Adding tsetse control to medical activities contributes to decreasing transmission of sleeping sickness in the Mandoul focus (Chad)

Abstract

Background: Gambian sleeping sickness or HAT (human African trypanosomiasis) is a neglected tropical disease caused by Trypanosoma brucei gambiense transmitted by riverine species of tsetse. A global programme aims to eliminate the disease as a public health problem by 2020 and stop transmission by 2030. In the South of Chad, the Mandoul area is a persistent focus of Gambian sleeping sickness where around 100 HAT cases were still diagnosed and treated annually until 2013. Pre-2014, control of HAT relied solely on case detection and treatment, which lead to a gradual decrease in the number of cases of HAT due to annual screening of the population.

Methods: Because of the persistence of transmission and detection of new cases, we assessed whether the addition of vector control to case detection and treatment could further reduce transmission and consequently, reduce annual incidence of HAT in Mandoul. In particular, we investigated the impact of deploying 'tiny targets' which attract and kill tsetse. Before tsetse control commenced, a census of the human population was conducted and their settlements mapped. A pre-intervention survey of tsetse distribution and abundance was implemented in November 2013 and 2600 targets were deployed in the riverine habitats of tsetse in early 2014, 2015 and 2016. Impact on tsetse and on the incidence of sleeping sickness was assessed through nine tsetse monitoring surveys and four medical surveys of the human population in 2014 and 2015. Mathematical modelling was used to assess the relative impact of tsetse control on incidence compared to active and passive screening.

Findings: The census indicated that a population of 38674 inhabitants lived in the vicinity of the Mandoul focus. Within this focus in November 2013, the vector is Glossina fuscipes fuscipes and the mean catch of tsetse from traps was 0.7 flies/trap/day (range, 0-26). The catch of tsetse from 44 sentinel biconical traps declined after target deployment with only five tsetse being caught in nine surveys giving a mean catch of 0.005 tsetse/trap/day. Modelling indicates that 70.4% (95% CI: 51-95%) of the reduction in reported cases between 2013 and 2015 can be attributed to vector control with the rest due to medical intervention. Similarly tiny targets are estimated to have reduced new infections dramatically with 62.8% (95% CI: 59-66%) of the reduction due to tsetse control, and 8.5% (95% 8-9%) to enhanced passive detection. Model predictions anticipate that elimination as a public health problem could be achieved by 2018 in this focus if vector control and screening continue at the present level and, furthermore, there may have been virtually no transmission since 2015.

Conclusion: This work shows that tiny targets reduced the numbers of tsetse in this focus in Chad, which may have interrupted transmission and the combination of tsetse control to medical detection and treatment has played a major role in reducing in HAT incidence in 2014 and 2015.

Fig 1. A tiny target suspended from a branch.

Tsetse collide with the deltamethrin-treated target and pick up a lethal dose of insecticide [3, 8].

Fig 2. Location of the study area and distribution of human population, tsetse flies and tiny targets in the study area.

This map shows the entire Mandoul focus, with the Mandoul river in blue. Target deployment started in the middle (just before the river separation in 3 branches) and the targets were mainly deployed on the two left branches.

Fig 3. Impact of tiny targets on catch of tsetse.

Black box and whiskers (with outliers shown as circles) show the numbers of tsetse caught per trap during each entomological survey (T0-T9). When no tsetse were caught during a survey, boxes appear as horizontal bars. Red bars indicate deployment of tiny targets.

Fig 4. Inferred level of underlying transmission from modelling.

The grey box plots show the estimated numbers of new infections in humans for each year (2000–2013). Coloured boxes denote the predicted number of new infections from 2014 onwards under four different strategies including combinations of either basic or enhanced medical strategies and vector control. Improved medical strategies had increases to the passive detection and reporting rates from 2015, whilst vector control strategies began in 2014. Purple boxes denote the strategy that took place with both improvements to passive screening and tsetse control.

Fig 5. Model fit to data.

The top graph shows the annual level of active screening achieved in the focus. The middle and bottom graphs show the observed active and passive case detections as a solid grey line with the model fit (years 2000–2013) displayed as grey box and whisker plots. The model projections of the four strategies for 2014 onwards are shown as coloured box plots and include combinations of either basic or enhanced medical strategies and vector control. Improved medical strategies had increases to the passive detection and reporting rates from 2015, whilst vector control strategies began in 2014. Purple boxes denote the strategy that took place with both improvements to passive screening and tsetse control.