ArticlePLoS neglected tropical diseases2019
Village-scale persistence and elimination of gambiense human African trypanosomiasis.
Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 38 citations in OpenAlex.
- Modelling timelines to elimination of sleeping sickness in the Democratic Republic of Congo, accounting for possible cryptic human and animal transmission.Parasites & vectors · 2024Article
- Elimination of human African trypanosomiasis: The long last mile.PLoS neglected tropical diseases · 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
- Discovery of an orally active nitrothiophene-based antitrypanosomal agent.European journal of medicinal chemistry · 2024Article
- Towards the sustainable elimination of gambiense human African trypanosomiasis in Côte d'Ivoire using an integrated approach.PLoS neglected tropical diseases · 2023Article
- Does a One Health approach to human African trypanosomiasis control hasten elimination? A stochastic compartmental modeling approach.Acta tropica · 2023Article
- Identifying regions for enhanced control of gambiense sleeping sickness in the Democratic Republic of Congo.Nature communications · 2022Article
- ModellingJournal of the Royal Society, Interface · 2021Article
- Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness.PLoS computational biology · 2021Article
- Modelling to Quantify the Likelihood that Local Elimination of Transmission has Occurred Using Routine Gambiense Human African Trypanosomiasis Surveillance Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 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
- Need of entomological criteria to assess zero transmission of gambiense HAT.PLoS neglected tropical diseases · 2021Review
- Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.PLoS computational biology · 2021Article
- Estimating the distribution of time to extinction of infectious diseases in mean-field approaches.Journal of the Royal Society, Interface · 2020Article
- Vinyl sulfone-based inhibitors of trypanosomal cysteine protease rhodesain with improved antitrypanosomal activities.Bioorganic & medicinal chemistry letters · 2020Article
- Screening Strategies for a Sustainable Endpoint for Gambiense Sleeping Sickness.The Journal of infectious diseases · 2020Article
- The impact of vector migration on the effectiveness of strategies to control gambiense human African trypanosomiasis.PLoS neglected tropical diseases · 2019Article
- Laboratory evaluation of molecular xenomonitoring using mosquito and tsetse fly excreta/feces to amplifyGates open research · 2019Article
- Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT).Gates open research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gambiense human African trypanosomiasis (gHAT) is one of several neglected tropical diseases that is targeted for elimination by the World Health Organization. Recent years have seen a substantial decline in the number of globally reported cases, largely driven by an intensive process of screening and treatment. However, this infection is highly focal, continuing to persist at low prevalence even in small populations. Regional elimination, and ultimately global eradication, rests on understanding the dynamics and persistence of this infection at the local population scale. Here we develop a stochastic model of gHAT dynamics, which is underpinned by screening and reporting data from one of the highest gHAT incidence regions, Kwilu Province, in the Democratic Republic of Congo. We use this model to explore the persistence of gHAT in villages of different population sizes and subject to different patterns of screening. Our models demonstrate that infection is expected to persist for long periods even in relatively small isolated populations. We further use the model to assess the risk of recrudescence following local elimination and consider how failing to detect cases during active screening events informs the probability of elimination. These quantitative results provide insights for public health policy in the region, particularly highlighting the difficulties in achieving and measuring the 2030 elimination goal.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.