The molecular epidemiology and evolution of the 2009 H1N1 influenza A pandemic virus

Transcription

1 The molecular epidemiology and evolution of the 2009 H1N1 influenza A pandemic virus Jessica Hedge Submitted for the degree of Doctor of Philosophy The University of Edinburgh 2013

2 Declaration This thesis is submitted to the University of Edinburgh in accordance with the requirements for the degree of Doctor of Philosophy in the College of Science and Engineering. Some of the work described in this thesis was only possible through collaborations, details of which are presented below. In each case, the majority of the work is my own. Chapter 5 The Bayesian skyride analysis used to generate the empirical tree distribution was carried out by Samantha J. Lycett. Unless otherwise stated, the remaining work and content of this thesis are entirely my own. The work comprising this thesis has not been submitted for any other degree or professional qualification. Signed: Jessica Hedge,

3 Acknowledgments I would like to thank many people who have made it possible for me to complete this thesis. Firstly, I m enormously indebted to my supervisor, Andrew Rambaut, who has provided extensive insight, advice and support throughout my PhD. Secondly, I m grateful to my secondary supervisor, Mark Woolhouse, for his continued enthusiasm and many helpful discussions. I would like to show my gratitude to Sam Lycett, who has assisted me endlessly with conference talks, analyses and writing, and to Tanja Stadler for her guidance in using the birth-death epidemiology model. I m also grateful for the help and constructive feedback provided by members of both Virus Club and Epigroup at the University of Edinburgh, in particular Melissa Ward, Matthew Hall and Trevor Bedford. This work could not have been completed without funding from the BBSRC or ICHAIR (Influenza Consortium for Human and Avian Influenza Research), for which I m grateful. I have been very fortunate to have carried out this work in a vibrant and friendly department and I d like to thank my friends in the Ashworth Laboratories, with whom it was a great pleasure to work. In particular, I owe a great deal to Jayna Raghwani, who has always made time to help me, especially during the final stages of my thesis. Lastly, I m incredibly lucky to have a couple of brilliant parents, who have offered unflagging encouragement from start to finish. And thanks to Jerome Kelleher for all his help and patience. 3

4 Publications The following paper has arisen from this thesis: Hedge, J., Lycett, S.L. and Rambaut, R., Real-time characterization of the molecular epidemiology of an influenza pandemic. Biol Lett, 9(5) DOI: /rsbl

5 Contents 1 Introduction The emergence of the 2009 influenza A pandemic Epidemiological modeling of influenza A virus Compartmental epidemic models The basic reproductive ratio Ecology of influenza A virus Pandemic influenza Seasonal influenza Molecular biology of influenza A virus Genome structure of influenza A virus The origin of the A(H1N1)pdm09 virus Molecular epidemiology using Bayesian phylogenetics Bayesian phylogenetics The phylogenetic likelihood The molecular clock and serially sampled data Nucleotide substitution models The coalescent Methods for model selection A(H1N1)pdm09 virus genome sequence data

6 1.7 Aims of thesis Real-time characterization of the molecular epidemiology of an influenza pandemic Introduction Methods Results Discussion A Appendix Estimation of the initial growth rate of influenza epidemics using a hierarchical phylogenetic model Introduction Methods Sequence data Identifying single-sourced epidemics Bayesian phylogenetic analyses Growth model selection Treatment of logistically growing epidemics Estimating priors for lineage-specific clock rate parameters Results Estimates of lineage-specific priors for the evolutionary rate parameter Choosing growth models for each epidemic Estimation of r 0 under the independent model and the HPM Hierarchical distributions of r Estimation of r 0 using the joint model Treatment of logistically growing epidemics

7 3.4 Discussion A Appendix Analysis of the A(H1N1)pdm09 pandemic using the birth-death epidemiology model Introduction Methods Sequence data Bayesian phylogenetic analysis Results Epidemic characterization under the BDE model and coalescent Epidemic characterization with fixed recovery rate Additional parameter estimation under the BDE model Discussion Analysis of the selection on A(H1N1)pdm09 over its transition from pandemic to seasonal influenza Introduction Methods Sequence data Bayesian phylogenetic analysis Post-processing robust counts of substitutions Results Demographic history of A(H1N1)pdm Non-synonymous and synonymous substitution rates over time Estimates of C N and C S for non-structural proteins 1 and d N /d S at each site across all time Selection on antigenic sites in HA

8 5.4 Discussion A Appendix Conclusion Evaluation of Bayesian phylogenetic methods in molecular epidemiology Characterizing the evolution and molecular epidemiology of A(H1N1)pdm Implications for future Bayesian phylogenetic analysis in molecular epidemiology Bibliography 189 8

9 List of Figures 1.1 SIR dynamics with low and high R 0 values Tree shape under different coalescent priors Accumulation of A(H1N1)pdm09 genome sequence data over time Bayesian skyride reconstruction of the demographic history of A(H1N1)pdm09 in North America in Mean evolutionary rate, date of emergence and r 0 estimates for A(H1N1)pdm09 from cumulatively sampled sequence data Parameter estimates from 100 genome sequences with increasing temporal range Estimates of r 0 from individual analysis of influenza epidemics Demographic reconstructions of exponentially and logistically growing epidemics Correlation of Bayes factor support for exponential growth and duration of sampling Estimates of r 0 from analysis of influenza epidemics individually and under the HPM Plot of dataset size and r 0 estimate from the individual and HPM analysis Hierarchical distributions of r 0 for pandemic and seasonal influenza lineages

