95137 A/Pune/NIV21193/2009 Mullick,J.; Potdar,V.A. 95138 A/Pune/NIV21163/2009 Mullick,J.; Potdar,V.A. 95139 A/Pune/NIV21159/2009 Mullick,J.; Potdar,V.A. 95140 A/Pune/NIV21139/2009 Mullick,J.; Potdar,V.A. 95141 A/Pune/NIV21090/2009 Mullick,J.; Potdar,V.A. 95142 A/Pune/NIV20069/2009 Mullick,J.; Potdar,V.A. 95143 A/Pune/NIV19934/2009 Mullick,J.; Potdar,V.A. 95144 A/Pune/NIV19609/2009 Mullick,J.; Potdar,V.A. 95145 A/Pune/NIV161/2009 Mullick,J.; Potdar,V.A. 95146 A/Pune/NIV15492/2009 Mullick,J.; Potdar,V.A. 95147 A/Pune/NIV14886/2009 Mullick,J.; Potdar,V.A. 95148 A/Ngp/NIV14725/2009 Mullick,J.; Potdar,V.A. 95150 A/Pune/NIV1166/2009 Mullick,J.; Potdar,V.A. 95152 A/Mum/NIV1134/2009 Mullick,J.; Potdar,V.A. 95153 A/Mum/NIV1126/2009 Mullick,J.; Potdar,V.A. 95154 A/Ngp/NIV11203/2009 Mullick,J.; Potdar,V.A. 95155 A/Kol/NIV105777/2009 Mullick,J.; Potdar,V.A.

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Parida,M.; Dash,P.K.; Kumar,J.S.; Joshi,G.; Tandel,K.; Sharma,S.; Srivastava,A.; Agarwal,A.; Saha,A.; 258676 A/India/DRDE_GWL703/2015 Saraswat,S.; Karothia,D.; Malviya,V. 258677 A/India/Kol-3828/2015 Mukherjee,A.; Chawla-Sarkar,M. 258678 A/Betul/6515/2015 Sahu,M.; Shukla,M.K.; Singh,N.; Barde,P.V. Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258679 A/India/D1510043/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. 258680 A/India/Srinagar1513279/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. Parida,M.; Dash,P.K.; Kumar,J.S.; Joshi,G.; Tandel,K.; Sharma,S.; Srivastava,A.; Agarwal,A.; Saha,A.; 258681 A/India/DRDE_GWL721/2015 Saraswat,S.; Karothia,D.; Malviya,V. 258682 A/India/P154479/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258683 A/India/Kol-4122/2015 Mukherjee,A.; Chawla-Sarkar,M. 258684 A/India/Kol-S4163/2015 Mukherjee,A.; Chawla-Sarkar,M. 258685 A/India/Kol-4040/2015 Mukherjee,A.; Chawla-Sarkar,M. 258686 A/India/8448/2015 (2015) National Centre for Disease Control (NCDC) Dash,P.K.; Pattabiraman,C.; Tandel,K.; Sharma,S.; Siddappa,S.; Kumar,J.; Gowda,M.; Krishna,S.; 258687 A/India/DRDE_GWL897/2015 Parida,M.M. Parida,M.; Dash,P.K.; Kumar,J.S.; Joshi,G.; Tandel,K.; Sharma,S.; Srivastava,A.; Agarwal,A.; Saha,A.; 258688 A/India/DRDE_GWL719/2015 Saraswat,S.; Karothia,D.; Malviya,V. 258689 A/India/P1510348/2015 Takashita,Emi; Fujisaki,Seiichiro; Shirakura,Masayuki; Watanabe,Shinji; Odagiri,Takato Dash,P.K.; Pattabiraman,C.; Tandel,K.; Sharma,S.; Siddappa,S.; Kumar,J.; Gowda,M.; Krishna,S.; 258690 A/India/DRDE_GWL812/2015 Parida,M.M. 258691 A/India/D1510041/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258692 A/India/P153793/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258693 A/India/P153926/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. 258694 A/India/Srinagar1510349/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258695 A/India/8259/2015 (H1N1) National Centre for Disease Control (NCDC) 258696 A/India/Pun153793/2015 Potdar,V.A.; Hinge,D.D.; Harpale,P.M. 258697 A/India/Srinagar131670/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258698 A/India/Srinagar131672/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. Dash,P.K.; Pattabiraman,C.; Tandel,K.; Sharma,S.; Siddappa,S.; Kumar,J.; Gowda,M.; Krishna,S.; 258699 A/India/DRDE_GWL672/2015 Parida,M.M. 258700 A/India/P152246/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258701 A/India/P152434/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258702 