Predictive Modeling of Localized Mobile App to Improve Snakebite Management in Ghana
Eric Nyarko, Aashna Uppal, Nicholas Amani Hamman, Louis Agyekum, Pascal Antwi, Nuhu Mohammed, Obu-Amoah Ampomah, Iddrisu Abugbil Atubiga, Trudie Lang · 2026
Read Predictive Modeling of Localized Mobile App to Improve Snakebite Management in Ghana by Eric Nyarko, Aashna Uppal, Nicholas Amani Hamman, Louis Agyekum, Pascal Antwi, Nuhu Mohammed, Obu-Amoah Ampomah, Iddrisu Abugbil Atubiga, Trudie Lang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13262930.txt.
Description
Eric Nyarko and colleagues ask Ghanaian health workers what a snakebite phone app must do first. Snakebite envenoming devastates poor rural communities, and apps could coach care where specialists are scarce. Their 2026 survey in two deprived eastern districts feeds seven machine learning models with stated preferences.
Step-by-step guidance for victims and first responders topped the wish list by a clear margin. Educational content also ranked among the vital functions participants chose. The World Health Organization wants snakebite harm halved by twenty thirty, and local fit decides whether tools get used. Their rankings give developers a priority order grounded in end users, not guesses.
Catalog record
| CREATOR | Eric Nyarko, Aashna Uppal, Nicholas Amani Hamman, Louis Agyekum, Pascal Antwi, Nuhu Mohammed, Obu-Amoah Ampomah, Iddrisu Abugbil Atubiga, Trudie Lang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |