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Longbottom, Joshua (2022) Geospatial methods for advancing efforts to eliminate human African trypanosomiasis, Thesis (Doctoral), Liverpool School of Tropical Medicine.
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J Longbottom PhD - Final Thesis.pdf - Accepted Version Download (20MB) | Preview |
Abstract
Gambian human African trypanosomiasis (g-HAT) is a neglected tropical disease caused by Trypanosoma brucei gambiense, transmitted by tsetse flies (Glossina). A World Health Organization (WHO) led programme to eliminate g-HAT, based on large-scale detection and treatment of cases alongside vector control, has reduced the global number of cases reported annually from a peak of 37,385 in 1998 to 565 in 2020. The WHO aims to eliminate transmission of g-HAT by 2030. To meet this goal, there is a pressing need for better mapping of cases, vectors, and interventions against g-HAT. Such maps will provide evidence that the elimination goal is being achieved and help guide interventions against persistent and/or re-emerging hotspots of transmission. g-HAT is a highly focal disease, requiring high spatial resolution analyses (<100m) which can identify vector and or transmission hotspots with high precision and accuracy. Increasing the resolution of geospatial analyses enables the utility of estimates in spatially targeted policy and programmatic decisions. This thesis aims to make an important contribution to the need for better maps and geospatial (spatially derived) models of cases, vectors and interventions in Uganda, a country which experienced an epidemic of g-HAT in the 1990s (13,842 cases, 1990-1999) but is now approaching the elimination goal (194 cases, 2010-2019). The thesis comprises four related studies. First (Chapter 2), a review of published literature on geospatial models of g-HAT and associated vectors revealed that most endemic countries have contemporary estimates of g-HAT risk, but there are large spatial and temporal discrepancies in the completeness of tsetse mapping. Second (Chapter 3), cost-distance analyses were performed to provide a rational basis for the positioning of entomological monitoring sites. The utility of satellite imagery at two different spatial resolutions (0.5 & 3m) were compared, and an improved approach for monitoring tsetse in Koboko district, Uganda was produced. Third (Chapter 4), the cost-distance approach was applied to quantify geographic access to Uganda’s national network of diagnostic facilities for g-HAT. Paired with a simulation-based method, I showed how Uganda might reduce its number of diagnostic centres from 170 to 51 and still meet the targets of ensuring that 50% of the at-risk population live within 1-hour of a diagnostic facility and 95% live within 5-hours travel. Finally (Chapter 5), a 10-year time series of tsetse catch data from 569 individual monitoring sites was analysed to produce a spatio-temporal geostatistical model of tsetse abundance. By incorporating data on the deployment of Tiny Targets to control tsetse, I estimated that this intervention has reduced the abundance of tsetse by 88%. The results from the empirical studies are discussed in the context of improved strategies and policies for monitoring the spatial and temporal dynamics of g-HAT and tsetse populations.
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