10 A3.1 Demographic reconstructions of exponentially and logistically growing epidemics A3.2 Hierarchical distributions of R 0 for pandemic and seasonal influenza lineages Mean evolutionary rate, date of emergence and R 0 estimates for A(H1N1)pdm09 from cumulatively sampled data using the birth-death epidemiology model Mean evolutionary rate, date of emergence and R 0 estimates for A(H1N1)pdm09 from cumulatively sampled data under the birth-death epidemiology model (fixed recovery rate parameter) Sampling probability, time of origin and recovery rate estimates for A(H1N1)pdm09 from cumulatively sampled sequence data under the birth-death epidemiology model Bayesian skyride reconstruction of the demographic history of the A(H1N1)pdm09 and A(H3N2) lineages globally from April 2009 to March Heatmaps of S N, S S and C N /C S for each segment in A(H1N1)pdm09 and A(H3N2) over 6 epochs Bar chart of counts of non-synonymous and synonymous substitutions across all sites in each segment of A(H1N1)pdm09 and A(H3N2) Maximum clade credibility phylogenetic tree for A(H1N1)pdm09 showing the distribution of two substitutions across the tree Proportion of non-synonymous and synonymous substitutions that occur at antigenic sites in A(H1N1)pdm09 and A(H3N2) A5.1 d N /d S estimates for sites in segments of the A(H1N1)pdm09 genome

11 List of Tables 2.1 Log marginal likelihood estimates of both growth models in analysis of cumulatively sampled sequence data A2.1 Collecting and submitting laboratories of sequence data A2.2 Abbreviations used in Table A Datasets used to estimate lineage specific evolutionary rate parameters Details of the epidemic datasets analysed independently, jointly and under an HPM A3.1 Collecting and submitting laboratories of sequence data A3.2 Abbreviations used in Table A Priors placed on parameters in the birth-death epidemiology model Published estimates of R 0 for the 2009 H1N1 pandemic A5.1 Sites in the A(H1N1)pdm09 genome with high estimates of d N /d S A5.2 Collecting and submitting laboratories of sequence data A5.3 Abbreviations used in Table A

12 Thesis abstract The swine-origin H1N1 influenza A pandemic virus (A(H1N1)pdm09) was detected in the human population in March Due to its antigenic novelty, the majority of individuals were susceptible to the virus and the pandemic quickly disseminated around the globe. Rapid characterization of the epidemic was required in order to help inform interventions and determine the risk posed to public health. Widespread sampling and sequencing of virus isolates enabled early characterization of the virus using phylogenetic analysis and continued surveillance over the subsequent three years of global circulation. Throughout this thesis, Bayesian phylogenetic methods are employed to investigate how quickly evolutionary parameters can be accurately and precisely estimated from pandemic genome sequence data and explore how selection has acted across the A(H1N1)pdm09 genome over its period of transition to a seasonal influenza lineage. It is shown that accurate estimates of the evolutionary rate, date of emergence and initial exponential growth rate of the virus can be obtained with high precision from analysis of 100 genome sequences, thereby helping to characterize the virus just 2 months after the first cases were reported. In order to account for variation in growth rates of influenza epidemics between localized outbreaks around the globe, a hierarchical phylogenetic model is employed for analysis of pandemic and seasonal influenza data. The results suggest that the A(H1N1)pdm09 lineage spread more easily and with greater variation between populations during its first pandemic wave than either seasonal influenza lineage in previous seasons. The birth-death epidemiology model has been shown to provide more precise estimates of the basic reproductive number than the coalescent in analysis of HIV epidemic data. Analysis of pandemic influenza data carried out here suggests that the model assumptions are less applicable to influenza and in fact the

13 birth-death epidemiology model loses accuracy more rapidly than coalescent models as data increased during the pandemic. The effects of an increasingly immune global host population over the pandemic and subsequent influenza seasons were investigated using robust counting of substitutions across the genome. Results suggest that antigenic genes were under a greater selective pressure to evolve than internally expressed genes and the rate of non-synonymous substitution was highest across all segments immediately after emergence in the human population. Bayesian phylogenetics is increasingly being employed as an important tool for rapid characterization of novel infectious disease epidemics. As such, the work carried out here aims to determine the accuracy and applicability of existing evolutionary models with pandemic sequence data sampled over a range of temporal and spatial scales to help better inform similar analyses of future epidemics. 13

