Biology subjects

Stanton, M. C.

Publications and source records attributed to Stanton, M. C..

5 recordsLinked to original sources

bioRxiv2024

Impact of a national tsetse control programme to eliminate Gambian sleeping sickness in Uganda: a spatio-temporal modelling study.

IntroductionTsetse 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. MethodsWe 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 model assessing environmental suitability which was used alongside Tiny Target data to fit a spatio-temporal geostatistical model predicting tsetse abundance across our study area ([~]16,000 km2). We used the spatio-temporal 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 spatio-temporal 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) with no spatial-temporal locations having high (>10 tsetse/trap/day) numbers of tsetse compared to 18% of locations for the counterfactual. ConclusionsIn Uganda, Tiny Targets reduced the abundance of G. f. fuscipes and maintained tsetse populations at low levels. Our model represents the first spatio-temporal model investigating the effects of a national tsetse control programme. The outputs provide important data for informing next steps for vector-control and surveillance. Key questionsO_ST_ABSWhat is already known on this topic?C_ST_ABSSmall panels of insecticide-treated fabric, called Tiny Targets, are used to attract, and kill riverine tsetse, the vectors of T. b. gambiense which causes gambiense human African trypanosomiasis (gHAT). In large-scale (250-2000 km2) trials conducted in five countries, deployment of Tiny Targets reduced the densities of tsetse by between 60 and >90%. What this study addsWe report an analysis of, and data from, a large-scale ([~]4,000km2) national tsetse control programme, implemented in Uganda to eliminate gHAT as a public health problem. We found that Tiny Targets reduced tsetse abundance across the study period (2011-2019) and maintained densities at low (<1 tsetse/trap/day) levels. We produce maps which detail spatial variances in tsetse abundance in response to vector control. How this study might affect research, practice, or policyIn 2022, Uganda received validation from the World Health Organisation (WHO) that it had eliminated gHAT as a public health problem. The large-scale deployment of Tiny Targets contributed to this achievement. Our findings provide evidence that Tiny Targets are an important intervention for other countries aiming to eliminate gHAT.

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bioRxiv2023

Small dams drive Anopheles abundance during the dry season in a high malaria burden area of Malawi

This study explores the influence of small dams on the exposure to malaria vectors during the dry season in Kasungu district, Malawi, an area recently identified as high priority for malaria interventions by the National Malaria Control Programme. Small dam impoundments provide communities with a continuous supply of water for domestic and agricultural activities across sub-Saharan Africa and are considered vital to food security and climate change resilience. However, these permanent water bodies also create ideal breeding sites for mosquitoes in typically arid landscapes. The study focuses on a specific dam impoundment and its vicinity, aiming to assess its spatial and temporal influence on indoor vector densities. Throughout May to August 2021, CDC light traps were used to measure indoor mosquito densities for two consecutive nights per month in three communities located at increasing distances from the dam (0km, [~]1km, [~]2km). Simultaneously, drone imagery was captured for each community, enabling the identification of additional standing water within approximately 400 meters of selected households. Larval sampling was carried out within the impoundment periphery and in additional water bodies identified in the drone imagery. Generalised linear mixed models (GLMMs) were employed to analyse the indoor Anopheles abundance data, estimating the effects of household structure (open/closed eaves), month, temperature, and water proximity on malaria vector exposure. Throughout 685 trapping nights, a total of 1,256 mosquitoes were captured, with 33% (412) being female Anopheles. Among these, 91% were morphologically identified as An. funestus s.l., and 5% as An. gambiae s.l. Catches progressively declines in each consecutive trapping month as the environment became drier. This decline was much slower in Malangano, the community next to the dam, with abundance being notably higher in June and July. Further, the majority of An. gambiae s.l. were caught in May, with none identified in July and August. Anopheles larvae were found both in the impoundment and other smaller water bodies such as irrigation wells in each survey month, however the presence of these smaller water bodies did not have a significant impact on adult female mosquito catches in the GLMM. The study concludes that proximity to the dam impoundment was the primary driver of differences between survey communities with the abundance in Chikhombwe ([~]1km away) and Chiponde ([~]2km away) being 0.35 (95% CI 0.19-0.66) and 0.28 (95% CI 0.16-0.47) lower than Malangano respectively after adjusting for other factors. These findings underscore the importance of targeted interventions, such as larval source management or housing improvements, near small dams to mitigate malaria transmission risks during the dry season. Further research is needed to develop cost-effective strategies for vector control within and around these impoundments.

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bioRxiv2020

The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

O_LISpatial and temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by removing habitat and/or reduce the likelihood of larvae developing into adults, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, we use our experiences of drone-led larval habitat identification in Malawi to assess the accuracy and practicalities of this approach. C_LIO_LIDrone imagery and larval surveys were conducted in Kasungu district, Malawi between 2018-2020. Water bodies and aquatic vegetation were identified in the imagery using both manual methods and geographical object-based image analysis (GeoOBIA) and the performance of the classifications were compared. Larval sampling sites were characterised by biotic factors visible in drone imagery (e.g. vegetation coverage, type), and generalised linear mixed models were used to determine their association with larval presence. C_LIO_LIImagery covering an area of 8.9km2 across eight sites was captured. Characteristics associated with rural larval habitat were successfully identified using GeoOBIA (e.g. median accuracy = 0.98, median kappa = 0.96 using a standard RGB camera), with a median of 18.3% being classed as surface water, compared to 20.1% using manual identification. The GeoOBIA approach, however, required greater processing time and technical skills. Larval samples were captured from 326 sites, and a relationship was identified between larval presence and vegetation (log-OR=1.44, p=0.01). Vegetation type was also a significant factor when considering late stage anopheline larvae only. C_LIO_LIOur study demonstrates the potential for drone-acquired imagery as a tool to support the identification of mosquito larval habitat in rural areas where malaria is endemic. There are, however, technical challenges to overcome before it can be smoothly integrated into malaria control activities. Further consultations between experts and stakeholders in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited. C_LI

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bioRxiv2020

Impact of Tiny Targets on Glossina fuscipes quanzensis, the primary vector of Human African Trypanosomiasis in the Democratic Republic of Congo.

