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Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis

Modelling Consortium Discussion Group on Gambiense Human African, N. T. D.. (2020) Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis. Gates open research, 3. p. 1553.

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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 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.
Keywords
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Household Economics and Health Systems Research > Health Systems and Policy (Tediosi)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Disease Dynamics > Mathematical Epidemiology (Chitnis)
UniBasel Contributors:CastaƱo, Soledad and Chitnis, Nakul and Antillon, Marina and Tediosi, Fabrizio
Item Type:Article, refereed
Article Subtype:Further Journal Contribution
Publisher:Bill and Melinda Gates Foundation
ISSN:2572-4754
Note:Publication type according to Uni Basel Research Database: Journal item
Language:English
edoc DOI:
Last Modified:27 Apr 2020 07:00
Deposited On:27 Apr 2020 07:00

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