14 Lay summary The 2009 influenza pandemic was first detected in Mexico in March 2009 and rapidly spread around the globe. Since the virus was very different to the influenza viruses causing epidemics in the human population over the past 60 years, the majority of the global population lacked immunity to the virus. As such, rapid understanding of the evolution and transmission of the pandemic was required in order to help inform interventions and determine the risk posed to public health. Bayesian phylogenetic analysis is frequently employed to help to infer the spread of an epidemic by reconstructing the evolutionary history of viruses sampled from infected individuals. These methods are employed here to investigate how quickly the evolution of the pandemic influenza virus could be accurately and precisely estimated from its genome sequence data and how the virus changed over time. It is shown that the date of emergence, rate of evolution and growth rate of the pandemic can be obtained just 2 months after the first cases were reported to help characterize the epidemic. Subsequent analysis of data sampled from multiple outbreaks of the pandemic in different locations suggests that it spread more easily and with greater variation in mid-2009 than either of the two influenza viruses that had previously circulated. In contrast to analysis of HIV epidemics, changing the methods used to construct the evolutionary history of the epidemic does not improve the accuracy or speed with which it can be characterized. The virus has continued to circulate in winter influenza seasons in many countries. Since immunity is acquired through infection, the virus is expected to have changed over this time in order to evade the immune system and re-infect individuals. Through analysis of the virus over the first two and a half years of its circulation and simulating the process by which the genome sequence changed, it is found that the virus was under pressure to change as immunity increased and also when it first emerged in the human population.

15 Bayesian phylogenetic methods are increasingly being used in infectious disease research and the work carried out here aims to determine their accuracy and applicability to help better inform similar analyses of future epidemics. 15

16 Chapter 1 Introduction Phylogenetic analysis of viral sequence data is becoming an increasingly important and readily adopted tool for revealing the origin and evolution of viral epidemics. Upon the emergence of the 2009 influenza A virus pandemic, rapid sequencing of viruses enabled estimation of some of the evolutionary and epidemiological parameters that helped to characterise the pandemic within two months of the first reported cases (Fraser et al., 2009). In light of the growing application of these methods within molecular epidemiology, the research carried out within this thesis aims to investigate their applicability to pandemic influenza sequence data and discuss the epidemiological and evolutionary inferences that can be made. This chapter provides a background to the biology and epidemiology of influenza A and the 2009 pandemic, in addition to the methods that are employed to analyse the viral sequence data in subsequent chapters. 1.1 The emergence of the 2009 influenza A pandemic In March 2009, Mexican authorities reported a steady increase in cases with respiratory and influenza-like illness (ILI) in several regions of the country (WHO, 2009a). This increase prompted a national alert for increased surveillance of cases. On April , 1

17 two children in southern California were also reported to have severe respiratory illness and testing quickly confirmed infection with a novel human H1N1 influenza A virus of swine origin (CDC, 2009c). Testing of Mexican cases on April 23 revealed that infections were caused by the same strain that was detected in the two influenza cases in the USA (CDC, 2009a). Since an epidemiological link between the cases could not be identified, it was apparent that an epidemic was already underway (CDC, 2009c; CDC, 2009a). Retrospective testing identified 97 patients with laboratory-confirmed infection with the virus, the first of which initially developed symptoms on 17 March (CDC, 2009a). Several characteristics of the virus raised concern regarding its threat to global public health. In contrast to winter influenza seasons, the initial cases of the epidemic in Mexico occurred predominantly in otherwise healthy young adults (Chowell et al., 2009). In addition, the virus had emerged outside of the typical influenza seasons in the Northern hemisphere, when seasonal influenza prevalence in the USA was in fact declining (CDC, 2009c). The unusually young age of cases and out-of-season dynamics were characteristics more typically associated with influenza pandemics (Chowell et al., 2009; Miller et al., 2009). Although sporadic swine-origin influenza cases had been frequently reported in North America over the preceding 10 years, none had demonstrated the capacity to transmit between human hosts (Vincent et al., 2009; Shinde et al., 2009; Newman et al., 2008; Myers et al., 2007). The epidemic rapidly disseminated around the world and reached four continents within three weeks of detection (Hosseini et al., 2010). Due to the sustained and widespread human-to-human transmission of the virus, the World Health Organisation (WHO) classified the epidemic as a Phase 6 pandemic in June 2009, thereby confirming the requirement for rapid public health intervention globally (WHO, 2009d). Following the recommendations of public health bodies, such as the Centres for Disease Control (CDC) and the WHO, many countries increased surveillance efforts in an attempt to limit transmission of the virus. As a consequence, the 2009 pandemic is one of the most 2

18 documented and intensively sampled pandemics to have emerged (Van Kerkhove et al., 2011). A range of intervention measures were employed during the 2009 pandemic to limit the spread of the virus, both nationally and internationally (WHO, 2009b; National Health Service, 2009). Within countries, these included social distancing measures, such as school closures (Chowell et al., 2011c), border closure (Nishiura et al., 2009b) or screening of travellers (Yu et al., 2012a), increased availability of antiviral medication (CDC, 2009e), establishment of public health campaigns to improve sanitation (BBC News, 2009), and increased surveillance of cases (CDC, 2009b). At the global level, the WHO coordinated the development of an effective vaccine, while advising and supporting individual countries in their efforts to contain localised epidemics of the virus (WHO, 2009b). Most countries experienced two pandemic waves, the first occurring between April and late summer and a second between autumn and early In a minority of countries, three waves were reported to have occurred over a similar period (Chowell et al., 2011a; Chen et al., 2013). Incidence decreased in mid-2010 and an end to the pandemic was declared by the WHO in August 2010 (WHO, 2010a). This marked the start of the post-pandemic period, by which time approximately 18,000 deaths had been confirmed globally, although the true figure is estimated to be between around 150,000 and 570,000 (CDC, 2010b; Dawood et al., 2012; WHO, 2010b). Although this estimate is similar to mortality for a typical seasonal influenza epidemic, the difference in the age distribution of deaths greatly increased the number of years of life lost in the pandemic (Viboud et al., 2010; Dawood et al., 2012). In addition, it has been estimated that the majority of deaths from infection with the pandemic virus occurred in low income countries in Africa and South East Asia (Dawood et al., 2012). The 2009 H1N1 influenza A virus (A(H1N1)pdm09) pandemic was the first major infectious disease outbreak for which high-throughput sequencing was carried out in 3