A/Dewas/4497/2015 Sahu,M.; Shukla,M.K.; Singh,N.; Barde,P.V. 258703 A/India/Srinagar1513278/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258704 A/India/Srinagar1513277/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258706 A/India/Kerala128941/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258707 A/India/Kerala128942/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258708 A/India/Kerala128943/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258709 A/India/Kerala1210449/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258710 A/India/Kerala1210448/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258711 A/India/Pun153389/2015 Potdar,V.A.; Hinge,D.D.; Harpale,P.M. 258712 A/India/P153321/2015 Takashita,Emi; Fujisaki,Seiichiro; Shirakura,Masayuki; Watanabe,Shinji; Odagiri,Takato

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258713 A/India/P153225/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258714 A/India/D1510039/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258715 A/India/P153017/2015 Takashita,Emi; Fujisaki,Seiichiro; Shirakura,Masayuki; Watanabe,Shinji; Odagiri,Takato 258716 A/India/P154871/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. Parida,M.; Dash,P.K.; Kumar,J.S.; Joshi,G.; Tandel,K.; Sharma,S.; Srivastava,A.; Agarwal,A.; Saha,A.; 258717 A/India/DRDE_GWL989/2015 Saraswat,S.; Karothia,D.; Malviya,V. 258718 A/India/P153017/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258719 A/India/D1510045/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258720 A/India/D1510038/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258721 A/India/6691/2015 (H1N1) 258722 A/India/Pun153388/2015 Potdar,V.A.; Hinge,D.D.; Harpale,P.M. 258725 A/Ujjain/5448/2015 Sahu,M.; Shukla,M.K.; Singh,N.; Barde,P.V. 258726 A/India/Kerala132655/2012 Potdar,V.; Dakhave,M.; Patil,K.; Chawala Sarkar,M.; B,A.; Koul,P.; Chadha,M. 258728 A/India/Srinagar1513279/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258729 A/India/Srinagar1510348/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258730 A/India/Srinagar1513274/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258731 A/India/Srinagar1513278/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258732 A/India/Srinagar1513277/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258735 A/India/P155355/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258737 A/India/Kol-S4659/2015 Mukherjee,A.; Chawla-Sarkar,M. 258738 A/India/Kol-S4587/2015 Mukherjee,A.; Chawla-Sarkar,M. 258739 A/India/Srinagar1513276/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258740 A/India/Srinagar1513275/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258741 A/India/Srinagar1510350/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258742 A/Assam/RMRC_449/2016 Borkakoty,B.; Jakharia,A.; Sarmah,K.; Hazarika,R.; Biswas,D.; Mahanta,J. 258743 A/India/Kol-S4666/2015 Mukherjee,A.; Chawla-Sarkar,M. 258744 A/India/7512/2016 Centers for Disease Control and Prevention 258745 A/India/4183/2016 Centers for Disease Control and Prevention 258746 A/India/7506/2016 Centers for Disease Control and Prevention 258747 A/Assam/RMRC_226/2016 Borkakoty,B.; Jakharia,A.; Sarmah,K.; Hazarika,R.; Biswas,D.; Mahanta,J. 258748 A/India/3725/2016 Centers for Disease Control and Prevention 258749 A/India/1819/2016 Centers for Disease Control and Prevention 258857 A/India/1819/2016 Centers for Disease