Exploring the phylodynamics, genetic reassortment and RNA secondary structure formation patterns of orthomyxoviruses by comparative sequence analysis

RNA viruses are among the most virulent microorganisms that threaten the health of humans and livestock. Among the most socio-economically important of the known RNA viruses are those found in the family Orthomyxovirus. In this era of rapid low-cost genome sequencing and advancements in computational biology techniques, many previously difficult research questions relating to the molecular epidemiology and evolutionary dynamics of these viruses can now be answered with ease. Using sequence data together with associated meta-data, in chapter two of this dissertation I tested the hypothesis that the Influenza A/H1N1 2009 pandemic virus was introduced multiple times into Africa, and subsequently dispersed heterogeneously across the continent. I further tested to what degree factors such as road distances and air travel distances impacted the observed pattern of spread of this virus in Africa using a generalised linear modelbased approach. The results suggested that their were multiple simultaneous introductions of 2009 pandemic A/H1N1 into Africa, and geographical distance and human mobility through air travel played an important role towards dissemination. In chapter three, I set out to test two hypotheses: (1) that there is no difference in the frequency of reassortments among the segments that constitute influenza virus genomes; and (2) that there is epochal temporal reassortment among influenza viruses and that all geographical regions are equally likely sources of epidemiologically important influenza virus reassortant lineages. The findings suggested that surface segments are more frequently exchanges than internal genes and that North America/Asia, Oceania, and Asia could be the most likely source locations for reassortant Influenza A, B and C virus lineages respectively. In chapter four of this thesis, I explored the formation of RNA secondary structures within the genomes of orthomyxoviruses belonging to five genera: Influenza A, B and C, Infectious Salmon Anaemia Virus and Thogotovirus using in silico RNA folding predictions and additional molecular evolution and phylogenetic tests to show that structured regions may be biologically functional. The presence of some conserved structures across the five genera is likely a reflection of the biological importance of these structures, warranting further investigation regarding their role in the evolution and possible development of antiviral resistance. The studies herein demonstrate that pathogen genomics-based analytical approaches are useful both for understanding the mechanisms that drive the evolution and spread of rapidly evolving viral pathogens such as orthomyxoviruses, and for illuminating how these approaches could be leveraged to improve the management of these pathogens

Seasonal influenza : modelling approaches to capture immunity propagation

Seasonal influenza poses serious problems for global public health, being a significant contributor to morbidity and mortality. In England, there has been a long-standing national vaccination programme, with vaccination of at-risk groups and children offering partial protection against infection. Transmission models have been a fundamental component of analysis, informing the efficient use of limited resources. However, these models generally treat each season and each strain circulating within that season in isolation. Here, we amalgamate multiple data sources to calibrate a susceptible-latent-infected-recovered type transmission model for seasonal influenza, incorporating the four main strains and mechanisms linking prior season epidemiological outcomes to immunity at the beginning of the following season. Data pertaining to nine influenza seasons, starting with the 2009/10 season, informed our estimates for epidemiological processes, virological sample positivity, vaccine uptake and efficacy attributes, and general practitioner influenza-like-illness consultations as reported by the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). We performed parameter inference via approximate Bayesian computation to assess strain transmissibility, dependence of present season influenza immunity on prior protection, and variability in the influenza case ascertainment across seasons. This produced reasonable agreement between model and data on the annual strain composition. Parameter fits indicated that the propagation of immunity from one season to the next is weaker if vaccine derived, compared to natural immunity from infection. Projecting the dynamics forward in time suggests that while historic immunity plays an important role in determining annual strain composition, the variability in vaccine efficacy hampers our ability to make long-term predictions

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Geographic and demographic transmission patterns of the 2009 A/H1N1 influenza pandemic in the United States

This thesis describes how transmission of the 2009 A/H1N1 influenza pandemic in the United States varied geographically, with emphasis on population distribution and age structure. This is made possible by the availability of medical claims records maintained in the private sector that capture the weekly incidence of influenza-like illness in 834 US cities. First, a probabilistic method is developed to infer each city's outbreak onset time. This reveals a clear wave-like pattern of transmission originating in the south-eastern US. Then, a mechanistic mathematical model is constructed to describe the between-city transmission of the epidemic. A model selection procedure reveals that transmission to a city is modulated by its population size, surrounding population density, and possibly by students mixing in schools. Geographic variation in transmissibility is explored further by nesting a latent Gaussian process within the mechanistic transmission model, revealing a possible region of elevated transmissibility in the south-eastern US. Then, using the mechanistic model and a probabilistic back-tracing procedure, the geographic introduction sites (the `transmission hubs') of the outbreak are identified. The transmission hubs of the 2009 pandemic were generally mid-sized cities, contrasting with the conventional perspective that major outbreaks should start in large population centres with high international connectivity. Transmission is traced forward from these hubs to identify `basins of infection', or regions where outbreaks can be attributed with high probability to a particular hub. The city-level influenza data is also separated into 12 age categories. Techniques adapted from signal processing reveal that school-aged children may have been key drivers of the epidemic. Finally, to provide a point of comparison, the procedures described above are applied to the 2003-04 and 2007-08 seasonal influenza outbreaks. Since the 2007-08 outbreak featured three antigenically distinct strains of influenza, it is possible to identify which antigenic strains may have been responsible for infecting each transmission hub. These strains are identified using a probabilistic model that is joined with the geographic transmission model, providing a link between population dynamics and molecular surveillance.Gates Cambridge scholarshi