19 real-time. In combination with epidemiological modeling of cases data, analysis of the molecular epidemiology and phylodynamics of the pandemic from virus sequence data enabled rapid characterisation of the virus. Both approaches helped to determine the threat to public health and direct interventions to limit transmission of A(H1N1)pdm09 (Nishiura et al., 2009a; Boëlle et al., 2009; Yang et al., 2009; Smith et al., 2009b; Rambaut and Holmes, 2009; Fraser et al., 2009). The remainder of this chapter discusses some of the common methods employed in epidemiological analysis of influenza and introduces the evolutionary dynamics and molecular biology of influenza A virus epidemics. 1.2 Epidemiological modeling of influenza A virus Seasonal influenza A emerges each winter in temperate regions of the world, resulting in approximately 250,000 to 500,000 deaths each year (WHO, 2012a). Epidemic duration is typically longer in the tropics than in temperate regions of the globe (4 and 6 months respectively) and epidemics gradually increase to a peak around February in the Northern Hemisphere and August in the Southern Hemisphere (Bloom-Feshbach et al., 2013). An individual becomes immune to the virus after infection and therefore the majority of seasonal influenza cases occur in individuals with a weaker immune system, such as those over 65 and under 2 years of age (WHO, 2012a). Influenza A pandemics emerge when an antigenically novel virus with unknown transmission potential and pathogenicity enters the human population. Due to the high genetic divergence of the new virus from previously circulating seasonal influenza viruses, a large majority of individuals are susceptible to infection. In contrast to seasonal epidemics, influenza pandemics are usually characterized by a higher prevalence in young adults, higher rates of transmission, and multiple, short waves outside of typical influenza season (Miller et al., 2009). 4

20 1.2.1 Compartmental epidemic models Compartmental epidemic models are employed to characterise influenza epidemiology during both seasonal epidemics and pandemics in order to inform implementation of effective control interventions. Such models are formalized by a set of differential equations which describe the rates of change between the different compartments or classes of individuals in an epidemic. Influenza epidemics are often modeled with a frequencydependent SIR model, in which these classes represent susceptible (S), infected (I) and recovered (R) individuals and sum to the total size of the population, N (Kermack and McKendrick, 1932). The transmission rate β is the rate at which individuals move from S to I and is a function of the number of contacts per individual and the probability of a contact resulting in an infection. The recovery rate γ is the rate at which infected individuals leave I and enter R. The dynamics of the system are described as follows: ds dt di dt dr dt = βsi N (1.1) = βsi γi N (1.2) = γi (1.3) In an influenza epidemic, the susceptible class includes individuals who do not posses immunity to the circulating strain. Immunity may have been gained through vaccination or previous infection with the same or a closely related virus that provides crossprotecting immunity. The size of the infected class quantifies the prevalence of the epidemic and is often represented by the epidemic curve (shown in Figure 1.1). A closely related parameter is the incidence rate, which describes the number of new infections per unit time. The recovered class includes individuals who recover completely following successful clearance of the infection by the host immune system. The individual is subsequently removed from the system since immunity to influenza is life-long. i.e. the 5

21 virus must adapt in order to evade the host immune response and successfully re-infect the same individual. Alternative compartmental models can be employed to describe different epidemiological dynamics, in which additional classes of individuals are described. For example, the SEIR model includes an exposed class (E) in which individuals are infected but cannot transmit. Influenza is often modeled with an SEIR model in order to account for the short incubation period which lasts approximately 2 days (Chowell et al., 2008a). Compartmental models make various assumptions regarding the epidemic system, including homogenous mixing within the population, constant β and γ across time and uniform susceptibility between individuals. These assumptions make for a simplistic model of influenza epidemiology and therefore more complex models are often used to account for changes in contact rates or transmission (Towers and Chowell, 2012) or interruption of transmission by vaccination (Chowell et al., 2008a). It is also often assumed that the population is closed and birth and death rates remain constant over time. This assumption is made due to the short timescale over which influenza epidemics occur (Chowell et al., 2007) but models including changes in demography are also frequently employed (Rasmussen et al., 2011) The basic reproductive ratio The ease with which an infectious disease spreads through a host population is quantified by the basic reproductive ratio, R 0. It represents the average number of secondary infections arising from a primary infector during their infectious period in a completely susceptible population (Anderson and May, 1991). R 0 is a fundamental parameter in epidemiology since it provides a threshold for determining the fate of an epidemic: if R 0 is less than 1, the number of new infections will decrease with time and the epidemic will eventually die out; conversely, if R 0 is greater than 1, then the epidemic is growing and intervention will be required to slow or stop disease spread. R 0 is therefore a useful 6

