Impact of a national tsetse control programme to eliminate Gambian sleeping sickness in Uganda: a spatiotemporal modelling study

Introduction Tsetse flies (Glossina) transmit Trypanosoma brucei gambiense, which causes gambiense human African trypanosomiasis (gHAT). As part of national efforts to eliminate gHAT as a public health problem, Uganda implemented a large-scale programme of deploying Tiny Targets, which comprise panels of insecticide-treated material which attract and kill tsetse. At its peak, the programme was the largest tsetse control operation in Africa. Here, we quantify the impact of Tiny Targets and environmental changes on the spatial and temporal patterns of tsetse abundance across North-Western Uganda.
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\nMethods We leverage a 100-month longitudinal dataset detailing Glossina fuscipes fuscipes catches from monitoring traps between October 2010 and December 2019 within seven districts in North-Western Uganda. We fitted a boosted regression tree (BRT) model assessing environmental suitability, which was used alongside Tiny Target data to fit a spatiotemporal geostatistical model predicting tsetse abundance across our study area (~16 000 km2). We used the spatiotemporal model to quantify the impact of Tiny Targets and environmental changes on the distribution of tsetse, alongside metrics of uncertainty.
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\nResults Environmental suitability across the study area remained relatively constant over time, with suitability being driven largely by elevation and distance to rivers. By performing a counterfactual analysis using the fitted spatiotemporal geostatistical model, we show that deployment of Tiny Targets across an area of 4000 km2 reduced the overall abundance of tsetse to low levels (median daily catch=1.1 tsetse/trap, IQR=0.85–1.28). No spatial–temporal locations had high (>10 tsetse/trap/day) numbers of tsetse compared with 18% of locations for the counterfactual.
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\nConclusions In Uganda, Tiny Targets reduced the abundance of G. f. fuscipes and maintained tsetse populations at low levels. Our model represents the first spatiotemporal geostatistical model investigating the effects of a national tsetse control programme. The outputs provide important data for informing next steps for vector control and surveillance.