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Integrated analysis of epidemiological and phylogenetic data to elucidate viral transmission dynamics

While infectious disease outbreaks are often summarised by population averages such as the reproductive number, variation between individuals in terms of onwards transmissions modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. This heterogeneity among individuals can be quantified by the dispersion parameter k of the offspring distribution, a distribution that defines the number of secondary infections per infected individual. I have developed an inference framework to estimate k and other epidemiological parameters by fitting stochastic transmission models to both incidence time series and the pathogen phylogeny. Applying the framework to simulated data, I found that more accurate, less biased and more precise estimates of the reproductive number and k were obtained by combining epidemiologic and phylogenetic analyses. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accurate estimation of epidemic start date and the probability of sampling an infection. I further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. In addition to methodological contributions, I found that the inclusion of sequences in statistical inference for polio improved the precision of parameter estimates. Based on sequences collected from patients during a poliovirus outbreak, the estimated values of k were high regardless of the data used. On the other hand, the k estimates were low when a transmission model was fit to environmental sequences collected in Pakistan, which is still endemic for wild poliovirus. Furthermore, analysis of environmental sequences was informative of seasonality parameters whereas inference from incidence time series alone was not. This type of analysis using environmental sequences would be useful as polio eradication draws to a close as the number of symptomatic cases approaches zero.Open Acces

Quantifying the Effects of Measures to Control Highly Pathogenic Avian Influenza H5N1 in Poultry in Southeast Asia

Despite the ongoing efforts to contain its spread, H5N1 is now considered endemic within poultry in various settings worldwide, threatening both the livelihoods of those involved in poultry production in affected countries and posing a continuous public health risk. The reasons for the varying levels of success in controlling H5N1 in Southeast Asia need to be better understood. In this thesis, various different methods of quantifying the effects of individual control measures, using the types of data available in various different contexts, are discussed and applied. In the first half of this thesis a spatio-temporal survival model is fitted to H5N1 outbreak surveillance data from Vietnam and Thailand using a Bayesian framework in order to account for unobserved infection times. Following vaccination in Vietnam it was found that transmissibility had been successfully reduced but, during a wave of outbreaks in 2007, that this coincided with a reduction in the rate of at which outbreaks were reported following the introduction of infection, limiting the overall impact this reduction in transmissibility had on the total epidemic size. In Thailand, active surveillance was found to be successful in contributing to the control of infection. Furthermore, backyard producers, whilst responsible for the majority of outbreaks, were, on average, less likely to transmit infection than those involved in more intensive production. In the second half of the thesis, the use of final size methods to assess the effectiveness of vaccination from trial data is explored. This involved an investigation into the effects of different assumptions regarding the action by which vaccination confers immunity and fitting estimates of transmissibility to data collected from outbreak investigations in the context of a field trial of vaccination in Indonesia, where, making strong assumptions about the underlying infection process, a reduction in both within and between flock transmissibility was detected for outbreaks occurring in areas where vaccination was being carried out

Spatial Dynamics of the Severe Acute Respiratory Syndrome (SARS) Epidemic in Hong Kong in 2003

The Severe Acute Respiratory Syndrome (SARS) epidemic in 2003 was the first infectious disease outbreak caused by a novel pathogen in the twenty-first century. The outbreak in Hong Kong was the second largest worldwide and was characterised by a large proportion of hospital infections and a super-spreading event caused by environmental factors in residential buildings. Hospitals treating SARS cases were at high risk for transmission. I found that hospital outbreaks triggered community transmission as well as the formation of spatial clusters of community cases. The size of the community outbreak in an area increased with the size of the outbreak in the nearest hospital treating SARS, and an area was more likely to have no community-infected cases if it was far from hospitals treating SARS, or had less hospital-infected cases within the area. To quantify the transmission between hospital and community, I developed a spatial epidemic-tree-reconstruction method that uses gravity models to spatially define the probability of contact between individuals in the community. From the reconstructed probabilistic infection tree, I estimated that 24% of community transmission was likely to be infected by cases infected in hospitals, with infected patients discharged during their incubation period and hospital visitors the most important drivers of transmission from healthcare settings to the community. Healthcare workers were key drivers of hospital transmission, with the hospital-to-hospital reproduction number, excluding a single hospital super-spreading event, estimated to be 0.8. A typical community-acquired case was estimated to generate 0.6 cases in the community and 0.2 cases in the hospital in which they were subsequently hospitalised. My findings suggest that hospital infection control could be improved. Restricted hospital visitor policies could have been imposed for longer time during the outbreak and quarantine could be considered for those who recently visited or have been discharged from hospitals treating SARS cases