22 measure of the risk of an epidemic and its magnitude can help to determine the type and scale of interventions required (Anderson and May, 1991). Effective public health intervention requires rapid and accurate estimation of R 0 during an epidemic (Wallinga and Lipsitch, 2007). As soon as the recovered class increases in size, the population is no longer entirely susceptible and the number of secondary cases per infectious case is defined by the effective reproductive number, R. In contrast to R 0, R accounts for the proportion of individuals in a population that are immune, x, and is equal to R 0 x. Estimates of R 0 are typically around for influenza pandemics (Ferguson et al., 2005) which is considerably lower and less variable than estimates of R 0 for many other RNA virus epidemics, such as measles (5-18), mumps (7-14) (Anderson and May, 1991) and norovirus (5-9) (Heijne et al., 2009). Despite the low R 0 of influenza epidemics, they are usually very large due to the short time between infections means (Mills et al., 2004). A small change in the magnitude of R 0 can dramatically affect the dynamics of an epidemic, as shown in Figure 1.1. This figure shows that an influenza pandemic with a higher R 0 infects more of the population over a shorter time period than a pandemic with a lower R 0. During an influenza epidemic, the number of infected individuals is expected to increase until a sufficiently high proportion of the population is immune to infection to limit onward transmission of the virus to the rest of the population (this effect is termed herd immunity). Since R 0 depends on the infectious period, contact rate with susceptible individuals and probability of infection upon contact, the R 0 for influenza is expected to vary between different host populations (Dietz, 1993). Accounting for this variation can be important for deciding on the public health interventions to deploy and is investigated further in Chapter 3. In estimating R 0, various assumptions are made regarding the dynamics of the epidemic, including constant infectivity and a completely susceptible and homogenous population (Heffernan et al., 2005). Under the SIR model for influenza, R 0 is estimated from the average duration of infectiousness and transmission rate, i.e., R 0 = β/γ. 7

23 Number of individuals Susceptible Infected Recovered Number of individuals Susceptible Infected Recovered Time Time (a) R 0 = 1.4 (b) R 0 = 2.8 Figure 1.1: SIR simulations under the lower (Figure 1.1(a)) and upper (2.8) (Figure 1.1(b)) estimates of R 0 for pandemic influenza in a population of 1000 individuals. The average duration of infectiousness (1/γ) = 4 days (i.e. β = 0.4, γ = 0.25) R 0 can also be estimated from the number of susceptibles at endemic equilibrium, the average age at infection or the final size equation and are discussed in detail in several reviews (Dietz, 1993; Heffernan et al., 2005; Keeling and Grenfell, 2000; Diekmann et al., 1990). However, R 0 for influenza epidemics is commonly estimated from the initial exponential growth rate parameter of the epidemic, r 0. This is defined as the per capita change in the size of the population per unit time i.e. r 0 = β γ. Like R 0, r 0 also behaves as a threshold parameter, since values greater than zero imply epidemic growth while values less than zero suggest that the epidemic is decreasing in size and will eventually die out without requiring any intervention. r 0 is estimated from the exponential growth phase of the epidemic curve, which can be reconstructed from the number of cases over time (Chowell et al., 2011b; Chowell et al., 2008b). During this early period, the proportion of the total population in the susceptible class is almost equal to 1 and therefore equation (1.2) becomes: 8

24 di dt = (β γ)i = r 0I (1.4) and rearranging gives R 0 = r 0 γ + 1 (1.5) where 1/γ is the mean generation interval between two infections (Pybus et al., 2001; Anderson and May, 1991; Ferguson et al., 2005). R 0 for influenza is often estimated from r 0 using the Lotka-Euler equation and prior knowledge of the distribution of times between one individual becoming infected and infecting another individual. Specification of an appropriate distribution is important for accurate estimation of R 0 (Wallinga and Lipsitch, 2007). This distribution of generation times accounts for variation in the times between two infections in the transmission chain (Wallinga and Lipsitch, 2007; Grassly and Fraser, 2008). Equation 2.7 in Wallinga and Lipsitch (2007) gives an expression for estimating R 0 using the Lotka-Euler equation: 1 R 0 = 0 e ra g(a) da (1.6) where a is the mean generation time and g(a) is its distribution (Wallinga and Lipsitch, 2007). The interval between two consecutive influenza infections is drawn from an exponential distribution and is usually defined using the time of onset of symptoms, although it is known that the asymptomatic infectious period is approximately one day (Griffin et al., 2011; Carrat et al., 2008). As such, the sum of generation times across the whole population is often assumed to be gamma distributed g(a), so that equation (1.6) becomes: R 0 = (1 + rθ) k (1.7) 9

