In efforts to survey biting flies, there is a need for a multi-species trap that can capture diverse families of hematophagous flies, thus functioning as a more robust and representative trapping method. The current study evaluated the efficiency of the baited P38T BioFlyTrap with other standard traps, namely the H-trap used in South Africa, the Vavoua (West/Central Africa), and the Nzi (East Africa), in informing decision-making to undertake enhanced surveillance and control interventions against biting flies in South Africa. A comparative study was done using a 4 × 4 Latin Square Design (LSD) approach over a period of 16 days in northeastern KwaZulu-Natal. The H-trap was most efficient for Glossinidae, while the Nzi trap caught the most diverse species dominated by Tabanidae. The P38T BioFlyTrap showed moderate performance across all categories (Glossinidae, Tabanidae, Muscidae (Stomoxyini)) of biting flies. The Vavoua trap was the least effective trap for Glossinidae and Tabanidae, but was relatively effective for Stomoxyini. In this environment, baited P38T BioFlyTrap would be useful for broader spectrum surveillance, while baited H-trap and Nzi remain specifically more performant, respectively for Glossinidae and Tabanidae.

African Animal Trypanosomosis (AAT) is a parasitic disease caused by trypanosome species, posing a major challenge to animal health and production in Africa. In the Matutuíne District, southern Mozambique, where Glossina brevipalpis is prevalent, AAT is endemic. Previous research conducted in South Africa suggested that G . brevipalpis plays a marginal role in the transmission of Trypanosoma congolense . However, considering the wide distribution of G. brevipalpis in Southeast Africa and the major epidemiological implications of the aforementioned observations, this study was designed to re-assess the vectorial competence of G. brevipalpis in the transmission of T. congolense- savannah type. The research involved 915 G. brevipalpis flies, fed six times on two cattle infected with T. congolense . After 48 hours, the flies were later fed on a non-infected animal to clean the proboscis, followed by extended feeding on four susceptible cattle for 22 days. These five clean animals were monitored for 60 days based on body temperature, packed cell volume, buffy coat technique and polymerase chain reaction. In the meantime, flies were dissected weekly to observe the development of trypanosomes in the midgut and proboscis. To discard any possible mechanical transmission by G. brevipalpis 48 hours after an infected blood meal, 97 flies from a colony were fed once on two infected cattle and once on a susceptible bovine. The Spearman correlation and Mann-Whitney U tests were used to analyse the data. Results showed that the four susceptible cattle became infected with T. congolense , while the animal used for proboscis cleaning and discard any mechanical transmission remained uninfected. Dissections revealed that 89% of flies were positive for trypanosome through microscopy and 100% through PCR. These findings confirm that G. brevipalpis is a competent biological vector for transmitting T. congolense, challenging earlier assumptions and emphasizing its role in AAT dynamics. ### Competing Interest Statement The authors have declared no competing interest.

BACKGROUND:Tsetse flies (Glossina) transmit species of Trypanosoma which cause human African trypanosomiasis (HAT) and animal African trypanosomiasis (AAT). Understanding the epidemiology of this disease and controlling the vector rationally requires analysis of the abundance, age structure, infection rates and feeding patterns of tsetse populations. METHODS:We analysed a population of G. fuscipes fuscipes in the Koboko district of Uganda. Seasonal variation in the abundance of tsetse was assessed from the numbers of tsetse caught in pyramidal traps. The age structure of the population was assessed by dissecting female tsetse to estimate their ovarian categories. Classical and PCR-based methods were utilised to determine the presence of the three major pathogenic species of salivarian trypanosomes: T. vivax, T. congolense and T. brucei in a subset (n = 2369) of flies. Further, bloodmeal analysis was carried out using PCR to amplify and sequence a portion of the vertebrate cytb gene. RESULTS:The abundance and age structure of tsetse populations were relatively stable and a slight seasonal four-fold variation in abundance appeared to be correlated with rainfall. Analyses of age structure suggests a low natural daily mortality of 1.75% (1.62-1.88). Infection rates estimated were significantly greater (1.9-9.3 times) using the PCR-based method compared to the classical dissection-based method. Positive rates for T. brucei sl, T. congolense and T. vivax were 1.6% (1.32-2.24), 2.4% (1.83-3.11and 2.0% (1.46-2.63), respectively by PCR. The majority of bloodmeals were identified as cattle (39%, 30.5-47.8) and human (37%, 28.4-45.6). CONCLUSION:The seasonally stable abundance, low mortality rate and high proportion of bloodmeals from humans may explain, in part, why this district has historically been a focus of sleeping sickness. Additionally, the high rates of cattle feeding indicate insecticide treated cattle may prove to be a useful vector control strategy in the area.