Control and Prevention 258858 A/India/5714/2015 Centers for Disease Control and Prevention 258859 A/Assam/SW-84/2015 Biswas,D.; Sarmah,K.; Dutta,M.; Buragohain,M.; Yadav,K.; Borkakoty,B. 258860 A/India/P157674/2015 Takashita,Emi; Fujisaki,Seiichiro; Shirakura,Masayuki; Watanabe,Shinji; Odagiri,Takato 258861 A/India/P158900/2015 Takashita,Emi; Fujisaki,Seiichiro; Shirakura,Masayuki; Watanabe,Shinji; Odagiri,Takato 258862 A/India/P155422/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258863 A/India/8900/2015 Centers for Disease Control and Prevention 258866 A/India/1674/2016 258867 A/India/Jaipur153442/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258868 A/India/Jaipur152573/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258869 A/India/Jaipur152569/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. 258870 A/India/Jaipur152563/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258871 A/India/Srinagar1510349/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258872 A/India/Kol-4992/2015 Mukherjee,A.; Chawla-Sarkar,M. 258873 A/India/C1510064/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258874 A/India/4880/2015 (H1N1) National Centre for Disease Control (NCDC) 258875 A/India/Jaipur152563/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258876 A/India/D1510036/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258877 A/India/D1510035/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258878 A/India/D1510033/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258879 A/India/4302/2015 (H1N1) National Centre for Disease Control (NCDC) 258880 A/India/4101/2015 (H1N1) National Centre for Disease Control (NCDC) Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258881 A/India/P152151/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. 258882 A/India/P152122/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258883 A/India/D1510032/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258884 A/India/P151963/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258885 A/India/D1510031/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258886 A/Kerala/RGCB145808/2015 Jones Palakkat,S.; Chirundodh,D.V.; Sujatha,A.S.; Fettle,A.; Jacob,J.; Pillai,M.R. 258887 A/Barmer/936/2015 Angel,B.; Angel,A.; Joshi,A.P.; Baharia,R.K.; Rathore,S.; Joshi,V. 258888 A/Barmer/922/2015 Angel,B.; Angel,A.; Joshi,A.P.; Baharia,R.K.; Rathore,S.; Joshi,V. 258889 A/India/Srinagar1513276/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. Gurav,Y.K.; Chadha,M.S.; Tandale,B.V.; Potdar,V.A.; Pawar,S.D.; Shil,P.; Deoshatwar,A.R.; Aarthy,R.; 258890 A/India/Jaipur152569/2015 Bhushan,A.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K. 258891 A/India/Jaipur152573/2015 Potdar,V.; Dakhave,M.; Hinge,D.; Bhosle,P.; Koul,P.; Dar,L.; Raj,K.; Chadha,M. 258892 A/Calicut/MCVRAF9834/2017 Jagadesh,A.; Arunkumar,G. 258893 A/Shimoga/MCVRAF9709/2017 Jagadesh,A.; Arunkumar,G. 258894 A/Chikmagalur/MCVRAF7881/2017 Jagadesh,A.; Arunkumar,G. 258895 A/Ernakulam/MCVRAF7821/2017 Jagadesh,A.; Arunkumar,G. 258896 A/Nilgiris/MCVRAF7809/2017 Jagadesh,A.; Arunkumar,G. 258897 A/Mysore/MCVRAF7638/2017 Jagadesh,A.; Arunkumar,G. 258898 A/India/Kol-5065/2015 Mukherjee,A.; Chawla-Sarkar,M. 258899 A/India/Kol-5025/2015 Mukherjee,A.; Chawla-Sarkar,M. 258900 A/India/Kol-5018/2015 Mukherjee,A.; Chawla-Sarkar,M. 258901 A/India/4051/2015

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