25 where k = µ 2 /σ 2 and θ = σ 2 /µ. Wallinga and Lipsitch (2007); Carrat et al. (2008); Grassly and Fraser (2008) have estimated mean generation time distributions from viral shedding curves for A(H1N1) and A(H3N2) seasonal influenza lineages with µ = 2.3 days ( ) and 3.1 days ( ) respectively. Cauchemez et al. (2009) used household transmission data to estimate a mean generation time distribution for A(H1N1)pdm09 influenza with µ = 2.6 and σ = 1.3. Estimation of R 0 from r 0 is useful for systems in which the infected population forms a single, discrete class. The next-generation operator method described by Diekmann et al. (2010) has recently been used to estimate R 0 for localised epidemics of A(H1N1)pdm09 in different countries (Opatowski et al., 2011). This approach is applicable when the infected class can be divided into further sub-classes, such as populations that are structured geographically or by age. Transmission events between different locations or age groups are treated differently in this model. The next generation matrix, G, describes the average number of secondary infections in individuals of type i by an individual of type j, when the population of type i is completely susceptible. As such, each type of infection has a specific R 0, which is termed R ij. An estimate of R 0 for the whole population is then identified as the dominant eigenvalue of G. In some cases, it may be more convenient to describe the growth of an epidemic using the doubling time, which is approximately equal to ln(2)/r 0. The doubling time can also be used to quantify the effect of intervention measures (i.e. attempts to increase the doubling time) and, unlike R 0, does not require accurate knowledge of the distribution of generation times (Black et al., 2013). 10

26 1.3 Ecology of influenza A virus Pandemic influenza Influenza A pandemics occur when a new influenza virus emerges in the global human population to which there is little or no immunity. This can happen when segments of the influenza genome are exchanged between different influenza virus lineages upon co-infection of a single host cell during a reassortment event. The resulting change in antigenicity of the virus is called an "antigenic shift". For example, A(H1N1)pdm09 is thought to have emerged from a swine population, in which viruses from a North American swine influenza lineage and Eurasian avian-like swine influenza lineage reassorted, giving rise to an antigenically novel virus (Smith et al., 2009b). Although avian hosts represent the major reservoir for influenza, swine are often cited as a common breeding ground for novel influenza viruses. The presence of α-2,6- and α-2,3-linked sialic acid receptors in their respiratory tract enables infection with viruses from both mammal and avian hosts respectively, thereby facilitating subsequent reassortment between them (Scholtissek et al., 1985; Ito et al., 1998). An influenza pandemic can also occur in the absence of reassortment, after a virus crosses the species barrier from the avian population directly. It is through this route that the 1918 pandemic is thought to have entered in the human population, followed by adaptation of the virus to its new mammalian host (Taubenberger et al., 2005a). Although reports of influenza pandemics date back to the 16th century (reviewed by Taubenberger and Morens (2009)), four pandemic lineages have emerged since the start of the 20th century and each has proceeded to replace the previously circulating lineage during subsequent influenza seasons. The most fatal of these was the 1918 ("Spanish influenza") H1N1 pandemic, which is estimated to have caused million deaths (Johnson and Mueller, 2002). R 0 for the 1918 pandemic has been estimated to be between , suggesting that although the virus was exceptionally virulent, 11

27 it was only moderately transmissible (White and Pagano, 2008; Ferguson et al., 2005; Chowell et al., 2010a; Fraser et al., 2011). The 1957 ("Asian influenza") H2N2 virus emerged after reassortment between the existing human H1N1 influenza lineage and an avian H2N2 lineage. Mortality was lower than during the 1918 pandemic ( 1-4 million (WHO, 2009b)) with an R 0 of 1.7 (Longini et al., 2004). Mortality and transmissibility were similar in the following H3N2 pandemic in 1968 ("Hong Kong influenza") (WHO, 2009b). This lineage has persisted in the human population since its emergence and currently circulates as a seasonal lineage. In addition to pandemic influenza, some inter-pandemic seasons have caused excessive mortality. The emergence of an H1N1 virus in 1977 had several hallmarks of a novel pandemic lineage (for example, a higher prevalence in young adults and widespread infection). However, it was later shown to be a re-emergence of the H1N1 seasonal virus which circulated prior to the emergence of H2N2 in Since it lacked the evolutionary change expected at the nucleotide level over the 20-year period since its previous appearance, it is believed that the virus was frozen in storage until it entered the population in 1977 (Nakajima et al., 1978; Scholtissek et al., 1978) Seasonal influenza All of the pandemic lineages described in section went on to circulate in global influenza seasonal epidemics each year, rapidly adapting to evade the increasing immunity of the host population. Seasonal influenza epidemics occur during the winter months in temperate countries in both Northern (October-March) and Southern (April-September) hemispheres, although some influenza transmission is known to occur outside of these seasons (Nelson et al., 2012; Ghedin et al., 2010). The forces affecting the timing and duration of seasonal epidemics are not currently fully understood, although climatic factors such as temperature (Prel et al., 2009) and humidity (Shaman and Kohn, 2009; Shaman et al., 2010), in addition to the susceptibility and contact patterns of the host 12

