ArticlePLoS neglected tropical diseases2020
Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC).
Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Modelling timelines to elimination of sleeping sickness in the Democratic Republic of Congo, accounting for possible cryptic human and animal transmission.Parasites & vectors · 2024Article
- Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC.PLoS computational biology · 2024Article
- Evaluating and mitigating the potential indirect effect of COVID-19 on control programmes for seven neglected tropical diseases: a modelling study.The Lancet. Global health · 2022Article
- Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC.PLoS neglected tropical diseases · 2022Article
- Synthesis and biological evaluation of imidamide analogs as selective anti-trypanosomal agents.Bioorganic & medicinal chemistry · 2022Article
- Identifying regions for enhanced control of gambiense sleeping sickness in the Democratic Republic of Congo.Nature communications · 2022Article
- Update of transmission modelling and projections of gambiense human African trypanosomiasis in the Mandoul focus, Chad.Infectious diseases of poverty · 2022Article
- Modelling the impact of fexinidazole use on human African trypanosomiasis (HAT) transmission in the Democratic Republic of the Congo.PLoS neglected tropical diseases · 2021Article
- Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness.PLoS computational biology · 2021Article
- Strengthening data collection for neglected tropical diseases: What data are needed for models to better inform tailored intervention programmes?PLoS neglected tropical diseases · 2021Article
- Cost-effectiveness modelling to optimise active screening strategy for gambiense human African trypanosomiasis in endemic areas of the Democratic Republic of Congo.BMC medicine · 2021Article
- Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.PLoS computational biology · 2021Article
- Screening Strategies for a Sustainable Endpoint for Gambiense Sleeping Sickness.The Journal of infectious diseases · 2020Article
- Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT).Gates open research · 2019Article
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14 authors.
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Abstract
Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies.
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