BackgroundOver the past 20 years there has been a >95% reduction in the number of Gambian Human African trypanosomiasis (g-HAT) cases reported globally, largely as a result of large-scale active screening and treatment programmes. There are however still foci where the disease persists, particularly in parts of the Democratic Republic of the Congo (DRC). Additional control efforts such as tsetse control using Tiny Targets may therefore be required to achieve g-HAT elimination goals. The purpose of this study was to evaluate the impact of Tiny Targets within DRC. Methodology/Principal findingsIn 2015-2017, pre- and post-intervention tsetse abundance data were collected from 1,234 unique locations across three neighbouring Health Zones (Yasa Bonga, Mosango, Masi Manimba). Remotely sensed dry season data were combined with pre-intervention tsetse presence/absence data from 332 locations within a species distribution modelling framework to produce a habitat suitability map. The impact of Tiny Targets on the tsetse population was then evaluated by fitting a generalised linear mixed model to the relative fly abundance data collected from 889 post-intervention monitoring sites within Yasa Bonga, with habitat suitability, proximity to the intervention and intervention duration as covariates. Immediately following the introduction of the intervention, we observe a dramatic reduction in fly catches by > 85% (pre-intervention: 0.78 flies/trap/day, 95% CI 0.676-0.900; 3 month post-intervention: 0.11 flies/trap/day, 95% CI 0.070-0.153) which is sustained throughout the study period. Declines in catches were negatively associated with proximity to Tiny Targets, and while habitat suitability is positively associated with abundance its influence is reduced in the presence of the intervention. Conclusions/SignificanceThis study adds to the body of evidence demonstrating the impact of Tiny Targets on tsetse across a range of ecological settings, and further characterises the factors which modify its impact. The habitat suitability maps have the potential to guide the expansion of tsetse control activities in this area. Authors SummaryThere have been large declines in the number of cases of sleeping sickness as a result of programmes that actively screen and treat the at-risk population. Additional control is needed in areas where the disease persists such as parts of the Democratic Republic of Congo (DRC). The disease is transmitted by tsetse flies, and reducing the tsetse population using Tiny Targets has been shown to control the disease in other countries. Extensive tsetse monitoring has been undertaken in one Health Zone in DRC where Tiny Targets have been deployed. We used these data to gain a better understanding of tsetse habitat, to produce habitat suitability maps, and to subsequently measure the impact of Tiny Targets on the tsetse population. We show that tsetse flies are largely found along rivers and surrounding densely vegetated habitat, with there being a positive relationship between habitat suitability and the number of flies caught. Once Tiny Targets were introduced, the number of flies caught in monitoring traps decreased by >85%, with habitat suitability at the trap location, and the proximity of the trap to the nearest Tiny Target influencing the size of the effect of the intervention. This study adds to the body of evidence demonstrating the impact of Tiny Targets on tsetse distribution in addition to providing information that can be used to guide the expansion of tsetse control activities in this area.

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bioRxiv2019

Quantifying geographic accessibility to improve cost-effectiveness of entomological monitoring.

BackgroundVector-borne diseases are important causes of mortality and morbidity in humans and livestock, particularly for poorer communities and countries in the tropics. Large-scale programs against these diseases, for example malaria, dengue and African trypanosomiasis, include vector control, and assessing the impact of this intervention requires frequent and extensive monitoring of disease vector abundance. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases are low. Methodology/Principal FindingsWe developed a system that allows the identification of monitoring sites where pre-intervention densities of vectors are predicted to be high, and travel cost to sites is low, highlighting the most efficient locations for longitudinal monitoring. Using remotely sensed imagery and an image classification algorithm, we mapped landscape resistance associated with on- and off-road travel for every gridded location (3m and 0.5m grid cells) within Koboko district, Uganda. We combine the accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine the most efficient locations for longitudinal data collection. Our modelled predictions were validated against empirical measurements of travel-time and existing maps of road networks. We applied this approach in northern Uganda where a large-scale vector control program is being implemented to control human African trypanosomiasis, a neglected tropical disease (NTD) caused by trypanosomes transmitted by tsetse flies. Our accessibility surfaces indicate a high performance when compared to empirical data, with remote sensing identifying a further ~70% of roads than existing networks. Conclusions/SignificanceBy integrating such estimates with predictions of tsetse abundance, we propose a methodology to determine the optimal placement of sentinel monitoring sites for evaluating control programme efficacy, moving from a nuanced, ad-hoc approach incorporating intuition, knowledge of vector ecology and local knowledge of geographic accessibility, to a reproducible, quantifiable one. Author SummaryAssessing the impact of vector control programmes requires longitudinal measurements of the abundance of insect vectors within intervention areas. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases of disease are low. Efficient monitoring involves a prior selection of monitoring sites that are easy to reach and produce rich information on vector abundance. Here, we used image classification and cost-distance algorithms to produce estimates of accessibility within Koboko district, Uganda, where vector control is contributing to the elimination of sleeping sickness, a neglected tropical disease (NTD). We combine an accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine locations which are associated with low cost (lowest travel time) and potential for longitudinal data collection (high pre-intervention abundance). Our method could be adapted for use in the planning and monitoring of tsetse- and other vector-control programmes. By providing methods to ensure that vector control programmes operate at maximum efficiency, we can ensure that the limited funding associated with some of these NTDs has the largest impact.

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