Evidence map›Paper›PMID 25890278›Full record

ReviewParasites & vectors2015

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook.

Yvonne Walz, Martin Wegmann, Stefan Dech, Giovanna Raso, Jürg Utzinger

Open access · goldAbstract readReview
In one paragraph

Review in Parasites & vectors, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 4 pooled it
7.9field-weighted citation impact, top 2% of its field
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

45 citing papers in PubMed, 4 syntheses or guidelines pooled it, 93 citations in OpenAlex.

  1. Pooled it
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  4. Pooled it
  5. Article
  6. Co-infection withParasite epidemiology and control · 2026
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  16. Schistosomiasis with a Focus on Africa.Tropical medicine and infectious disease · 2021
    Review
  17. Editorial: Trematode Infection in Ruminants.Frontiers in veterinary science · 2021
    Article
  18. Molecular Tools and Schistosomiasis Transmission Elimination.The American journal of tropical medicine and hygiene · 2020
    Review
  19. Article
  20. 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

5 authors at 2 institutions in 3 countries.

Yvonne WalzDepartment of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg, Germany. yvonne.walz@uni-wuerzburg.de.
Martin WegmannDepartment of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg, Germany. martin.wegmann@uni-wuerzburg.de.
Stefan DechDepartment of Remote Sensing, Institute for Geography and Geology, University of Würzburg, Würzburg, Germany. stefan.dech@dlr.de.
Giovanna RasoDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland. giovanna.raso@unibas.ch.
Jürg UtzingerDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland. juerg.utzinger@unibas.ch.
University of Würzburg · DESwiss Tropical and Public Health Institute · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSchistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions.

methodsWe reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised.

resultsWe found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from.

conclusionsOur findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.

Indexed as

Remote Sensing TechnologyAfrica South of the SaharaAnimalsHumansRisk FactorsSchistosomiasis

Identifiers

PMID25890278
PMCPMC4406176
OpenAlexW2103896085

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.