IntroductionTsetse flies (Glossina) transmitTrypanosoma 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.MethodsWe leverage a 100-month longitudinal dataset detailingGlossina fuscipes fuscipescatches 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.ResultsEnvironmental 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 km2reduced 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.ConclusionsIn Uganda, Tiny Targets reduced the abundance ofG. f. fuscipesand 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.

BACKGROUND:Tsetse flies (Glossina) transmit Trypanosoma brucei gambiense which causes Gambian human African trypanosomiasis (gHAT) in Central and West Africa. Several countries use Tiny Targets, comprising insecticide-treated panels of material which attract and kill tsetse, as part of their national programmes to eliminate gHAT. We studied how the scale and arrangement of target deployment affected the efficacy of control.METHODOLOGY AND PRINCIPAL FINDINGS:Between 2012 and 2016, Tiny Targets were deployed biannually along the larger rivers of Arua, Maracha, Koboko and Yumbe districts in North West Uganda with the aim of reducing the abundance of tsetse to interrupt transmission. The extent of these deployments increased from ~250 km2 in 2012 to ~1600 km2 in 2015. The impact of Tiny Targets on tsetse populations was assessed by analysing catches of tsetse from a network of monitoring traps; sub-samples of captured tsetse were dissected to estimate their age and infection status. In addition, the condition of 780 targets (~195/district) was assessed for up to six months after deployment. In each district, mean daily catches of tsetse (G. fuscipes fuscipes) from monitoring traps declined significantly by >80% following the deployment of targets. The reduction was apparent for several kilometres on adjacent lengths of the same river but not in other rivers a kilometre or so away. Expansion of the operational area did not always produce higher levels of suppression or detectable change in the age structure or infection rates of the population, perhaps due to the failure to treat the smaller streams and/or invasion from adjacent untreated areas. The median effective life of a Tiny Target was 61 (41.8-80.2, 95% CI) days.CONCLUSIONS:Scaling-up of tsetse control reduced the population of tsetse by >80% across the intervention area. Even better control might be achievable by tackling invasion of flies from infested areas within and outside the current intervention area. This might involve deploying more targets, especially along smaller rivers, and extending the effective life of Tiny Targets.

Background Riverine species of tsetse ( Glossina ) transmit Trypanosoma brucei gambiense , which causes Gambian human African trypanosomiasis (gHAT), a neglected tropical disease. Uganda aims to eliminate gHAT as a public health problem through detection and treatment of human cases and vector control. The latter is being achieved through the deployment of ‘Tiny Targets’, insecticide-impregnated panels of material which attract and kill tsetse. We analysed the spatial and temporal distribution of cases of gHAT in Uganda during the period 2010–2019 to assess whether Tiny Targets have had an impact on disease incidence. Methods To quantify the deployment of Tiny Targets, we mapped the rivers and their associated watersheds in the intervention area. We then categorised each of these on a scale of 0–3 according to whether Tiny Targets were absent (0), present only in neighbouring watersheds (1), present in the watersheds but not all neighbours (2), or present in the watershed and all neighbours (3). We overlaid all cases that were diagnosed between 2000 and 2020 and assessed whether the probability of finding cases in a watershed changed following the deployment of targets. We also estimated the number of cases averted through tsetse control. Results We found that following the deployment of Tiny Targets in a watershed, there were fewer cases of HAT, with a sampled error probability of 0.007. We estimate that during the intervention period 2012–2019 we should have expected 48 cases (95% confidence intervals = 40–57) compared to the 36 cases observed. The results are robust to a range of sensitivity analyses. Conclusions Tiny Targets have reduced the incidence of gHAT by 25% in north-western Uganda. Graphical abstract

