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Title: Spatial and temporal distribution of onchocerciasis in West Africa, with particular reference to Ghana
Author: O'Hanlon, Simon
ISNI:       0000 0004 7232 6261
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Background. Quantifying and predicting the effect of control interventions against infectious diseases requires knowledge about prevalence prior to the implementation of such control measures as well as knowledge of key parameters which affect model outcomes. Here is presented the first geostatistical map estimating the prevalence of human onchocerciasis in the former Onchocerciasis Control Programme in West Africa (OCP) before the initiation of vector control operations in 1975. The map is used to quantify the burden of onchocercal skin disease prior to interventions. The results of a socioeconomic survey investigating coverage and compliance to community-directed treatment with ivermectin are then presented. Methods and Findings. The OCP epidemiological database was investigated for spatial autocorrelation. A total of 737 village surveys had parasitological examinations conducted prior to the start of control. Using these 737 pre-control data points plus environmental covariates, a Bayesian model-based geostatistical (B-MBG) approach was used to generate a continuous surface (at a pixel resolution of 25km2) of microfilarial prevalence in West Africa prior to the commencement of the OCP. The mean Pearson’s correlation between observed prevalence and model-estimates of prevalence at hold-out locations was 0.693; mean prediction error was 0.77% and mean absolute prediction error was 12.2%. Within OCP boundaries, 17.8 million people were deemed to be at risk and 7.6 million infected in 1975 which is greater than previous estimates. The mean microfilarial prevalence across all countries was 45% (range: 2–90%). Conclusions and Significance. This is the first map of continuous onchocerciasis prevalence in West Africa, developed using robust geostatistical methods. Important environmental predictors of infection prevalence were identified and used in a model that out-performs those without spatial-random effects or environmental covariates. Our results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission, informing feasibility of elimination with current and novel tools.
Supervisor: Basáñez, Maria-Gloria ; Cheke, Robert ; Prichard, Roger Sponsor: Economic and Social Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral