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Title: Phylodynamic modelling of foot-and-mouth disease virus sequence data
Author: Di Nardo, Antonello
ISNI:       0000 0004 5989 2197
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2016
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The under-reporting of cases of infectious diseases is a substantial impediment to the control and management of infectious diseases in both epidemic and endemic contexts. Information about infectious disease dynamics can be recovered from sequence data using time-varying coalescent approaches, and phylodynamic models have been developed in order to reconstruct demographic changes of the numbers of infected hosts through time. In this study I have demonstrated the general concordance between empirically observed epidemiological incidence data and viral demography inferred through analysis of foot-and-mouth disease virus VP1 coding sequences belonging to the CATHAY topotype over large temporal and spatial scales. However a more precise and robust relationship between the effective population size (Ne) of a virus population and the number of infected hosts (or 'host units') (N) has proven elusive. The detailed epidemiological data from the exhaustively-sampled UK 2001 foot-and-mouth (FMD) epidemic combined with extensive amounts of whole genome sequence data from viral isolates from infected premises presents an excellent opportunity to study this relationship in more detail. Using a combination of real and simulated data from the outbreak I explored the relationship between Ne, as estimated through a Bayesian skyline analysis, and the empirical number of infected cases. I investigated the nature of this scaling defining prevalence according to different possible timings of FMD disease progression, and attempting to account for complex variability in the population structure. I demonstrated that the variability in the number of secondary cases per primary infection Rt and the population structure greatly impact on effective scaling of Ne. I further explored how the demographic signal carried by sequence data becomes imprecise and weaker when reducing the number of samples are described, including how the extent of the size and structure of the sampled dataset impact on the accuracy of a reconstructed viral demography at any level of the transmission process. Methods drawn from phylodynamic inference combine powerful epidemiological and population genetic tools which can provide valuable insights into the dynamics of viral disease. However, the strict and sensitive dependency of the majority of these models on their assumptions makes estimates very fragile when these assumptions are violated. It is therefore essential that for these methods to be applied as reliable tools supporting control programs, more focused theoretical research is undertaken to model the epidemiological dynamics of infected populations using sequence data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QH301 Biology ; QH426 Genetics ; QR355 Virology ; SF600 Veterinary Medicine