Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT)
Open Access
- 7 October 2019
- journal article
- Published by F1000 Research Ltd in Gates Open Research
- Vol. 3, 1553
- https://doi.org/10.12688/gatesopenres.13070.1
Abstract
Gambiense human African trypanosomiasis (gHAT) is a parasitic, vector-borne neglected tropical disease that has historically affected populations across West and Central Africa and can result in death if untreated. Following from the success of recent intervention programmes against gHAT, the World Health Organization (WHO) has defined a 2030 goal of global elimination of transmission (EOT). The key proposed indicator to measure achievement of the goal is to have zero reported cases. Results of previous mathematical modelling and quantitative analyses are brought together to explore both the implications of the proposed indicator and the feasibility of achieving the WHO goal. Whilst the indicator of zero case reporting is clear and measurable, it is an imperfect proxy for EOT and could arise either before or after EOT is achieved. Lagging reporting of infection and imperfect diagnostic specificity could result in case reporting after EOT, whereas the converse could be true due to underreporting, lack of coverage, and cryptic human and animal reservoirs. At the village-scale, the WHO recommendation of continuing active screening until there are three years of zero cases yields a high probability of local EOT, but extrapolating this result to larger spatial scales is complex. Predictive modelling of gHAT has consistently found that EOT by 2030 is unlikely across key endemic regions if current medical-only strategies are not bolstered by improved coverage, reduced time to detection and/or complementary vector control. Unfortunately, projected costs for strategies expected to meet EOT are high in the short term and strategies that are cost-effective in reducing burden are unlikely to result in EOT by 2030. Future modelling work should aim to provide predictions while taking into account uncertainties in stochastic dynamics and infection reservoirs, as well as assessment of multiple spatial scales, reactive strategies, and measurable proxies of EOT.Funding Information
- Medical Research Council
- Engineering and Physical Sciences Research Council
- Bill and Melinda Gates Foundation (OPP1184344, OPP1177824)
This publication has 25 references indexed in Scilit:
- Assessing the impact of data aggregation in model predictions of HAT transmission and control activitiesPublished by Cold Spring Harbor Laboratory ,2019
- Monitoring the elimination of human African trypanosomiasis: Update to 2016PLoS Neglected Tropical Diseases, 2018
- The impact of passive case detection on the transmission dynamics of gambiense Human African TrypanosomiasisPLoS Neglected Tropical Diseases, 2018
- Impact of the Ebola outbreak on Trypanosoma brucei gambiense infection medical activities in coastal Guinea, 2014-2015: A retrospective analysis from the Guinean national Human African Trypanosomiasis control programPLoS Neglected Tropical Diseases, 2017
- Adding tsetse control to medical activities contributes to decreasing transmission of sleeping sickness in the Mandoul focus (Chad)PLoS Neglected Tropical Diseases, 2017
- Monitoring the elimination of human African trypanosomiasis: Update to 2014PLoS Neglected Tropical Diseases, 2017
- Data-driven models to predict the elimination of sleeping sickness in former Equateur province of DRCEpidemics, 2017
- Predicting the Impact of Intervention Strategies for Sleeping Sickness in Two High-Endemicity Health Zones of the Democratic Republic of CongoPLoS Neglected Tropical Diseases, 2017
- Quantitative evaluation of the strategy to eliminate human African trypanosomiasis in the Democratic Republic of CongoParasites & Vectors, 2015
- Monitoring the Progress towards the Elimination of Gambiense Human African TrypanosomiasisPLoS Neglected Tropical Diseases, 2015