ArticlePLoS neglected tropical diseases2026
Predictive Modeling of Localized Mobile App to Improve Snakebite Management in Ghana.
Article in PLoS neglected tropical 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSnakebite envenoming (SBE) is a significant yet often overlooked public health crisis that primarily affects impoverished communities. Mobile applications (apps) can be effective tools for managing snakebites. To meet the World Health Organization's (WHO) goal of reducing SBE by 50% by 2030, app developers must consider regional users' preferences to ensure their apps provide relevant and accurate information. This study predicted the importance of various functionalities of a mobile app for managing snakebites based on user preferences. Our objective was to provide quantitative evidence on which functions should be prioritized to inform the development of a customized local app to enhance snakebite care in Ghana.
methodsA cross-sectional survey using a quantitative statistical experiment design method was conducted to identify healthcare workers' preferences for vital mobile app functions for managing snakebites. Participants were selected from two deprived and predominantly rural districts in the Eastern region of Ghana through a multi-stage sampling technique. The attributes used in the questionnaire were developed based on literature reviews and focus group discussions, and a statistical block design was employed to create the choice tasks. To rigorously evaluate the performance of the machine learning (ML) models and reduce the risk of overfitting, we employed 5-fold cross-validation and multiple evaluation metrics. The data were analyzed using seven ML models.
resultsThe four most vital mobile app functions identified by the participants were "step-by-step assistance for victims and first responders" (utility estimates (β) =0.3924; 95% confidence interval (CI): 0.3183 to 0.4665), "providing educational and training materials" (β = 0.2243; 95% CI: 0.1500 to 0.2987), "identifying venomous snakes through clinical evidence or symptoms" (β = 0.1718; 95% CI: 0.0982 to 0.2454) and "identifying venomous snake biodiversity in your area/region" (β = 0.0898; 95% CI: 0.0176 to 0.1620). Conversely, the app functions that were less favored included "platform for sharing snakebite treatment experiences" (β = -0.2503; 95% CI: -0.3268 to -0.1738) and "designing educational games about snakes" (β = -0.3995; 95% CI: -0.4737 to -0.3252). These findings align with subgroup analyses by gender, suggesting a consistent understanding of needs across different demographic groups.
conclusionsThis study provides quantitative evidence on which mobile app functions should be prioritized to inform the development of a customized local app to improve management and care for snakebite victims in Ghana and other sub-Saharan African countries.
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.