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  • 2015EmanueleGiorgiPhD

    Accepted author manuscript, 32.7 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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Geostatistical methods for disease prevalence mapping

Research output: ThesisDoctoral Thesis

Published
Publication date2015
Number of pages143
QualificationPhD
Awarding Institution
Supervisors/Advisors
Publisher
  • Lancaster University
Original languageEnglish

Abstract

Geostatistical methods are increasingly used in low-resource settings where disease registries are either non-existent or geographically incomplete. In this thesis, which is comprised of four papers, we address some of the common issues that arise from analysing disease prevalence data. In the first paper we consider the problem of combining data from multiple spatially referenced surveys so as to account for two main sources of variation: temporal variation, when surveys are repeated over time; data-quality variation, e.g. between randomised and non-randomised surveys. We then propose a multivariate binomial geostatistical model for the combined analysis of data from multiple surveys. We also show an application to malaria prevalence data from three surveys conducted in two consecutive years in Chikwawa District, Malawi, one of which used a more economical convenience sampling strategy. In the second paper, we analyse river-blindness prevalence data from a survey conducted in 20 African countries enrolled in the African Programme of Onchocerciasis Control (APOC). The main challenge of this analysis is computational, as a binomial geostatistical model has to be fitted to more than 14,000 village locations and predictions carried out on about 10 millions locations across Africa. To make the computation feasible and efficient, we then develop a low rank approximation based on a convolution-kernel representation which avoids matrix inversion. The third paper is a tutorial on the use of a new R package, namely “PrevMap”, which provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. In the fourth paper, we present some extensions of the standard geostatistical model for spatio-temporal analysis of prevalence data and modelling of spatially structured zero-inflation. We then describe three applications that have arisen through our collaborations with researchers and public health programmers in African countries.