A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana
The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers.
Authors
- Eduardo Fernández (ORCID: https://orcid.org/0000-0002-4354-1195)
- Eric Nana Yaw Nyarko (ORCID: https://orcid.org/0000-0002-1666-7489)
- José Marı́a Gutiérrez (ORCID: https://orcid.org/0000-0001-8385-3081)
- Ebenezer Kwesi Ameho
- Edmund Fosu Agyemang (ORCID: https://orcid.org/0000-0001-8124-4493)
- Louis Agyekum
Institutions
- University of Ghana (GH)
- Brock University (CA)
- The University of Texas Rio Grande Valley (US)
- Universidad de Costa Rica (CR)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1371/journal.pntd.0012736
- Citations
- 7
- Primary Topic
- Venomous Animal Envenomation and Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.85
Funders
- Carnegie Corporation of New York
- University of Ghana