BackgroundRiverine tsetse (Glossinaspp.) transmitTrypanosoma brucei gambiensewhich causes Gambian Human African Trypanosomiasis. Tiny Targets were developed for cost-effective riverine tsetse control, and comprise panels of insecticide-treated blue polyester fabric and black net that attract and kill tsetse. Versus typical blue polyesters, two putatively more attractive fabrics have been developed: Vestergaard ZeroFly blue, and violet. Violet was most attractive to savannah tsetse using large targets, but neither fabric has been tested for riverine tsetse using Tiny Targets.MethodsWe measured numbers ofG.f.fuscipesattracted to electrified Tiny Targets in Kenya and Uganda. We compared violets, Vestergaard blues, and a typical blue polyester, using three replicated Latin squares experiments. We then employed Bayesian statistical analyses to generate expected catches for future target deployments incorporating uncertainty in model parameters, and prior knowledge from previous experiments.ResultsExpected catches for average future replicates of violet and Vestergaard blue targets were highly likely to exceed those for typical blue. Accounting for catch variability between replicates, it remained moderately probable (70–86% and 59–84%, respectively) that a given replicate of these targets would have a higher expected catch than typical blue on the same day at the same site. Meanwhile, expected catches for average violet replicates were, in general, moderately likely to exceed those for Vestergaard blue. However, the difference in medians was small, and accounting for catch variability, the probability that the expected catch for a violet replicate would exceed a Vestergaard blue equivalent was marginal (46–71%).ConclusionViolet and Vestergaard ZeroFly blue are expected to outperform typical blue polyester in the Tiny Target configuration. Violet is unlikely to greatly outperform Vestergaard blue deployed in this way, but because violet is highly attractive to both riverine and savannah tsetse using different target designs, it may provide the more suitable general-purpose fabric.

Gambiense human African trypanosomiasis (g-HAT) is the chronic form of sleeping sickness caused by Trypanosoma brucei gambiense in West and Central Africa, while Trypanosoma brucei rhodesiense causes an acute form in eastern Africa. g-HAT is targeted for elimination as a public health problem by 2020 and 0 transmission by 2030 [1,2]. Control of g-HAT is largely based on identification and treatment of infected individuals, supplemented by control of the tsetse fly vectors [3]. There has been growing evidence that when both tsetse control and case identification activities are carried out simultaneously in the same geographies, elimination of the disease is accelerated [4–6]. Here, we describe how the Trypa-NO! Partnership is using novel and classical tools to drive g-HAT elimination in an integrated approach, progress made, lessons learnt, and future directions.

AbstractWe investigated genetic variation at 37 newly-developed microsatellite loci in populations of the tsetse flyGlossina fuscipes fuscipescaptured from the upper and lower reaches of a single hydrographical network within an endemic Human African Trypanosomiasis focus. Our primary aim was to assess the impact of vector control using insecticide-treated baits (Tiny Targets) on genetic structure. We initially usedSTRUCTUREto delineate geographical boundaries of two stable ‘ancestral’ reference populations without any history of vector control but marked for either vector control (‘intervention’) or no control (‘non-intervention’). We then used theADMIXTUREmodel to assess genetic divergence in temporal populations collected after vector control implementation. We applied the Linkage Disequilibrium method to explicitly measure spatial and temporal changes in effective population size (Ne). We observed a significant reduction inNecoincident with vector control, whereasNeremained stable in the non-intervention area. Our empirical findings show how classical population genetics approaches detected within a short period of time, a significant genetic bottleneck associated with vector control, and opens up the possibility of using routine genomic surveillance. We have also generated a resource of new genetic markers for studies on the population genetics of tsetse at finer-scale resolution.FundingThis work was funded through a Wellcome Trust Master’s Fellowship in Public Health and Tropical Medicine awarded to Allan Muhwezi (103268/Z/13/Z).

We investigated genetic variation at 37 newly-developed microsatellite loci in populations of the tsetse fly captured from the upper and lower reaches of a single hydrographical network within an endemic Human African Trypanosomiasis focus. Our primary aim was to assess the impact of vector control using insecticide-treated baits (Tiny Targets) on genetic structure. We initially used to delineate geographical boundaries of two stable ‘ancestral’ reference populations without any history of vector control but marked for either vector control (‘intervention’) or no control (‘non-intervention’). We then used the model to assess genetic divergence in temporal populations collected after vector control implementation. We applied the Linkage Disequilibrium method to explicitly measure spatial and temporal changes in effective population size (). We observed a significant reduction in coincident with vector control, whereas remained stable in the non-intervention area. Our empirical findings show how classical population genetics approaches detected within a short period of time, a significant genetic bottleneck associated with vector control, and opens up the possibility of using routine genomic surveillance. We have also generated a resource of new genetic markers for studies on the population genetics of tsetse at finer-scale resolution.

