The emergence of novel influenza A viral lineages to which there is limited or no immunity remains an on-going concern for human and animal public health. At least three of the pandemics reported in human hosts during the 20th and 21st century are believed to have emerged as a result of reassortment between human, avian and swine viruses circulating at the time. The emergence of novel lineages with pandemic potential is frequently believed to occur as a consequence of reassortment. The ability to predict when and how the next novel lineage with pandemic potential will arise remains challenging. Additionally, the dynamics of infection in systems where multiple subtypes are known to circulate remain poorly understood. Gaining insight into these biological processes is of vital importance if we are to understand how much co-infection is occurring, and hence the opportunity for reassortment. This thesis uses data analysis, novel algorithmic methods applied to genomic data and mathematical modelling to develop an understating of the dynamics of influenza A infection within non-human hosts. It seeks to analyse patterns of reported reassortment in influenza A across all hosts, geographic regions and time periods. Chapters 3 and Chapter 4 use data on reported reassortment events to identify additional, previously unreported suspected reassortant viral lineages, and develop a quantitative definition of significant reassortants. These chapters use publicly available sequence data to understand to what extent published data on reassortants reflects the possible extent of reassortment that can be detected with an iterative clustering algorithm. They develop methods that can be used to assign a rank score to suggest whether an individual isolate is a suspected reassortant. Methods presented in these chapters are applied to all publicly available influenza A sequence data from two different databases. The estimation of epidemiological parameters such as R0 and the time of virus introduction into a population (Ts) are of vital importance to infer in the event of an outbreak of a novel influenza virus n a poultry production facility. This thesis assesses the performance of different surveillance strategies to estimate R0 and Ts using simulated surveillance data from an outbreak of H7N9 in commercial poultry. Since the detection of novel H7N9, it has been isolated from numerous live bird markets. Live birds markets are known to harbour diverse subtypes of avian influenza within numerous different susceptible hosts. Co-infection with two different influenza viruses is a prerequisite for reassortment to occur therefore the final results chapter explores the conditions required to generate the multi-strain dynamics of different AI subtypes and the resulting co-infection dynamics that emerge. Taken together, through these topics addressed in this thesis we understand more broadly the possible true extent of reassortant detectable in publicly available sequence data. How surveillance data collection can be optimised to help estimate epidemiological parameters in the event of an outbreak, and the possible mechanisms that generate the multi-strain dynamics within a live bird market.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:724120 |
| Date | January 2015 |
| Creators | Pinsent, Amy |
| Contributors | Riley, Steven ; Ferguson, Neil |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/51494 |
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