ArticlePLoS neglected tropical diseases2018
Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.
Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 50 citations in OpenAlex.
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- Determining spatio-temporal distribution and hotspots for female schistosomiasis in Ghana, 2020-2024: a call for increased public health action against a neglected tropical disease.Infectious diseases of poverty · 2026Article
- Stakeholders perspective of integrating female genital schistosomiasis into HIV care: A qualitative study in Ghana.PLoS neglected tropical diseases · 2025Article
- A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi.Parasites & vectors · 2024Article
- Enabling targeted mass drug administration for schistosomiasis in north-western Tanzania: Exploring the use of geostatistical modeling to inform planning at sub-district level.PLoS neglected tropical diseases · 2024Article
- How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana.PLoS neglected tropical diseases · 2023Article
- Article
- Persistent Vulvar Itch Unresponsive to Treatment: A Case of Vulvar Schistosomiasis Caused byCase reports in infectious diseases · 2023Article
- Malaria, Urogenital Schistosomiasis, and Anaemia in Pregnant Ghanaian Women.Journal of parasitology research · 2023Article
- Intestinal Helminth Infections in Ghanaian Children from the Ashanti Region between 2007 and 2008-A Retrospective Cross-Sectional Real-Time PCR-Based Assessment.Tropical medicine and infectious disease · 2022Article
- Models for predicting bulinids species habitats in southwestern Nigeria.Parasite epidemiology and control · 2022Article
- Screening forPathogens (Basel, Switzerland) · 2022Article
- Schistosomiasis with a Focus on Africa.Tropical medicine and infectious disease · 2021Review
- Schistosoma haematobium infection and environmental factors in Southwestern Tanzania: A cross-sectional, population-based study.PLoS neglected tropical diseases · 2020Article
- Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches.PLoS neglected tropical diseases · 2020Article
- Prediction of hookworm prevalence in southern India using environmental parameters derived from Landsat 8 remotely sensed data.International journal for parasitology · 2020Article
- Freshwater snails of biomedical importance in the Niger River Valley: evidence of temporal and spatial patterns in abundance, distribution and infection with Schistosoma spp.Parasites & vectors · 2019Article
- Contextualizing Schistosoma haematobium transmission in Ghana: Assessment of diagnostic techniques and individual and community water-related risk factors.Acta tropica · 2019Article
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Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundSchistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. METHODOLOGY: Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. PRINCIPAL
findingsPredictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. CONCLUSIONS/SIGNIFICANCE: The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.
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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.