Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768749
Title: Outfoxing rabies : robust vaccination designs for disease elimination
Author: Baker, Laurie Louise
ISNI:       0000 0004 7655 2899
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Prediction of pathogen dynamics and the design of effective interventions to control and eliminate disease are key goals in epidemiology. While progress has been made towards the elimination of many infectious diseases, only two, smallpox and rinderpest, have been globally eradicated. Mass vaccination can greatly reduce the burden of vaccine-preventable diseases. However, there is relatively little scientific guidance on the optimal duration, frequency and placement of control interventions for achieving elimination. Such insights could greatly inform policy and practice. Rabies is a deadly and terrifying disease that exacts a heavy toll on human lives and national economies, with over 50,000 human deaths each year and many millions more requiring expensive life-saving post-exposure vaccines. Elimination of rabies is feasible through vaccination, and oral rabies vaccination (ORV) campaigns have eliminated fox rabies from Western Europe. However, scientific guidance could improve elimination efforts elsewhere, and is still needed for contingency planning to maintain rabies freedom and for emergency response to incursions. My thesis focuses on two pivotal questions in infectious disease ecology: what are the underlying determinants of disease persistence, and how can vaccination strategies be optimized to eliminate infection? To answer these questions, I analysed a rich and highly resolved spatial dataset of fox rabies cases and ORV efforts over three decades in Germany and neighbouring countries. The long-term, large-scale nature of these data provides a unique opportunity to improve our understanding of wildlife rabies dynamics in response to vaccination using novel spatial modeling techniques. In chapter 2, I create a metapopulation model of regional rabies dynamics that incorporates local transmission (within regions) and spatial coupling (between regions) using a hierarchical Bayesian state-space model. In chapter 3, I extend the model developed in chapter 2 to determine the best vaccination strategy, in terms of scale and duration of ORV efforts for three common epidemiological scenarios: {\bf endemic} circulation of rabies; {\bf high-risk} situations when rabies-free but neighbor endemic areas; and an {\bf endgame} scenario when only a single endemic foci remains. In chapter 4, I develop a space-time model of fox rabies dynamics and explore the effect of scale on estimates of transmission terms by aggregating rabies case data at different spatial resolutions. I then relate these estimates to the scaling of individual interactions to regional dynamics through population mixing. Collectively, the findings from this thesis contribute to our understanding of how infectious diseases persist and can be controlled through vaccination. The methods generated can be used to explore tradeoffs in the scale and duration of ORV efforts, and generate recommendations on the time horizon and investment required to achieve and maintain freedom from disease. The model developed in chapter 4 also presents the first steps to developing a highly resolved spatial model of local rabies dynamics. These findings have immediate application to the design of cordon sanitaires in Europe, and to strategies aiming to rapidly eliminate re-emergence in high-risk countries such as Greece and Turkey. The analytical and statistical framework developed in this thesis is also applicable to answering analogous questions for the elimination of dog-mediated rabies and for other vaccine preventable diseases.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.768749  DOI:
Keywords: Q Science (General) ; QH301 Biology
Share: