Trypa-NO! contributes to the elimination of gambiense human African trypanosomiasis by combining tsetse control with “screen, diagnose and treat” using innovative tools and strategies

Ndung’u, Joseph Mathu; Boulangé, Alain; Picado, Albert; Mugenyi, Albert; Mortensen, Allan; Hope, Andrew; Mollo, Brahim Guihini; Bucheton, Bruno; Wamboga, Charles; Waiswa, Charles; Kaba, Dramane; Matovu, Enock; Courtin, Fabrice; Garrod, Gala; Gimonneau, Geoffrey; Bingham, Georgina; Hassane, Hassane Mahamat; Tirados, Iñaki; Saldanha, Isabel; Kaboré, Jacques; Rayaissé, Jean-Baptiste; Bart, Jean-Mathieu; Lingley, Jessica K.; Esterhuizen, Johan; Longbottom, Joshua; Pulford, Justin; Kouakou, Lingué; Sanogo, Lassina; Cunningham, Lucas J.; Camara, Mamadou; Koffi, Mathurin; Stanton, Michelle C.; Lehane, Mike; Kagbadouno, Moise S.; Camara, Oumou; Bessell, Paul R.; Péka, Mallaye; Solano, Philippe; Selby, Richard; Dunkley, Sophie; Torr, Stephen J.; Biéler, Sylvain; Lejon, Veerle; Jamonneau, Vincent; Yoni, Wilfried; Katz, Zachary

doi:10.1371/journal.pntd.0008738

Cited by 33 publications

(47 citation statements)

References 21 publications

(28 reference statements)

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“…A comparison showing the accessibility to diagnostic facilities is given in Fig 5, detailing accessibility to all 170 facilities in operation during 2017 (Fig 5A and 5B) and the scaled back number of 51 facilities operating from late 2018 [47], with the optimal placement determined through our approach (Fig 5C and 5D). To aid interpretation, we visualise the cost-distance PLOS NEGLECTED TROPICAL DISEASES surfaces on a continuous scale (Fig 5A and 5C), and on a categorical scale, with travel time binned by 30 minute increments (Fig 5B and 5D).…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Longbottom

Wamboga

Bessell³

et al. 2021

PLoS Negl Trop Dis

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Background Surveillance is an essential component of global programs to eliminate infectious diseases and avert epidemics of (re-)emerging diseases. As the numbers of cases decline, costs of treatment and control diminish but those for surveillance remain high even after the ‘last’ case. Reducing surveillance may risk missing persistent or (re-)emerging foci of disease. Here, we use a simulation-based approach to determine the minimal number of passive surveillance sites required to ensure maximum coverage of a population at-risk (PAR) of an infectious disease. Methodology and principal findings For this study, we use Gambian human African trypanosomiasis (g-HAT) in north-western Uganda, a neglected tropical disease (NTD) which has been reduced to historically low levels (<1000 cases/year globally), as an example. To quantify travel time to diagnostic facilities, a proxy for surveillance coverage, we produced a high spatial-resolution resistance surface and performed cost-distance analyses. We simulated travel time for the PAR with different numbers (1–170) and locations (170,000 total placement combinations) of diagnostic facilities, quantifying the percentage of the PAR within 1h and 5h travel of the facilities, as per in-country targets. Our simulations indicate that a 70% reduction (51/170) in diagnostic centres still exceeded minimal targets of coverage even for remote populations, with >95% of a total PAR of ~3million individuals living ≤1h from a diagnostic centre, and we demonstrate an approach to best place these facilities, informing a minimal impact scale back. Conclusions Our results highlight that surveillance of g-HAT in north-western Uganda can be scaled back without substantially reducing coverage of the PAR. The methodology described can contribute to cost-effective and equable strategies for the surveillance of NTDs and other infectious diseases approaching elimination or (re-)emergence.

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Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Longbottom

Wamboga

Bessell³

et al. 2021

PLoS Negl Trop Dis

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“…For this simulation, when given a fixed number of facilities, , we wish to identify the optimal subset of facilities, from the 170 available which results in the highest percentage of the PAR within 1-hour and 5-hour travel. N'dungu et al (46), detail in their work reporting on the Trypa-NO! project within north-western Uganda, that there is funding available to retain a maximum of 51 g-HAT diagnostic facilities from the 170 facilities currently operational.…”

Section: Deriving the Optimal Placement Of Facilities When The Facilimentioning

confidence: 99%

“…As a baseline for which to assess the impact of a scale back on the accessibility of the PAR, we first generated estimates of accessibility to all 170 facilities which were operational in 2017 ( To demonstrate the ability of this approach to identify the optimal placement of facilities when the facility number is known, either as derived from the above simulation, or pre-defined, we generated 10,000 estimates of travel time to facilities under varying selection combinations of 51 facilities, the number operating from late 2018 onwards (46).…”

Section: Deriving the Optimal Placement Of Facilities When The Facilimentioning

confidence: 99%

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Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Longbottom

Wamboga²,

Bessell

et al. 2020

Preprint

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Background: Surveillance is an essential component of global programs to eliminate infectious diseases, with surveillance systems often being considered the first line in averting epidemics for (re-)emerging diseases. As the numbers of cases decline, costs of treatment and control diminish, but those for surveillance remain high even after the ‘last’ case. Economies made by reducing surveillance risk missing persistent or (re-)emerging foci of disease, therefore it is vital that surveillance networks are adequately designed; however, guidance on how best to optimise disease surveillance in an elimination setting is lacking. Here, we use a simulation-based approach to determine the minimal number of passive surveillance sites required to ensure maximum coverage of the population at-risk (PAR) of an infectious disease. Methodology and Principal Findings: For this study, we use Gambian human African trypanosomiasis (g-HAT) in north-western Uganda, a neglected tropical disease (NTD) which has been reduced to historically low levels (<1000 cases/year), as an example application. To quantify travel time to diagnostic facilities, a proxy for surveillance coverage, we produced a high spatial-resolution friction surface and performed cost-distance analyses. We simulated travel time for the PAR with different numbers (n = 1...170) and locations (170,000 total placement combinations) of diagnostic facilities, quantifying the percentage of the PAR within 1h and 5h travel of the facilities, as per in-country targets. Our simulations indicate that a minimum of 54 diagnostic centres are required to meet provisional targets, and we determine where best to place these facilities, enabling a minimal impact scale back from 170 facilities operational in 2017. Scaling back operational facilities ensures that costs can be reduced without impairing surveillance of the core areas with remaining cases. Conclusions: Our results highlight that surveillance of g-HAT in north-western Uganda can be scaled back without reducing coverage of the PAR. The methodology described can contribute to cost-effective strategies for the surveillance of NTDs and other infectious diseases approaching elimination or applied to (re-)emerging diseases for which the design of a novel surveillance network is required.

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“…The technology is in use in Chad, Côte d'Ivoire, Guinea and Uganda and has been shown to significantly reduce tsetse populations and disease incidence. 4 In 2015, tiny targets were introduced to the DRC through a pilot project between the Liverpool School of Tropical Medicine (LSTM) and the Programme National de Lutte contre la Tryaponosomiase Humaine Africaine (PNLTHA). Working initially in the health zone of Yasa Bonga in the former Province of Bandundu, tiny targets were used to reduce tsetse densities by >85%, 5 leading to a scale-up of vector control activities to complement medical interventions in 11 health zones, increasing coverage from approximately 2000 km 2 to approximately 12 000 km 2 .…”

Section: Introductionmentioning

confidence: 99%

Demonstrating the sustainability of capacity strengthening amidst COVID-19

Abomo¹,

Miaka²,

Crossman

et al. 2021

International Health

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The global disruptions caused by the coronavirus disease 2019 crisis posed a threat to the momentum the vector control team at the Liverpool School of Tropical Medicine (LSTM) and the Programme National de Lutte contre la Tryaponosomiase Humaine Africaine (PNLTHA) had built in their efforts to control tsetse fly populations in the Democratic Republic of Congo. But despite the pandemic and global lockdown, field activities did continue and the same impressive results in tsetse fly reduction were observed and the team followed this by completing a round of ‘tiny target’ deployment without any external presence. Such a success was possible due to the investment in vector control capacity strengthening undertaken by the LSTM and PNLTHA.

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Trypa-NO! contributes to the elimination of gambiense human African trypanosomiasis by combining tsetse control with “screen, diagnose and treat” using innovative tools and strategies

Cited by 33 publications

References 21 publications

Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Optimising passive surveillance of a neglected tropical disease in the era of elimination: A modelling study

Demonstrating the sustainability of capacity strengthening amidst COVID-19

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