Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748879
Title: Improving geospatial models of risk for vector-borne, zoonotic diseases
Author: Shearer, Freya
ISNI:       0000 0004 7232 6288
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Abstract:
Public health surveillance data are often incomplete, particularly where resources are lacking, but geospatial models can help to fill the gaps by providing estimates where data are sparse. By combining information on locations where diseases have been recorded with geographic data on environmental and socioeconomic covariates known to affect disease transmission using machine-learning models (such as boosted regression trees), niche modelling can generate fine-resolution, evidence-based risk maps for a variety of diseases of public health importance. This thesis investigates the geographical distribution of two vector-borne, zoonotic diseases of public health importance: Plasmodium knowlesi malaria and yellow fever (YF). A number of new methodological approaches to niche modelling are developed for: mapping diseases whose distributions are impacted by multiple host and vector species, ameliorating spatial bias in disease reporting rates, and accounting for human vaccination coverage. Chapter 2 investigates spatial variation in risk of human P. knowlesi infection across Southeast Asia. The infection risk model for P. knowlesi malaria is based on improvements to a standard niche modelling approach, and incorporates a novel joint distribution model to leverage data from a number of host species. Chapter 3 estimates YF vaccination coverage through time across all age cohorts in every district/municipality of countries at risk of YF, globally. These estimates are used to estimate the additional vaccination coverage needed to prevent further YF outbreaks, and they provide information needed to account for population immunity when estimating YF infection risk. Chapter 4 describes the development of a novel Poisson point process niche model, which is then used to predict YF infection risk in humans and demonstrates how vaccination coverage can be efficiently accounted for in disease niche models. The disease risk maps of P. knowlesi malaria and YF produced through this thesis will act as resources to improve the targeting, implementation and evaluation of disease prevention, surveillance and control strategies. Methods developed to account for vaccination coverage, reporting rate biases, and complex transmission systems will be applicable to risk mapping for a range of vector-borne, zoonotic diseases of public health importance.
Supervisor: Hay, Simon ; Moyes, Catherine ; Golding, Nick Sponsor: Rhodes Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.748879  DOI: Not available
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