Infectious diseases pose a significant threat to animal and human health across the globe, with much of the burden falling on low-income countries. Despite efforts to control many of these diseases, very few have ever been eradicated. Their dynamics are often embedded in complex, heterogeneous landscapes defined by interacting population and landscape level processes. As such, landscape heterogeneity plays a key role in driving disease transmission and persistence. Incorporating landscape heterogeneity in studies of pathogen dynamics is challenging but the accessibility of data, particularly next generation sequencing data, has opened new avenues of research. Landscape epidemiology involves using an integrated approach to understand spatial patterns of disease, using methods that combine landscape genetics, ecology and epidemiology. in this thesis I use these integrative methods to determine the underlying mechanisms facilitating the spread and persistence of canine rabies virus in Tanzania. Whole genome level characterisation of rabies virus samples was achieved and used in combination with cutting-edge inference techniques to explore spatial patterns of rabies at different spatial scales. Phylogeographic patterns were able to characterise spatial scales of endemic rabies transmission in Tanzania, uncovering strong viral population structure at sub-continental levels with evidence of a more fluid dispersal dynamic at local ( less than 100km2 area) spatial scales . Within-country phylogeographic patterns revealed large regional movements within Tanzania that could be attributed to human-mediated movements and revealed the presence of multiple co-circulating lineages within a single administrative district. Finely resolved incidence data from the Serengeti District complemented with whole genome sequences enabled the exploration of local scales of transmission in more detail. By extending phylogeographic diffusion models to incorporate landscape heterogeneity I was able to uncover evidence supporting landscape predictors of rabies diffusion. While much of the spatial structure was attributable to the effects of isolation by distance, landscape predictors had discernible effects on diffusion. In particular, rivers appeared to act as a barrier to dispersal and road networks facilitated diffusion and I found evidence to support vaccination as an effective control measure for canine rabies in the Serengeti District. Importantly, I also found evidence to support vaccination as resistance to diffusion and therefore an effective control measure for dog rabies. As a complementary approach a space-time-genetic algorithm was used to determine who-infected-whom in the Serengeti District. The model explicitly accounted for the possibility of exogenous sources of infection and how to incorporate genetic data available for only a proportion of samples. Direct transmission events were estimated between 42% of observed cases and highlighted the co-circulation of two major lineages in both time and space. Direct transmission events predominantly occurred over very small distances, less than 1km, but a large proportion of cases had unobserved sources that could represent transmission from dogs in neighbouring regions or larger indirect transmission events. A future development of the model is to delineate between these possibilities to assess the true contribution of exogenous sources to the system dynamic. Ultimately these integrative models are at an early stage of development but highlight the power of genetic data to delineate fine-scale transmission patterns. The results from this thesis suggest that landscape features such as rivers could be exploited as barriers in step-wise vaccination campaigns and highlight the utility of genetic surveillance to monitor control and elimination as rabies management progresses.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:685868 |
| Date | January 2016 |
| Creators | Brunker, Kirstyn |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/7278/ |
Page generated in 0.0022 seconds