ArticleBMC infectious diseases2026
High-resolution mapping of onchocerciasis risk in Ghana using spatial machine learning models.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOnchocerciasis, caused by Onchocerca volvulus and transmitted through repeated bites of infected blackflies, remains endemic in several sub-Saharan African countries, including Ghana. Despite long-term ivermectin (IVM) mass drug administration (MDA), transmission persists, particularly in ecologically complex regions. Although geospatial models have advanced the identification of high-risk areas, nationwide fine-scale gridded maps for high-risk areas do not yet exist in Ghana.
methodsCommunities with documented infections were extracted from published studies and subsequently geocoded. Raster datasets representing environmental, climatic, and healthcare accessibility variables were generated using remote sensing platforms and global data repositories. The predictive models employed encompassed logistic regression, random forest, and gradient boosting machines.
resultsTraditional and penalized logistic regression models yielded balanced accuracies of 63.17% and 53.56%, respectively, with areas under the receiver operating characteristic (ROC) curve (AUC) of 71.11% and 70.90%, respectively. The random forest model achieved a balanced accuracy of 70.30% and the highest AUC of 76.40%, along with a favorable sensitivity of 58.97%. Gradient boosting machines achieved a balanced accuracy of 61.69%, an AUC of 71.60%, and a sensitivity of 50.00%. Among all models, the random forest performed best. Based on predictions from the random forest model, high-risk regions were consistently identified in the central-west, the Volta Lake area, the southwest, and the central north. River density (relative risk [RR] = 7.99; 95% confidence interval [CI]: 3.16-20.19; p < 0.0001) was associated with increased transmission risk, whereas higher land surface temperatures (RR = 0.06; 95% CI: 0.02-0.20; p < 0.0001) and dense vegetation cover (RR = 0.19; 95% CI: 0.06-0.61; p = 0.005) correlated with reduced transmission risk.
conclusionsThese findings underscore the need for targeted vector control, improved healthcare access, and integrated social interventions. Generated risk maps provide valuable tools for adaptive strategies toward the World Health Organization's 2030 elimination targets.
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.