BACKGROUND:Large-scale control of sleeping sickness has led to a decline in the number of cases of Gambian human African trypanosomiasis (g-HAT) to <2000/year. However, achieving complete and lasting interruption of transmission may be difficult because animals may act as reservoir hosts for T. b. gambiense. Our study aims to update our understanding of T. b. gambiense in local vectors and domestic animals of N.W. Uganda.METHODS:We collected blood from 2896 cattle and 400 pigs and In addition, 6664 tsetse underwent microscopical examination for the presence of trypanosomes. Trypanosoma species were identified in tsetse from a subsample of 2184 using PCR. Primers specific for T. brucei s.l. and for T. brucei sub-species were used to screen cattle, pig and tsetse samples.RESULTS:In total, 39/2,088 (1.9%; 95% CI = 1.9-2.5) cattle, 25/400 (6.3%; 95% CI = 4.1-9.1) pigs and 40/2,184 (1.8%; 95% CI = 1.3-2.5) tsetse, were positive for T. brucei s.l.. Of these samples 24 cattle (61.5%), 15 pig (60%) and 25 tsetse (62.5%) samples had sufficient DNA to be screened using the T. brucei sub-species PCR. Further analysis found no cattle or pigs positive for T. b. gambiense, however, 17/40 of the tsetse samples produced a band suggestive of T. b. gambiense. When three of these 17 PCR products were sequenced the sequences were markedly different to T. b. gambiense, indicating that these flies were not infected with T. b. gambiense.CONCLUSION:The lack of T. b. gambiense positives in cattle, pigs and tsetse accords with the low prevalence of g-HAT in the human population. We found no evidence that livestock are acting as reservoir hosts. However, this study highlights the limitations of current methods of detecting and identifying T. b. gambiense which relies on a single copy-gene to discriminate between the different sub-species of T. brucei s.l.

Background: Vector control is emerging as an important component of global efforts to control Gambian sleeping sickness (human African trypanosomiasis, HAT). The deployment of insecticide-treated targets ("Tiny Targets") to attract and kill riverine tsetse, the vectors of Trypanosoma brucei gambiense, has been shown to be particularly cost-effective. As this method of vector control continues to be implemented across larger areas, knowledge of the abundance of tsetse to guide the deployment of "Tiny Targets" will be of increasing value. In this paper, we use a geostatistical modelling framework to produce maps of estimated tsetse abundance under two scenarios: (i) when accurate data on the local river network are available; and (ii) when river information is sparse. Methods: Tsetse abundance data were obtained from a pre-intervention survey conducted in northern Uganda in 2010. River network data obtained from either digitised maps or derived from 30 m resolution digital elevation model (DEM) data as a proxy for ground truth data. Other environmental variables were derived from publicly-available resolution remotely sensed data (e.g. Landsat, 30 m resolution). Zero-inflated negative binomial geostatistical models were fitted to the abundance data using an integrated nested Laplace approximation approach, and maps of estimated tsetse abundance were produced. Results: Restricting the analysis to traps located within 100 m of any river, positive associations were identified between the length of river and the minimum soil/vegetation moisture content of the surrounding area and daily fly catches, whereas negative associations were identified with elevation and distance to the river. The resulting models could accurately distinguish between traps with high and low fly catches (e.g. < 5 or > 5 flies/day), with a ROC-AUC (receiver-operating characteristic area under the curve) greater than 0.9. Whilst the precise course of the river was not well approximated using the DEM data, the models fitted using DEM-derived river data performed similarly to those that incorporated the more accurate local river information. Conclusions: These models can now be used to assist in the design, implementation and monitoring of tsetse control operations in northern Uganda and further can be used as a framework by which to undertake similar studies in other areas where Glossina fuscipes fuscipes spreads Gambian sleeping sickness.

An unpredicted outbreak of African animal trypanosomosis or nagana in 1990 in north-eastern KwaZulu-Natal necessitated an emergency control programme, utilising the extensive cattledipping system in the area, as well as a reassessment of the tsetse and trypanosomosis problem in the province. Since 1990, sporadic blood sampling of cattle at the dip tanks in the naganainfested areas were undertaken to identify trypanosome species involved and to determine the infection prevalence in cattle. The distribution and species composition of the tsetse populations in the area were also investigated. From November 2005 to November 2007 selected dip tanks were surveyed for trypanosome infection prevalence. During April 2005 to August 2009 the distribution and abundance of tsetse populations were assessed with odour-baited H traps. The tsetse and trypanosome distribution maps were updated and potential correlations between tsetse apparent densities (ADs) and the prevalence of trypanosomosis were assessed. Glossina brevipalpis Newstead and Glossina austeni Newstead were recorded in locations where they have not previously been collected. No significant correlation between tsetse relative abundance and nagana prevalence was found, which indicated complex interactions between tsetse fly presence and disease prevalence. This was epitomised by data that indicated that despite large differences in the ADs of G. austeni and G. brevipalpis, trypanosome infection prevalence was similar in all three districts in the area. This study clearly indicated that both tsetse species play significant roles in trypanosome transmission and that it will be essential that any control strategy, which aims at sustainable management of the disease, should target both species. Keywords: Tsetse distribution; Glossina brevipalpis; Glossina austeni; trypanosome infection prevalence

