Predictive Modeling of Localized Mobile App to Improve Snakebite Management in Ghana

Journal article

Background: Snakebite 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. Methods: A 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. Results: The 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. Conclusions: This 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.

Authors: Nyarko, E; Uppal, A; Hamman, NA; Agyekum, L; Antwi, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Neglected Tropical Diseases
• Volume: 20
• Issue: 6
• Pages: e0014435
• Article number: e0014435
• Publication date: 2026-06-08
• Acceptance date: 2026-06-03
• DOI: 10.1371/journal.pntd.0014435
• EISSN: 1935-2735
• ISSN: 1935-2727
• Language: English
• Keywords: Mobile apps; Mobile device; Health care; mHealth; Smartphone app; Focus group; Public health; Confidence interval