28 population (Lipsitch and Viboud, 2009) are thought to contribute. There is substantial phylogenetic support for the external seeding of strains in seasonal epidemics from the tropics, where year-round influenza transmission is observed (Nelson et al., 2006; Nelson et al., 2007; Russell et al., 2008; Viboud et al., 2006a). As such, the global circulation of influenza strains is thought to represent a source-sink model but with high transmission between temperate countries, which gives rise to a complex global transmission network (Rambaut et al., 2008; Bedford et al., 2010; Bahl et al., 2011). Prior to the emergence of A(H1N1)pdm09, the A(H1N1) and A(H3N2) lineages cocirculated during influenza seasons since 1978, with one lineage typically dominating over the other (Holmes, 2010). This pattern is thought to be driven by periods of stasis in the A(H3N2) population, in which diversity of the hemagglutinin surface antigen gene is sufficiently high to prevent a single lineage prevailing and enabling the A(H1N1) lineage to dominate (Wolf et al., 2006; Tom et al., 2012). The evolutionary rate across seasons has been shown to be lower in A(H1N1) viruses, while H3N2 viruses exhibit lower genetic diversity and limited antigenic diversity (Nelson et al., 2007; Bedford et al., 2012). Additionally, prevalence is usually higher for the A(H3N2) lineage than A(H1N1) (Viboud et al., 2006a). The evolution of H3N2 is characterised by ongoing substitution with phenotypic changes in the antigenic epitopes every 2-8 years (Smith et al., 2004; Wolf et al., 2006; Koelle et al., 2006). This corresponds well with the pattern of waning immunity which lasts between 3 and 8 years (Truscott et al., 2012). Phylogenetic analysis of the hemagglutinin gene in A(H3N2) reveals a cactus-like tree, in which the backbone was initially thought to represent the advantageous mutations while side branches die out due to an absence of mutations that confer resistance to the immune system (Bush et al., 1999; Fitch et al., 1997; Ferguson et al., 2003). This topology may in fact indicate a population bottleneck and relatively weak positive selection (Nelson and Holmes, 2007). Whole-genome analysis has revealed that several influenza lineages co-circulate within a location, presenting the opportunity for reassortment 13

29 between strains (Holmes et al., 2005). Differences between genetic and antigenic evolution of H3N2 have been identified through the application of antigenic cartography and helped to determine the most suitable candidates for inclusion in the influenza vaccine (Smith et al., 2004). Subsequent modelling of antigenic data has helped to reconcile the high evolutionary rate of H3N2 with its limited genetic diversity (Bedford et al., 2012). Since the 2009 influenza pandemic, seasonal A(H1N1) virus has mostly been replaced by A(H1N1)pdm09, which has continued to circulate with A(H3N2) in influenza seasons (Blyth et al., 2010a). Although such replacement of a previously circulating lineage is common after a pandemic, it is currently unknown how the two lineages will dominate over future seasons. Due to the difference in the length of time each lineage has circulated in the human population, selection pressures acting on both lineages are expected to differ between them. Differences in selection pressures are investigated further in Chapter 5, in a comparison between the evolution of A(H1N1)pdm09 and A(H3N2) over the pandemic waves and post-pandemic seasons. 1.4 Molecular biology of influenza A virus Genome structure of influenza A virus Influenza A virus is a negative sense, single stranded RNA virus of the Orthomyxoviridae family. The 13.6 kb genome comprises 8 segments encoding 11 genes (Palese and Schulman, 1976; Ritchey et al., 1976; Palese et al., 1977). Hemagglutinin (HA) and neuraminidase (NA) genes encode the two membrane-bound surface antigens that interact with the host immune system and the antigenicity of these proteins are used to subtype the virus. Nine NA (N1-N9) and 17 HA (H1-H17) subtypes been identified in the avian influenza reservoir, although only H1, 2 and 3 have successfully emerged to cause pandemics in human populations. Sporadic cases of other subtypes continue to enter the human population following a zoonotic event, and range from mild to fatal 14

30 in severity, although none have demonstrated the ability to transmit between humans (Koopmans et al., 2004). As such, pandemic preparedness involves continued surveillance of cases to quickly identify any novel subtypes demonstrating pandemic potential through sustained human-to-human transmissibility (WHO, 2011). The remaining 9 genes encode internally expressed proteins. The polymerase genes (PB1, PB2 and PA) collectively code for the RNA polymerase complex that is responsible for transcription and replication of the viral RNA. Species-specific substitutions at particular sites in PB2 are essential for adaptation of an avian influenza virus to a mammalian host (Subbarao et al., 1993; Taubenberger et al., 2005b). In their controversial paper describing the generation of a transmissible highly pathogenic H5N1 influenza A virus, Herfst et al. (2012) showed that two substitutions in PB2, in addition to four in HA, were present in all viruses capable of airborne transmission between ferrets. The nucleoprotein (NP) encodes a structural protein that encapsidates the virus genome prior to RNA replication and interacts with many of the host proteins, making it particularly important in host specificity and is also a major target of the host cytotoxic T-cell response (Webster et al., 1992; Portela and Digard, 2002). The matrix protein is encoded by two genes, M1 and M2, which are involved in viral budding and replication respectively (Noton et al., 2007). The matrix protein is thought to have played an important role in increasing the transmissibility of A(H1N1)pdm09 relative to ancestral viruses that had previously emerged only sporadically in humans (Chou et al., 2011). The shortest segment of the influenza genome encodes the two non-structural proteins (NS1 and NS2). While NS1 is involved in RNA transport, splicing and translation, NS2 is thought to modulate the export of nucleic acids from the nucleus (O Neill et al., 1998). 15