The traditional role of African elders and their connection with the community make them important stakeholders in community-based disease control programmes. We explored elders' memories related to interventions against sleeping sickness to assess whether or not past interventions created any trauma which might hamper future control operations. Using a qualitative research framework, we conducted and analysed twenty-four in-depth interviews with Lugbara elders from north-western Uganda. Participants were selected from the villages inside and outside known historical sleeping sickness foci. Elders' memories ranged from examinations of lymph nodes conducted in colonial times to more recent active screening and treatment campaigns. Some negative memories dating from the 1990s were associated with diagnostic procedures, treatment duration and treatment side effects, and were combined with memories of negative impacts related to sleeping sickness epidemics particularly in HAT foci. More positive observations from the recent treatment campaigns were reported, especially improvements in treatment. Sleeping sickness interventions in our research area did not create any permanent traumatic memories, but memories remained flexible and open to change. This study however identified that details related to medical procedures can remain captured in a community's collective memory for decades. We recommend more emphasis on communication between disease control programme planners and communities using detailed and transparent information distribution, which is not one directional but rather a dialogue between both parties.

BackgroundGambian sleeping sickness (human African trypanosomiasis, HAT) outbreaks are brought under control by case detection and treatment although it is recognised that this typically only reaches about 75% of the population. Vector control is capable of completely interrupting HAT transmission but is not used because it is considered too expensive and difficult to organise in resource-poor settings. We conducted a full scale field trial of a refined vector control technology to determine its utility in control of Gambian HAT.Methods and FindingsThe major vector of Gambian HAT is the tsetse fly Glossina fuscipes which lives in the humid zone immediately adjacent to water bodies. From a series of preliminary trials we determined the number of tiny targets required to reduce G. fuscipes populations by more than 90%. Using these data for model calibration we predicted we needed a target density of 20 per linear km of river in riverine savannah to achieve >90% tsetse control. We then carried out a full scale, 500 km(2) field trial covering two HAT foci in Northern Uganda to determine the efficacy of tiny targets (overall target density 5.7/km(2)). In 12 months, tsetse populations declined by more than 90%. As a guide we used a published HAT transmission model and calculated that a 72% reduction in tsetse population is required to stop transmission in those settings.InterpretationThe Ugandan census suggests population density in the HAT foci is approximately 500 per km2. The estimated cost for a single round of active case detection (excluding treatment), covering 80% of the population, is US$433,333 (WHO figures). One year of vector control organised within the country, which can completely stop HAT transmission, would cost US$42,700. The case for adding this method of vector control to case detection and treatment is strong. We outline how such a component could be organised.

Introduction To evaluate the relative effectiveness of tsetse control methods, their costs need to be analysed alongside their impact on tsetse populations. Very little has been published on the costs of methods specifically targeting human African trypanosomiasis Methodology/Principal Findings In northern Uganda, a 250 km2 field trial was undertaken using small (0.5 X 0.25 m) insecticide-treated targets (“tiny targets”). Detailed cost recording accompanied every phase of the work. Costs were calculated for this operation as if managed by the Ugandan vector control services: removing purely research components of the work and applying local salaries. This calculation assumed that all resources are fully used, with no spare capacity. The full cost of the operation was assessed at USD 85.4 per km2, of which USD 55.7 or 65.2% were field costs, made up of three component activities (target deployment: 34.5%, trap monitoring: 10.6% and target maintenance: 20.1%). The remaining USD 29.7 or 34.8% of the costs were for preliminary studies and administration (tsetse surveys: 6.0%, sensitisation of local populations: 18.6% and office support: 10.2%). Targets accounted for only 12.9% of the total cost, other important cost components were labour (24.1%) and transport (34.6%). Discussion Comparison with the updated cost of historical HAT vector control projects and recent estimates indicates that this work represents a major reduction in cost levels. This is attributed not just to the low unit cost of tiny targets but also to the organisation of delivery, using local labour with bicycles or motorcycles. Sensitivity analyses were undertaken, investigating key prices and assumptions. It is believed that these costs are generalizable to other HAT foci, although in more remote areas, with denser vegetation and fewer people, costs would increase, as would be the case for other tsetse control techniques.