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Quantitative approaches to informing the surveillance and control of avian influenza in British poultry

Nickbakhsh, Sema (2012) Quantitative approaches to informing the surveillance and control of avian influenza in British poultry. PhD thesis, University of Glasgow.

Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available in this service.

Abstract

The continued endemic circulation of avian influenza (AI) virus across many parts of the world, as well as the presence of a wild bird reservoir, maintains the threat of incursion of this virus into currently unaffected countries. Major concerns for human health, particularly with respect to the highly pathogenic (HP) H5N1 subtype, as well as the impact on governments' and poultry industries, makes the control of AI an important goal for countries worldwide. Chapter 1 reviews the past, present and future epidemiology of AI in Great Britain (GB), where to date recorded outbreaks have been controlled by stamping out control measures. The potential for large outbreaks under certain conditions is recognised and therefore contingency plans are necessary to limit the potential impact of future incursions. Knowledge of heterogeneity in AI transmission dynamics could be particularly helpful in designing more targeted control measures. In the absence of data to inform the likely mechanisms of between-farm spread, modelling can be a valuable tool to achieve this. This thesis aims to critically assess the appropriate use of existing data for developing epidemiological tools. In contrast to previous studies, this work focuses on epidemiological risks, as well as the dynamics of transmission, at different scales of the British poultry industry. Both airborne and fomite transmission are considered possible mechanisms of between-farm spread of AI within GB, although their relative importance is not well understood. In Chapter 2, epidemiologically-relevant between-farm associations were used to inform an individual-based stochastic network model to explore the geographical variation in airborne versus fomite-mediated transmission predominance. In Chapter 3, the limitation of these findings, by the likely over-estimation of contact frequency, as well as the biased picture of network properties resulting from targeted sampling, were assessed. Nevertheless, these data provide an insight into the complexity of connectivity within the GB poultry network, with implications for resource distribution during outbreak control. Chapter 4 considers the reduction in transmission risk due to company integration and the implications of this in relation to compartmentalisation. Using a deterministic metapopulation framework specific to GB, outbreak conditions posing a risk for HPAI transmission under compartmentalisation were identified. In Chapter 5, cross-population scale interactions were considered through incorporating temporally explicit movement data from one catching company with a within-flock model of HPAI transmission. Important insight into the impact of within-group dynamics on the opportunity for spread at the population-level was gained; in particular, transmission mode assumptions were found to complicate predictions that can otherwise be based on knowledge of flock size. Chapter 6 describes, more generally, how these findings imply that the different sources of information that describe the GB poultry industry can be used to inform different aspects of risk heterogeneity and the targeting of disease control. However, the more informative these data were of population dynamics at the resolution of an individual farm, the less representative they became at a national-level. Further work on the relative importance of farm-level transmission dynamics and network structure could help to establish how vital knowledge at the scale of the individual farm is for informing predictive mathematical models of AI outbreaks. As the opportunity for AI propagation hinges on the rapid detection and notification of an outbreak, further work focusing on the transmission potential of low pathogenic strains in particular is warranted.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Avian influenza, commercial poultry, epidemiology, group-level transmission, population-level transmission, mathematical modelling, statistical modelling, network analysis
Subjects: S Agriculture > SF Animal culture > SF600 Veterinary Medicine
S Agriculture > S Agriculture (General)
Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Veterinary Medicine
Supervisor's Name: Kao, Prof. Rowland and Matthews, Dr. Louise
Date of Award: 2012
Embargo Date: 15 August 2015
Depositing User: Miss Sema Nickbakhsh
Unique ID: glathesis:2012-3518
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 15 Aug 2012
Last Modified: 10 Dec 2012 14:08
URI: http://theses.gla.ac.uk/id/eprint/3518

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