Evidence map›Paper›PMID 38033142›Full record

ArticlePloS one2023

Modelling the historical distribution of schistosomiasis-transmitting snails in South Africa using ecological niche models.

Nisa Ayob, Roelof P Burger, Monray D Belelie, Ncobile C Nkosi, Henno Havenga, Lizaan de Necker, Dirk P Cilliers

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Impact of Climate Change on Schistosomiasis Transmission and Distribution-Scoping Review.International journal of environmental research and public health · 2025
    Article
  9. Distribution Patterns ofEcology and evolution · 2025
    Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nisa AyobUnit for Environmental Sciences and Management, North-West University, Mafikeng Campus, Mafikeng, South Africa.ORCID 0000-0001-9616-6395
Roelof P BurgerUnit for Environmental Sciences and Management, North-West University, Potchefstroom Campus, Potchefstroom, South Africa.
Monray D BelelieUnit for Environmental Sciences and Management, North-West University, Potchefstroom Campus, Potchefstroom, South Africa.
Ncobile C NkosiUnit for Environmental Sciences and Management, North-West University, Mafikeng Campus, Mafikeng, South Africa.
Henno HavengaUnit for Environmental Sciences and Management, North-West University, Potchefstroom Campus, Potchefstroom, South Africa.
Lizaan de NeckerSouth African Institute for Aquatic Biodiversity (NRF-SAIAB), Makhanda, South Africa.
Dirk P CilliersUnit for Environmental Sciences and Management, North-West University, Potchefstroom Campus, Potchefstroom, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Schistosomiasis is a vector-borne disease transmitted by freshwater snails and is prevalent in rural areas with poor sanitation and no access to tap water. Three snail species are known to transmit schistosomiasis in South Africa (SA), namely Biomphalaria pfeifferi, Bulinus globosus and Bulinus africanus. In 2003, a predicted prevalence of 70% was reported in tropical climates in SA. Temperature and rainfall variability can alter schistosomiasis-transmitting snails' development by increasing or decreasing their abundance and geographical distribution. This study aimed to map the historical distribution of schistosomiasis from 1950 to 2006 in SA. The snail sampling data were obtained from the historical National Snail Freshwater Collection (NFSC). Bioclimatic variables were extracted using ERA 5 reanalysis data provided by the Copernicus Climate Change Service. In this study, we used 19 bioclimatic and four soil variables. The temporal aggregation was the mean climatological period pre-calculated over the 40-year reference period with a spatial resolution of 0.5° x 0.5°. Multicollinearity was reduced by calculating the Variance Inflation Factor Core (VIF), and highly correlated variables (> 0.85) were excluded. To obtain an "ensemble" and avoid the integration of weak models, we averaged predictions using the True Skill Statistical (TSS) method. Results showed that the ensemble model achieved the highest Area Under the Curve (AUC) scores (0.99). For B. africanus, precipitation-related variables contributed to determining the suitability for schistosomiasis. Temperature and precipitation-related variables influenced the distribution of B. globosus in all three models. Biomphalaria pfeifferi showed that Temperature Seasonality (bio4) contributed the most (47%) in all three models. According to the models, suitable areas for transmitting schistosomiasis were in the eastern regions of South Africa. Temperature and rainfall can impact the transmission and distribution of schistosomiasis in SA. The results will enable us to develop future projections for Schistosoma in SA based on climate scenarios.

Indexed as

BiomphalariaSchistosomiasisAnimalsBulinusEcosystemSnailsSouth Africa

Identifiers

PMID38033142
PMCPMC10688899

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Registered trials

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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.