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

Eric Nyarko¹, Aashna Uppal², Nicholas Amani Hamman³

¹Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.

Plos Neglected Tropical Diseases

|June 8, 2026

Summary

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Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Predicting challenges and opportunities in improving snakebite envenoming management in Ghana.

Transactions of the Royal Society of Tropical Medicine and Hygiene·2026

Mobile apps can improve snakebite management. The most vital functions for healthcare workers in Ghana include step-by-step assistance, educational materials, and snake identification features.

Area of Science:

Public Health
Digital Health
Medical Informatics

Background:

Snakebite envenoming (SBE) is a neglected global health issue disproportionately affecting impoverished communities.
Mobile applications offer a promising avenue for enhancing snakebite management and care.
Developing region-specific apps is crucial for meeting the World Health Organization's goal to reduce SBE by 50% by 2030.

Purpose of the Study:

To quantitatively assess user preferences for mobile application functionalities in snakebite management.
To provide evidence-based recommendations for developing a customized snakebite management app in Ghana.
To inform the design of digital health tools for improving snakebite care in sub-Saharan Africa.

Main Methods:

A cross-sectional survey utilizing a quantitative statistical experiment design was conducted.

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Healthcare workers in rural Ghanaian districts were surveyed using a multi-stage sampling technique.

Machine learning models, including 5-fold cross-validation, were employed for data analysis.

Main Results:

The highest-rated functions were step-by-step assistance for victims/responders (β=0.3924), educational materials (β=0.2243), and identifying venomous snakes (β=0.1718).
Identifying local venomous snake biodiversity was also a prioritized function (β=0.0898).
Functions like sharing treatment experiences and educational games received lower utility estimates.

Conclusions:

Prioritizing specific functionalities in mobile apps can significantly enhance snakebite management.
The study provides quantitative data to guide the development of effective snakebite management tools in Ghana.
Findings are applicable to improving snakebite care in other sub-Saharan African countries.