31 1.4.2 The origin of the A(H1N1)pdm09 virus Upon emergence of A(H1N1)pdm09, it was quickly apparent that the virus originated from multiple genetic origins (Garten et al., 2009; Dawood et al., 2009; Trifonov et al., 2009). Phylogenetic analysis showed that it was generated through a reassortment event in which the polymerase genes, HA, NP and NS originated from a triple-reassortant H3N2 virus that had circulated in North America swine since at least 1998, and NA and MP from a Eurasian avian-like swine H1N1 virus that had previously not been detected in North America swine (Smith et al., 2009b). The triple-reassortant H3N2 virus emerged in swine when a reassortment event brought together segments from human H3N2, classical swine H1N1 and North American avian viruses (Smith et al., 2009b). Analysis of multiple avian, human and swine influenza lineages suggest that a virus ancestral to A(H1N1)pdm09 was geographically widespread and circulated undetected in swine for at least 10 years before it emerged in the human population, much like the 1918, 1957 and 1968 pandemic lineages (Smith et al., 2009a). The authors also found that reassortment between the major lineages circulating in swine was common over this period (Smith et al., 2009b). Extensive research has also been carried out to identify mutations across the genome that demonstrate the potential to confer increased transmissibility or pathogenicity (Glinsky, 2010; Ikonen et al., 2010b; Kilander et al., 2010; Hurt et al., 2011). The fate of many such mutations and the evolutionary pressures acting on each influenza segment are investigated further in Chapter 5. 16

32 1.5 Molecular epidemiology using Bayesian phylogenetics Many RNA viruses have very high mutation rates due to a high replication rate and error-prone RNA polymerase. For example, the evolutionary rate of influenza A varies across genes, but is estimated to be around 3 x 10 3 substitutions/site/year (Nelson et al., 2006). Such high evolutionary rates makes it possible to measure the evolution of RNA viruses over an epidemiological time frame. Genetic diversity can accumulate so quickly that measurable change can be detected between viruses sampled only weeks apart during an epidemic (Drummond et al., 2003). Phylodynamics describes the framework within which phylogenetic analysis of measurably evolving populations provides inference regarding host immunity and epidemic dynamics (Grenfell et al., 2004). This has been facilitated by rapid advances in high-throughput sequencing and phylogenetic and statistical modeling (Pybus et al., 2013). Analysis of viral genetic sequence data has offered extensive, additional insight into the origins, evolution and epidemiology of novel viruses, such as the 2005 SARS epidemic (Vijaykrishna et al., 2007), 2009 A(H1N1)pdm09 pandemic (Fraser et al., 2009; Smith et al., 2009b) and most recently the 2013 H7N9 epidemic (Cotten et al., 2013; Lam et al., 2013). Molecular epidemiology uses genetic sequence data of the pathogen to make inferences about the spread of an infectious disease and is not affected by many of the sampling issues that hinder epidemiological analysis of case data. Although analyses have focused on rapidly evolving RNA viruses, phylodynamic methods are now being employed in a wider range of infectious disease organisms, including bacteria (Harris et al., 2010; McAdam et al., 2012) and DNA viruses (Kerr et al., 2012). Bayesian phylogenetics lends itself to the analysis of viral infectious diseases for several reasons. During an epidemic, sequence data are often sparsely sampled, owing to large population sizes and incomplete sampling. Such datasets satisfy several 17

33 assumptions of the coalescent, which reconstructs the past population dynamics of the virus population (Pybus and Rambaut, 2009). Virus sequences are usually sampled over multiple time-points during an epidemic and also over the course of some infections, like HIV (Shankarappa et al., 1999; Lemey et al., 2007). Due to the rapid accumulation of mutations between sampling times, virus sequence data can be used to construct time-calibrated phylogenetic trees. Bayesian phylogenetics provides a statistical framework in which to estimate the probabilities of various evolutionary and epidemiological models, from both prior expectations and sequence data. These methods also provide a measure of uncertainty regarding the estimated genealogy and evolutionary parameters. Markov chain Monte Carlo (MCMC) simulation is employed to sample the distribution of possible trees, thereby increasing the computational efficiency and enabling analysis of large whole-genome datasets on a practical timescale. BEAST (Bayesian Evolutionary Analysis by Sampling Trees) is a software package that performs Bayesian phylogenetic analysis of temporally sampled sequence data, estimating time calibrated phylogenetic trees (Drummond and Rambaut, 2007; Drummond et al., 2012). BEAST employs a range of models for nucleotide and protein substitution, molecular clock variation, demographic history, spatial distribution and phenotypic trait evolution and has therefore been used extensively in analyses of viral evolution (for example Fraser et al. (2009); Lemey et al. (2009b); Bahl et al. (2011); Lycett et al. (2012); Nelson et al. (2012)). Phylogenetic analysis of influenza genome sequences in BEAST forms the core component of Chapters 2-5 and therefore a background to the methods is provided in the following sections Bayesian phylogenetics Bayes Theorem provides a framework for updating prior hypotheses about the evolution of a population, in light of the observed data. The posterior probability distribution for the evolutionary model given the data, p(m D), is calculated as a function of the 18