Modelling the Epidemiological Dynamics of Seasonal Influenza Viruses at Local Scales

Seasonal influenza viruses are a substantial source of disease burden globally, causing epidemics across all climatic regions. Through error-prone RNA replication, influenza viruses can escape pre-existing humoral immunity and reinfect humans, resulting in recurrent epidemics within populations. From year to year, individual epidemics differ substantially in timing, duration and size. Despite intensive study, characterising the spatiotemporal patterns of virus circulation and identifying the underlying sources of this variability at global, regional and local scales remain as ongoing challenges. There is a need to reconcile environmental, virological and host drivers of virus epidemiological dynamics across diverse contexts. Such insights can only be generated through a holistic approach that integrates observational, ecological, experimental and modelling studies: this would enable more accurate and timely epidemiological forecasts and more efficient allocation of public health resources. In this thesis, I investigate the phylodynamical interactions between the seasonal influenza virus, environment and human host population, integrating analyses from observational study and theoretical modelling approaches. The current knowledge gap on the drivers of local city-level epidemics is identified in Chapter 2 and subsequently addressed over 4 research chapters. In Chapter 3, I review existing epidemic detection algorithms and present a novel statistical model that I developed for use with noisy disease surveillance data and is optimised for the context of seasonal influenza. In Chapter 4, I apply this novel algorithm and analyse a 15-year dataset of 18,250 typed, subtyped, and antigenically characterised seasonal influenza viruses from the five most populous cities in Australia. With the necessary geographical and virus resolution, I quantify the effects of previously hypothesised environmental and virological factors. Most surprisingly, despite an apparent lack of marked change in virus antigenicity, individual antigenic variants are capable of reinvading the same population over consecutive seasons, which runs contrary to predictions made by existing mathematical models. In Chapters 5 and 6, I investigate how antigenic variants are capable of causing recurrent epidemics at local scales by building upon previous theoretical modelling studies and developing a modelling framework to investigate the interactions between and joint effects exerted by the topology of cross-immunity and host contact structure within a population. In Chapter 5, I investigate the effects of correlations between network structure and individual susceptibility. In Chapter 6, I examine the population-level significance of age-specific changes to an individual's immune response. In Chapter 7, I review my findings and discuss how these new insights into virus ecology can open new avenues for better influenza control and future research

Revealing the evolutionary history and epidemiological dynamics of emerging RNA viral pathogens

Fast-evolving RNA viruses are a leading cause of morbidity and mortality among human and animal populations, contributing significantly to both global health and economic burden. The advent and revolution of high-throughput sequencing has empowered phylogenetic analyses with increasing amounts of temporally and spatially sampled viral data. Moreover, the parallel advancement in molecular evolution and phylogenetic methods has provided investigators with a unique opportunity to gain detailed insight into the evolutionary and epidemiological dynamics of emerging viral pathogens. Using state-of-the-art statistical approaches, this thesis addresses some of the important but controversial questions in viral emergence. Chapter 2 introduces a new framework to quantify and investigate reassortment events in influenza A viruses. By developing a computationally efficient algorithm to calculate the largest common subtree for a pair of tree sets, which are estimated from diffe rent parts of the genome for the same taxa set, the level of phylogenetic incongruency due to reassortment can be appropriately ascertained. Chapters 3, 4 and 5 investigate the evolutionary origins of three diff erent viruses: the novel emergence and cross-species transmission of SARSCoV, the genesis and dissemination of the unique HCV circulating recombinant form, and the ancient divergence of all influenza viruses, respectively. Moreover, Chapter 4 presents an improved statistical framework, which provides more precise evolutionary estimates, by utilizing the hierarchical bayes approach to investigate recombination events in emerging RNA viruses. The last empirical study, presented in Chapter 6, applies the recently developed Bayesian phylogeography models to a large viral sequence dataset sampled from southern Viet Nam to examine the fine-scale spatiotemporal dynamics of endemic dengue in Southeast Asia. The work presented here reflects both the advancements made in sequencing technology and statistical phylogenetics, along with some of the challenges that remain in studying the emergence of fast-evolving RNA viruses. This thesis proposes new and improved solutions to these evolutionary problems, such as incorporating non-vertical evolution (i.e. homologous recombination and reassortment) into the phylodynamic framework, with the aim of facilitating future investigations of emerging viral diseases