Evaluating long-term effectiveness of sleeping sickness control measures in Guinea

Abstract

BackgroundHuman African Trypanosomiasis threatens human health across Africa. The subspecies T.b. gambiense is responsible for the vast majority of reported HAT cases. Over the past decade, expanded control efforts accomplished a substantial reduction in HAT transmission, spurring the WHO to include Gambian HAT on its roadmap for 2020 elimination. To inform the implementation of this elimination goal, we evaluated the likelihood that current control interventions will achieve the 2020 target in Boffa prefecture in Guinea, which has one of the highest prevalences for HAT in the country, and where vector control measures have been implemented in combination with the traditional screen and treat strategy.MethodsWe developed a three-species mathematical model of HAT and used a Bayesian melding approach to calibrate the model to epidemiological and entomological data from Boffa. From the calibrated model, we generated the probabilistic predictions regarding the likelihood that the current HAT control programs could achieve elimination by 2020 in Boffa.ResultsOur model projections indicate that if annual vector control is implemented in combination with annual or biennial active case detection and treatment, the probability of eliminating HAT as public health problem in Boffa by 2020 is over 90%. Annual implementation of vector control alone has a significant impact but a decreased chance of reaching the objective (77%). However, if the ongoing control efforts are interrupted, HAT will continue to remain a public health problem. In the presence of a non-human animal transmission reservoir, intervention strategies must be maintained at high coverage, even after 2020 elimination, to prevent HAT reemerging as a public health problem.ConclusionsComplementing active screening and treatment with vector control has the potential to achieve the elimination target before 2020 in the Boffa focus. However, surveillance must continue after elimination to prevent reemergence.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1121-x) contains supplementary material, which is available to authorized users.

Sleeping Sickness at the Crossroads

“…This field has significantly developed against all odds in the past years: trypanosomiasis with its extremely focal distribution and the many external factors influencing its transmission has been a true headache over two decades for all modellers and predictive mappers. Studies of existing Gambiense-HAT models in a few foci (i.e., DRC, Guinea, and Chad) suggest that some type of additional infection reservoir is needed to match the observed dynamics of reported HAT cases [42,43]. This could arise from another human reservoir (including undiagnosed and latent infections), an animal reservoir, and/or heterogeneities in human risk exposure and surveillance coverage [39].…”

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

“…In recent analyses, two research groups have independently addressed the feasibility of the WHO goal of elimination as a public health problem by 2020 under current strategies using mechanistic mathematical models [ 55 , 56 ]. Both models used differential equations to quantify stage 1 and 2 disease in humans, tsetse infection and possible animal reservoirs (Fig.…”

“…7 Schematic of HAT results. The results include a) quantitative estimates of the level of heterogeneity in human exposure and screening participation by Rock et al [ 56 ]; and b) an assessment of strategies combining both human screening and tsetse control by Pandey et al [ 55 ] …”

Village-scale persistence and elimination of gambiense human African trypanosomiasis

“…Gambiense sleeping sickness (gambiense human African trypanosomiasis, referred to 2 here as gHAT) is a tsetse-borne neglected tropical disease (NTD) caused by the 3 parasitic protozoa, Trypanosoma brucei gambiense. There has recently been a decline in 4 global cases, with just 1,420 cases reported in 2017, compared to 10,466 reported in 48 However, much of the modelling work on the gHAT infection dynamics has been done in 49 large populations using deterministic models, either for an entire regional infection focus 50 or at a health zone level (approximately 100,000 people) [8,15,17,20,21]. Here we The infection dynamics are described by a stochastic compartmental 100 Ross-Macdonald-type model [26][27][28][29] extended from the previous work of Rock et al [17] 101 September 9, 2019 4/33…”

“…The 121 distinction between teneral and non-teneral yet uninfected is used to capture the 122 observation that tsetse are far more susceptible to infection at their first blood meal 123 than at any subsequent blood meals [30]. The effect of a possible animal reservoir is not 124 considered, since its role remains unclear [15,17,20,31,42] and its inclusion does not 125 significantly improve the match between model outputs and currently available data in 126 this setting [17]. 127 Additional to the epidemiological and demographic processes, we simulate the effect 128 of active screening and passive detection of cases.…”